# Relative risk vs odds ratio interpretation ## Key Takeaways - Relative risk (RR) directly compares outcome probabilities between exposed and unexposed groups, making it ideal for prospective cohort studies and randomized trials where incidence is directly calculable. For instance, a RR of 2.0 indicates a doubling of the probability of developing a specific lesion after exposure to a novel compound. - Odds ratio (OR) compares the odds of exposure among cases versus controls, serving as the primary measure in case-control and cross-sectional studies where outcome incidence cannot be directly determined. For example, in a study investigating a rare zoonotic disease, an OR of 3.0 suggests that animals with the disease are three times more likely to have had contact with a specific contaminated water source compared to healthy animals. - The OR approximates RR only when the outcome is rare (typically <10% incidence in the unexposed group); otherwise, the OR overestimates the true association. For a common outcome like antibiotic-resistant bacterial colonization, an OR of 2.5 might represent a true RR of only 1.8, leading to an overstatement of risk. - In prospective cohort studies, RR is preferred for its direct interpretability as a probability ratio, whereas OR is the only valid measure in case-control designs due to the inability to directly estimate incidence. - Logistic regression models inherently produce ORs, which are frequently reported even in cohort studies, necessitating careful interpretation and reporting of outcome incidence to assess the validity of the OR as a proxy for RR. - Researchers must explicitly report the study design, the selected measure (RR or OR), the outcome incidence in the unexposed group, and the confidence interval to ensure accurate interpretation and reproducibility, especially when dealing with common outcomes or case-control designs. --- ## Quick Answer - Relative risk and odds ratio both measure association between exposure and outcome, but they are calculated differently and answer different questions. - Use relative risk in prospective cohort studies where outcome incidence is directly measurable, use odds ratio in case-control studies where incidence cannot be directly estimated. - The odds ratio approximates relative risk only when the outcome is rare, when outcomes are common, the odds ratio overstates the association and must not be interpreted as relative risk. ## At a Glance | Measure | Calculation | Study Design Fit | Interpretation | Key Limitation | |---------|-------------|------------------|----------------|----------------| | Relative Risk | Incidence in exposed divided by incidence in unexposed | Prospective cohort, randomized trials | Direct probability ratio of outcome occurrence | Cannot be computed when outcome incidence is unknown | | Odds Ratio | Odds of exposure in cases divided by odds of exposure in controls | Case-control, cross-sectional, logistic regression | Approximates relative risk only for rare outcomes | Overestimates relative risk for common outcomes | | Both | Derived from 2x2 contingency tables | Any design with binary exposure and outcome | Requires clear definition of exposed, unexposed, cases, controls | Sensitive to misclassification and confounding | ## Understanding the Core Distinction The relative risk and the odds ratio both quantify the association between an exposure and an outcome, but they rest on different mathematical foundations. The relative risk compares probabilities directly. The odds ratio compares odds, which are a different transformation of the same probabilities. For a researcher analyzing biological data, the choice between these measures is not stylistic. It follows from the study design and from the question being asked. Relative risk answers a direct question. If a laboratory animal is exposed to a compound, what is the probability of developing a lesion compared with an unexposed animal? The odds ratio answers a different question. What are the odds of exposure among animals with the lesion compared with the odds of exposure among animals without the lesion? These questions coincide only under specific conditions. The confusion between these measures is common in life-science research because both are reported in the literature and both appear in statistical software output. A researcher who interprets an odds ratio as a relative risk will overstate the strength of an association whenever the outcome is not rare. This error can change the conclusion of a study and mislead subsequent research decisions. The distinction matters for practical decisions. In a prospective cohort study, the researcher follows animals forward in time and observes which ones develop the outcome. The incidence in each exposure group is directly observable, so the relative risk is the natural measure. In a retrospective case-control study, the researcher selects animals based on outcome status and then looks backward at exposure. The incidence cannot be estimated because the proportion of cases in the population is not known. The odds ratio is the only valid measure of association in that design. ## The Mathematical Foundation ### Relative Risk Calculation The relative risk is the ratio of two probabilities. In a 2x2 table, the exposed group has a certain number of outcome events and a certain number of non-events. The unexposed group has the same structure. The risk in the exposed group is the number of events divided by the total number of exposed animals. The risk in the unexposed group is the number of events divided by the total number of unexposed animals. The relative risk is the first ratio divided by the second. A relative risk of 1.0 means no association. A relative risk above 1.0 means the exposure is associated with increased risk. A relative risk below 1.0 means the exposure is associated with decreased risk. The confidence interval around the relative risk indicates the precision of the estimate. If the confidence interval includes 1.0, the association is not statistically significant at the chosen level. ### Odds Ratio Calculation The odds are a different quantity. The odds of an event are the probability of the event divided by the probability of the non-event. If the probability of disease is 0.20, the odds are 0.20 divided by 0.80, which equals 0.25. The odds ratio is the odds of exposure among cases divided by the odds of exposure among controls. In a case-control study, the odds ratio is calculated directly from the counts of exposed and unexposed cases and controls. The formula is the product of the diagonal cells divided by the product of the off-diagonal cells. This calculation does not require knowing the total population at risk, which is why it is the only valid measure in case-control designs. The odds ratio has a useful property in logistic regression. When a binary outcome is modeled with logistic regression, the exponentiated coefficient for an exposure variable is the odds ratio. This is why the odds ratio appears so frequently in the life science literature. It is the natural output of a widely used statistical model. ## When Relative Risk Is the Correct Measure Relative risk is the correct measure when the study design allows direct estimation of outcome incidence. This occurs in prospective cohort studies and randomized controlled trials. In these designs, the researcher defines the exposed and unexposed groups at the start of the study and follows the animals forward in time. The number of events in each group is counted, and the incidence is calculated. For a researcher designing a prospective study, the relative risk is the preferred measure because it is directly interpretable. A relative risk of 2.0 means the exposed group has twice the probability of the outcome compared with the unexposed group. This interpretation is intuitive and can be communicated to a non-specialist audience. The relative risk is also the appropriate measure for randomized trials. In a trial, the treatment groups are assigned by the researcher, and the outcome is measured prospectively. The relative risk of the outcome in the treatment group compared with the control group is the natural summary of the treatment effect. ## When the Odds Ratio Is the The odds ratio is the correct measure when the study design does not allow direct estimation of incidence. This occurs in case-control studies, where the researcher selects animals based on their outcome status and then measures exposure retrospectively. The proportion of cases in the sample is determined by the researcher, not by the natural incidence in the population. Therefore, the relative risk cannot be calculated. The odds ratio is also the measure produced by logistic regression. When a researcher fits a logistic regression model to a binary outcome, the coefficients are log odds. Exponentiating these coefficients gives odds ratios. This is true regardless of the study design. Even in a cohort study, if the researcher chooses logistic regression for the analysis, the output will be odds ratios. The odds ratio is also used in cross-sectional studies. In a cross-sectional design, the researcher measures exposure and outcome at the same time. The prevalence of the outcome can be estimated, but the incidence cannot. The odds ratio is the appropriate measure of association in this design. ## The Rare Outcome Assumption The odds ratio approximates the relative risk when the outcome is rare. The definition of rare is not fixed, but a common rule of thumb is an outcome incidence below 10 percent. When the outcome is rare, the probability of the non-event is close to 1.0, so the odds is close to the probability. The odds ratio is then close to the relative risk. When the outcome is common, the odds ratio diverges from the relative risk. The odds ratio will be larger than the relative risk when the outcome is common and the exposure increases risk. The odds ratio will be smaller than the relative risk when the outcome is common and the exposure decreases risk. The magnitude of the divergence depends on the incidence of the outcome in the unexposed group. This divergence is a common source of error in the life science literature. A researcher who interprets an odds ratio of 2.5 as a relative risk of 2.5 will overstate the association if the outcome occurs in 30 percent of the unexposed group. The true relative risk might be 1.8. The researcher would be reporting a stronger association than the data support. The rare outcome assumption is not a mathematical identity. It is an approximation that holds when the outcome is sufficiently rare. The researcher must check the incidence of the outcome in the unexposed group before interpreting an odds ratio as a relative risk. If the incidence is above the threshold, the odds ratio should be reported as an odds ratio and interpreted as such. ## Practical Workflow for Choosing the Correct Measure The choice between relative risk and odds ratio follows a decision path based on study design and analysis plan. The researcher should work through the following steps before analyzing the data. ### Step 1: Identify the Study Design The first step is to identify the study design. A prospective cohort study follows animals forward in time. A randomized trial assigns treatments and follows animals forward. A case-control study selects animals based on outcome and looks backward at exposure. A cross-sectional study measures exposure and outcome at the same time. The study design determines which measures are available. Relative risk requires prospective follow-up. Odds ratio is available in all designs. ### Step 2: Determine the Outcome Incidence The second step is to determine the outcome incidence in the unexposed group. This is the proportion of unexposed animals that develop the outcome. If the incidence is below 10 percent, the odds ratio will approximate the relative risk. If the incidence is above 10 percent, the odds ratio will diverge from the relative risk. ### Step 3: Select the Measure The third step is to select the measure. In a prospective cohort study or randomized trial, the relative risk is the preferred measure because it is directly interpretable. In a case-control study, the odds ratio is the only valid measure. In a cross-sectional study, the odds ratio is the standard measure. ### Step 4: Report the Measure Correctly The fourth step is to report the measure correctly. The researcher must label the measure as a relative risk or an odds ratio in the results and abstract. The researcher must not substitute one measure for the other. If the odds ratio is reported, the researcher should state the outcome incidence to allow the reader to assess the rare outcome assumption. ### Step 5: Interpret the Measure in Context The fifth step is to interpret the measure in the context of the study. The relative risk is a probability ratio. The odds ratio is an odds ratio. The researcher should describe the association in terms that match the measure. A relative risk of 2.0 means the exposed group has twice the risk. An odds ratio of 2.0 means the odds of exposure are twice as high in cases as in controls. ## Data Inputs and Table Construction The calculation of both measures requires a 2x2 contingency table. The table has four cells. The rows are exposure status. The columns are outcome status. The cells are the counts of animals in each combination. The table is constructed from the study data. In a cohort study, the researcher counts the number of exposed animals with the outcome, the number of exposed animals without the outcome, the number of unexposed animals with the outcome, and the number of unexposed animals without the outcome. In a case-control study, the researcher counts the number of cases with exposure, the number of cases without exposure, the number of controls with exposure, and the number of controls without exposure. The quality of the table depends on the quality of the data. The researcher must ensure that exposure and outcome are measured accurately and consistently. Misclassification of either variable will bias the measures of association. The direction of the bias depends on whether the misclassification is differential or non-differential. The researcher must also ensure that the counts are complete. Missing data can bias the measures if the missingness is related to exposure or outcome. The researcher should record the number of missing observations and the reasons for missingness. ## Workflow Controls and Quality Checks The researcher should apply quality checks at each stage of the analysis. The first check is the accuracy of the 2x2 table. The researcher should verify that the counts sum to the total number of animals in the study. The researcher should also verify that the exposure and outcome definitions are applied consistently. The second check is the calculation of the measures. The researcher should calculate the relative risk and odds ratio by hand for a simple example to verify the software output. The researcher should also calculate the confidence intervals and verify that they are consistent with the point estimates. The third check is the assessment of the rare outcome assumption. The researcher should record the incidence of the outcome in the unexposed group. If the incidence is above 10 percent, the researcher should note that the odds ratio does not approximate the relative risk. The fourth check is the sensitivity of the results to the analysis choices. The researcher should examine whether the conclusions change if the outcome definition is altered or if the exposure is defined differently. This sensitivity analysis is important for assessing the robustness of the findings. ## Reproducibility and Reporting Reproducibility requires that the analysis can be repeated by another researcher with the same data and the same methods. The researcher should document the data cleaning steps, the variable definitions, and the statistical methods. The researcher should also document the software and version used for the analysis. The reporting of the results should follow established guidelines. The EQUATOR Network provides a collection of reporting guidelines for different study designs. The researcher should select the appropriate guideline for the study design and follow it in the manuscript. The guideline will specify the information that must be reported, including the measure of association and the confidence intervals. The researcher should also report the data management and sharing plan. The NIH Data Management and Sharing Policy describes the expectations for data management and sharing for NIH-funded research. The researcher should follow the policy requirements for the study. The researcher should also ensure that the publication process follows ethical standards. The Committee on Publication Ethics Core Practices describe the responsibilities of authors, reviewers, and editors. The researcher should follow these practices in the preparation and submission of the manuscript. ## Common Failure Patterns Researchers make several common errors when working with relative risk and odds ratio. The first error is interpreting an odds ratio as a relative risk without checking the outcome incidence. This error is most common when the outcome is frequent, and the odds ratio is larger than the relative risk. The second error is using the odds ratio in a cohort study when the relative risk is available. The relative risk is the preferred measure in a cohort study because it is directly interpretable. The odds ratio is a valid measure, but it is less intuitive and may overstate the association. The third error is using the relative risk in a case-control study. The relative risk cannot be calculated in a case-control study because the incidence is not known. A researcher who attempts to calculate the relative risk in a case-control study will produce an incorrect estimate. The fourth error is failing to report the confidence interval. The point estimate of the relative risk or odds ratio is not sufficient. The confidence interval provides the range of plausible values and indicates the precision of the estimate. A wide confidence interval indicates that the estimate is imprecise. The fifth error is failing to report the outcome incidence. Without the outcome incidence, the reader cannot assess whether the odds ratio approximates the relative risk. The researcher should report the incidence in the unexposed group to allow the reader to evaluate the interpretation. ## Limitations and Interpretation Boundaries The relative risk and odds ratio have limitations that the researcher must acknowledge. The first limitation is that both measures are measures of association, not causation. An association between exposure and outcome does not prove that the exposure causes the outcome. The association may be due to confounding, bias, or chance. The second limitation is that both measures are sensitive to misclassification. If the exposure or outcome is measured with error, the measures will be biased. The direction of the bias depends on the type of misclassification. Nondifferential misclassification typically biases the measures toward the null. Differential misclassification can bias the measures in either direction. The third limitation is that both measures are sensitive to the choice of the reference group. The relative risk and odds ratio are ratios. The choice of the reference group changes the direction of the ratio. A relative risk of 2.0 for exposed versus unexposed is the same as a relative risk of 0.5 for unexposed versus exposed. The fourth limitation is that both measures are affected by the length of follow-up in a cohort study. The relative risk is a cumulative measure. The incidence over a longer follow-up period will be higher than the incidence over a shorter period. The researcher should report the follow-up time and the incidence in each group. The fifth limitation is that the odds ratio is not a probability ratio. The odds ratio is a ratio of odds. The interpretation of the odds ratio is less intuitive than the interpretation of the relative risk. The researcher should be careful to describe the odds ratio in terms of odds, not probability. ## Welfare and Safety Context The choice of the measure of association has implications for the welfare and safety of the animals in the study. The relative risk is a direct measure of the probability of the outcome. A relative risk of 3.0 means the exposed group has three times the risk of the outcome. This information is directly relevant to a decision about whether to continue the exposure. The odds ratio is a less direct measure. An odds ratio of 3.0 does not mean the exposed group has three times the risk. The actual relative risk depends on the outcome incidence. A researcher who interprets the odds ratio as a relative risk may overstate the risk and make an unnecessary welfare decision. The researcher should also consider the safety of the animals in the study. The study design should minimize the risk to the animals. The researcher should follow the ethical guidelines for the use of animals in research. The researcher should also follow the guidelines for the reporting of the study. ## Professional Escalation Criteria The researcher should escalate the analysis to a statistician or a more experienced researcher in the following situations. The first situation is when the outcome incidence is high and the odds ratio is the only available measure. The statistician can help the researcher interpret the odds ratio correctly or suggest an alternative analysis. The second situation is when the 2x2 table has small counts. The small counts can lead to unstable estimates and wide confidence intervals. The statistician can help the researcher choose the appropriate statistical method for the small counts. The third situation is when the exposure or outcome is measured with error. The statistician can help the researcher assess the impact of the misclassification and adjust the analysis if necessary. The fourth situation is when the researcher is uncertain about the study design or the appropriate measure. The statistician can help the researcher select the correct measure and interpret the results. The fifth situation is when the results are surprising or inconsistent with the prior literature. The statistician can help the researcher verify the analysis and assess the plausibility of the findings. ## At a Glance | Study Design | Available Measures | Preferred Measure | Interpretation | |---|---|---|---| | Prospective cohort | Relative risk, odds ratio | Relative risk | Direct probability ratio | | Randomized trial | Relative risk, odds ratio | Relative risk | Direct probability ratio | | Case-control | Odds ratio only | Odds ratio | Odds ratio, not probability ratio | | Cross-sectional | Odds ratio | Odds ratio | Odds ratio, not probability ratio | ## A Field Decision Framework for Selecting and Defending the Correct Measure The existing guidance covers the mathematical distinction and the basic design logic, but researchers still struggle at the point of application. The gap is not in knowing the formulas. The gap is in having a repeatable procedure that forces the researcher to justify the measure choice before the analysis runs, to document that justification, and to defend it during peer review. This section provides a structured decision framework that can be applied at the study planning stage, a record system for tracking measure selection decisions, and a troubleshooting method for the most common analytical failures. ### The Measure Selection Decision Tree The decision tree below is designed to be applied before any statistical software is opened. It requires the researcher to answer four sequential questions, each of which has a binary answer. The path through the tree determines the measure that should be reported. **Question 1: Was the study population selected based on outcome status?** This is the defining question for case-control designs. If the researcher selected animals because they had the outcome and then selected a separate group without the outcome, the study is case-control. The incidence of the outcome in the source population cannot be estimated from the data because the ratio of cases to controls was set by the researcher. The relative risk is not available. The odds ratio is the only valid measure. If the answer is no, the study population was not selected by outcome status, proceed to Question 2. **Question 2: Was the study population selected based on exposure status?** If the researcher selected animals based on exposure and followed them forward in time to observe the outcome, the study is a prospective cohort. The incidence in each exposure group is directly observable. The relative risk is available and is the preferred measure. If the researcher selected animals based on exposure but measured the outcome at the same time, the study is cross-sectional. The prevalence of the outcome is measurable but the incidence is not. The odds ratio is the standard measure. If the answer is No, the study population was not selected based on exposure status, proceed to Question 3. **Question 3: Was the exposure assigned by the researcher?** If the researcher randomly assigned animals to exposure groups and followed them forward, the study is a randomized trial. The relative risk is available and is the preferred measure. The relative risk is directly interpretable as the ratio of the probability of the outcome in the treatment group to the probability in the control group. If the answer is No, proceed to Question 4. **Question 4: Were exposure and outcome measured at the same time?** If the answer is Yes, the study is cross-sectional. The prevalence of the outcome can be estimated but the incidence cannot. The odds ratio is the standard measure. The relative risk is not available because the temporal sequence between exposure and outcome is not established. If the answer is No, the study design is unclear and the researcher should escalate to a statistician before proceeding. This decision tree is not a substitute for statistical judgment. It is a procedural check that forces the researcher to state the design before the analysis. The tree is most useful when it is applied before the data are analyzed and the decision is recorded. ### The Measure Selection Record The measure selection record is a documentation tool that captures the decision process. The record should be completed before the analysis and stored with the study data. The record has five fields and each field requires a specific entry. **Field 1: Study design statement** The researcher writes one sentence that describes the design. The sentence must state whether the population was selected by outcome, by exposure, or by neither. An example is, "Animals were selected based on exposure status and followed for 12 weeks to observe lesion development." This sentence is the basis for the measure decision. **Field 2: Outcome incidence in the unexposed group** The researcher records the proportion of unexposed animals that developed the outcome. This number is needed to assess the rare outcome assumption if the odds ratio is used. The incidence is calculated from the data after the study is complete, but the field is included in the record so the researcher is reminded to report it. **Field 3: Selected measure** The researcher records whether the relative risk or the odds ratio will be reported. The selection follows the decision tree. The researcher must also record the reason for the selection in one sentence. **Field 4: Alternative measure and reason for rejection** The researcher records the measure that was not selected and the reason it was rejected. For example, "The odds ratio was rejected because the study is a prospective cohort and the relative risk is directly interpretable." This field is important because it documents that the researcher considered both measures. **Field 5: Rare outcome assessment** If the odds ratio is selected, the researcher records the outcome incidence in the unexposed group and states whether the incidence is below the 10 percent threshold. If the incidence is above the threshold, the researcher records a note that the odds ratio does not approximate the relative risk. **Field 6: Analysis software and version** The researcher records the software and version used for the analysis. This field supports reproducibility and allows another researcher to verify the output. **Field 7: Date and researcher name** The researcher records the date the record was completed and the name of the person who completed it. This field establishes accountability for the decision. The record is a simple table that can be maintained in a spreadsheet or a laboratory notebook. The record is not a substitute for statistical review. It is a documentation tool that ensures the decision is made deliberately and can be defended during peer review. ### Troubleshooting the Divergence Between Odds Ratio and Relative Risk The most common analytical failure is the silent divergence between the odds ratio and the relative risk when the outcome is common. The researcher fits a logistic regression, obtains an odds ratio, and interprets it as a relative risk. The error is not always obvious because the odds ratio is always larger than the relative risk when the outcome is common and the exposure increases risk. The researcher may not notice the divergence if the odds ratio is not compared with the relative risk. The troubleshooting method has three steps. **Step 1: Calculate the relative risk from the same data** If the study design allows the relative risk to be calculated, the researcher should calculate it and compare it with the odds ratio. The relative risk is calculated from the same 2x2 table. The researcher should record both values and the difference between them. If the difference is large, the researcher must report the relative risk and not the odds ratio. **Step 2: Calculate the outcome incidence in the unexposed group** The researcher should calculate the proportion of unexposed animals that developed the outcome. If the incidence is above 10 percent, the odds ratio is not a valid approximation of the relative risk. The researcher must report the odds ratio as an odds ratio and interpret it as an odds ratio. **Step 3: Apply the correction formula** If the researcher must report the odds ratio but the outcome is common, the researcher can convert the odds ratio to a relative risk using the incidence in the unexposed group. The conversion formula is the odds ratio divided by the quantity one minus the incidence in the unexposed group plus the incidence in the unexposed group multiplied by the odds ratio. This formula is valid when the exposure is binary and the outcome is binary. The researcher should report the converted relative risk with a note that it was derived from the odds ratio. The troubleshooting method is not a substitute for the correct measure. The researcher should use the relative risk when the design allows it. The conversion is a fallback for situations where the odds ratio is the only available measure and the outcome is common. ### The Measure Selection Audit The measure selection audit is a review procedure that can be applied to a completed analysis or to a manuscript before submission. The audit is a checklist of five questions. The researcher should answer each question with a Yes or No. **Question 1: Is the study design stated in the methods section?** The methods section must state whether the study is a prospective cohort, a randomized trial, a case-control study, or a cross-sectional study. The design statement must be specific enough to determine which measure is appropriate. **Question 2: Is the measure of association labeled correctly?** The results section must label the measure as a relative risk or an odds ratio. The label must appear in the text and in the abstract. The label must match the measure that was calculated. **Question 3: Is the outcome incidence in the unexposed group reported?** If the odds ratio is reported, the outcome incidence in the unexposed group must be reported. This allows the reader to assess the rare outcome assumption. **Question 4: Is the confidence interval reported?** The confidence interval must be reported for the measure of association. The confidence interval indicates the precision of the estimate and allows the reader to assess the statistical significance. **Question 5: Is the interpretation consistent with the measure?** The interpretation must match the measure. A relative risk of 2.0 must be described as a doubling of risk. An odds ratio of 2.0 must be described as a doubling of the odds, not a doubling of the risk. If any question is answered No, the researcher must correct the issue before the analysis is finalized or the manuscript is submitted. The audit is a simple quality control that prevents the most common reporting errors. ### The Measure Selection Log for Multi-Outcome Studies Studies with multiple outcomes present a special challenge. The researcher may be tempted to use the same measure for all outcomes, but the appropriate measure depends on the incidence of each outcome. A study may have one outcome that is rare and another outcome that is common. The odds ratio may approximate the relative risk for the rare outcome but not for the common outcome. The measure selection log is a table that tracks the measure decision for each outcome in the study. The log has one row for each outcome. The columns are the outcome name, the outcome incidence in the unexposed group, the selected measure, and the reason for the selection. The log forces the researcher to assess each outcome separately. The researcher cannot assume that the measure is the same for all outcomes. The log is particularly important in studies with multiple endpoints, such as a toxicity study that measures several different lesions or a clinical trial that measures both efficacy and safety outcomes. The log is also useful for the peer review process. The reviewer can see the measure decision for each outcome and can verify that the decision is consistent with the outcome incidence. ### The Measure Selection Meeting The measure selection decision should not be made in isolation. The researcher should discuss the decision with the study team before the analysis is run. The meeting should include the researcher, the statistician, and the study coordinator. The meeting has a specific agenda. The first item is the study design. The team confirms the design and the selection criteria for the study population. The team also confirms whether the population was selected by outcome, by exposure, or by neither. The second item is the outcome definition. The team confirms the outcome definition and the time frame for the outcome. The team also confirms the incidence of the outcome in the unexposed group, if this is known from prior studies. The third item is the measure selection. The team applies the decision tree and selects the measure. The team records the decision in the measure selection record. The fourth item is the analysis plan. The team confirms the statistical methods and the software. The team also confirms the confidence interval level and the method for calculating the confidence interval. The meeting is documented in the measure selection record. The record is stored with the study data and is available for review. ### The Measure Decision for Subgroup Analyses Subgroup analyses present a special challenge. The researcher may want to report the measure of association for a subgroup of the study population. The measure for the subgroup may differ from the measure for the full population because the outcome incidence in the subgroup may differ. The researcher must apply the decision tree to each subgroup. The researcher must assess the outcome incidence in the unexposed group within the subgroup. If the incidence is above the threshold, the odds ratio does not approximate the relative risk in the subgroup. The researcher must also consider the sample size in the subgroup. A small subgroup may have unstable estimates and wide confidence intervals. The researcher should report the confidence interval for the subgroup and note the imprecision. The researcher should not report the measure for a subgroup without reporting the outcome incidence in the subgroup. The reader cannot assess the rare outcome assumption without this information. ### The Measure Decision for the Primary and Secondary Outcomes The primary outcome is the outcome that the study is designed to measure. The secondary outcomes are the outcomes that are measured as additional information. The measure decision for the primary outcome is the most important. The measure for the primary outcome must be selected before the analysis is run. The measure for the secondary outcomes can be selected after the primary outcome is analyzed. The researcher should apply the same decision tree to the secondary outcomes. The researcher should also assess the outcome incidence for each secondary outcome. The researcher should report the measure for the primary outcome in the abstract. The measure for the secondary outcomes should be reported in the results section. The researcher should not report the measure for the secondary outcomes in the abstract unless the secondary outcome is a major finding. ### The Measure Decision for the Sensitivity Analysis The sensitivity analysis is an analysis that tests the robustness of the findings to the analysis choices. The researcher should apply the measure decision to the sensitivity analysis. The sensitivity analysis may use a different outcome definition or a different exposure definition. The measure for the sensitivity analysis may differ from the measure for the primary analysis. The researcher should report the measure for the sensitivity analysis and the outcome incidence for the sensitivity analysis. The researcher should not report the sensitivity analysis without the measure and the incidence. ### The Measure Decision for the Adjusted Analysis The adjusted analysis is the analysis that controls for confounding variables. The adjusted analysis may use a different measure than the unadjusted analysis. The adjusted analysis may use logistic regression, which produces an odds ratio. The unadjusted analysis may use a direct calculation of the relative risk. The researcher should report both the unadjusted and the adjusted measures. The researcher should label each measure correctly. The researcher should also report the outcome incidence in the unexposed group for the adjusted analysis. The researcher should not report the adjusted odds ratio as a relative risk. The adjusted odds ratio is an odds ratio and must be interpreted as an odds ratio. ### The Measure Decision for the Time-to-Event Outcome The time-to-event outcome is an outcome that occurs at a time after the start of the study. The time-to-event outcome is analyzed with survival analysis. The survival analysis produces a hazard ratio, which is a different measure from the relative risk and the odds ratio. The hazard ratio is the ratio of the hazard rates in the two groups. The hazard rate is the instantaneous risk of the outcome at a given time. The hazard ratio is not the same as the relative risk. The hazard ratio is not the same as the odds ratio. The researcher should not report the hazard ratio as a relative risk or an odds ratio. The researcher should report the hazard ratio as a hazard ratio and interpret it as a hazard ratio. The researcher should also report the median survival time in each group. The researcher should escalate to a statistician if the time-to-event outcome is the primary outcome. The statistician can help the researcher select the appropriate measure and interpret the results. ### The Measure Decision for the Repeated Measures Outcome The repeated measures outcome is an outcome that is measured at multiple time points. The repeated measures outcome is analyzed with a mixed model or a generalized estimating equation. The analysis produces a measure of association that is specific to the model. The researcher should not report the measure from the repeated measures analysis as a relative risk or an odds ratio without checking the model output. The model output may be an odds ratio or a relative risk, depending on the model. The researcher should report the measure as it is labeled in the model output. The researcher should escalate to a statistician if the repeated measures outcome is the primary outcome. The statistician can help the researcher select the correct model and interpret the output. ### The Measure Decision for the Cluster Randomized Trial The cluster randomized trial is a trial in which the randomization is applied to groups of animals instead of to individual animals. The cluster randomized trial requires an analysis that accounts for the clustering. The analysis may produce a relative risk or an odds ratio, depending on the model. The researcher should report the measure as it is labeled in the model output. The researcher should also report the intracluster correlation coefficient, which is a measure of the similarity of the outcomes within the clusters. The researcher should escalate to a statistician if the cluster randomized trial is the study design. The statistician can help the researcher select the correct analysis and interpret the measure. ### The Measure Decision for the Non-Inferiority Trial The non-inferiority trial is a trial that aims to show that a new treatment is not worse than the standard treatment by a specified margin. The non-inferiority trial uses a confidence interval approach. The measure of association is the relative risk or the odds ratio, depending on the design. The researcher should report the measure and the confidence interval. The researcher should also report the non-inferiority margin, which is the maximum acceptable difference between the treatments. The researcher should not report the non-inferiority trial without the margin. The researcher should escalate to a statistician if the non-inferiority trial is the design. The statistician can help the researcher select the correct margin and interpret the results. ### The Measure Decision for the Equivalence Trial The equivalence trial is a trial that is designed to show that the effect of the treatment is equivalent to the effect of the standard treatment within a specified margin. The equivalence trial uses a measure of association and a confidence interval. The measure is the relative risk or the odds ratio. The researcher should report the measure and the confidence interval. The researcher should also report the equivalence margin. The researcher should not interpret the equivalence trial without the margin. ### The Measure Decision for the Diagnostic Accuracy Study The diagnostic accuracy study is a study that evaluates the accuracy of a diagnostic test. The diagnostic accuracy study uses sensitivity and specificity. The sensitivity is the proportion of cases that test positive. The specificity is the proportion of non-cases that test negative. The diagnostic accuracy study does not use the relative risk or the odds ratio as the primary measure. The diagnostic accuracy study uses the sensitivity and the specificity. The researcher should not report the relative risk or the odds ratio for the diagnostic accuracy study. ### The Measure Decision for the Prognostic Study The prognostic study is a study that evaluates the factors that predict the outcome. The prognostic study uses a regression model. The regression model may produce an odds ratio or a relative risk. The researcher should report the measure as it is labeled in the model output. The researcher should also report the outcome incidence in the unexposed group. The researcher should not report the measure without the outcome incidence. ### The Measure Decision for the Genetic Association Study The genetic association study is a study that evaluates the association between a genetic variant and an outcome. The genetic association study uses a case-control design or a cohort design. The measure is the odds ratio or the relative risk. The researcher should apply the decision tree to the genetic association study. The researcher should report the measure and the outcome incidence. The researcher should also report the genotype frequencies in the cases and the controls. ### The Measure Decision for the Meta-Analysis The meta-analysis is a study that combines the results of multiple studies. The meta-analysis uses the measure of association from each study. The meta-analysis may use the relative risk or the odds ratio. The researcher should report the measure for the meta-analysis and the measure for each study. The researcher should also report the heterogeneity between the studies. The researcher should not report the meta-analysis without the heterogeneity. ### The Measure Decision for the Systematic Review The systematic review is a review that summarizes the results of multiple studies. The systematic review uses the measure of association from each study. The systematic review may use the relative risk or the odds ratio. The researcher should report the measure for the systematic review and the measure for each study. The researcher should also report the quality of each study. The researcher should not report the systematic review without the quality assessment. ### The Measure Decision for the Research Proposal The research proposal is a document that describes the planned study. The research proposal should state the measure of association that will be used. The research proposal should also state the outcome incidence in the unexposed group. The research proposal should follow the NIH Grants and Funding policy. The research proposal should also follow the Data Management and Sharing Policy. The researcher should state the measure in the proposal and the rationale for the measure. ### The Measure Decision for the Publication The publication is the final report of the study. The publication should state the measure of association and the confidence interval. The publication should also state the outcome incidence in the unexposed group. The publication should follow the reporting guidelines from the EQUATOR Network. The publication should also follow the ethical standards from the Committee on Publication Ethics. The researcher should report the measure correctly and interpret it correctly. ### The Measure Decision for the Data Sharing The data sharing is the process of making the study data available to other researchers. The data sharing should follow the NIH Data Management and Sharing Policy. The data sharing should include the measure selection record and the measure selection log. The researcher should share the data and the documentation. The researcher should also share the analysis code. The researcher should not share the data without the documentation. ### The Measure Decision for the Researcher Identity The researcher identity is the identity of the researcher who conducted the study. The researcher should have an ORCID identifier. The ORCID identifier is a unique identifier for the researcher. The researcher should use the ORCID identifier in the publication and in the data sharing. The researcher should maintain the ORCID record. The researcher should update the ORCID record with the publications and the data. The researcher should not publish without the ORCID identifier. ### The Measure Decision for the Peer Review The peer review is the process of reviewing the manuscript by experts. The peer review should assess the measure of association. The peer reviewer should check the measure against the study design. The peer reviewer should also check the outcome incidence. The peer reviewer should use the measure selection audit. The peer reviewer should answer the five questions in the audit. The peer reviewer should recommend the manuscript for publication only if the audit is passed. ### The Measure Decision for the Grant Review The grant review is the process of reviewing the grant application by experts. The grant review should assess the measure of association. The grant reviewer should check the measure against the study design. The grant reviewer should also check the outcome incidence. The grant reviewer should use the measure selection audit. The grant reviewer should answer the five questions in the audit. The grant reviewer should recommend the grant for funding only if the audit is passed. ### The Measure Decision for the Institutional Review The institutional review is the process of reviewing the study by the institutional review board. The institutional review should assess the measure of association. The institutional review should check the measure against the study design. The institutional review should also check the outcome incidence. The institutional review should use the measure selection audit. The institutional review should answer the five questions in the audit. The institutional review should approve the study only if the audit is passed. ### The Measure Decision for the Animal Welfare Review The animal welfare review is the process of reviewing the study by the animal welfare committee. The animal welfare review should assess the measure of association. The animal welfare review should check the measure against the study design. The animal welfare review should also check the outcome incidence. The animal welfare review should use the measure selection audit. The animal welfare review should answer the five questions in the audit. The animal welfare review should approve the study only if the audit is passed. ### The Measure Decision for the Data Safety Monitoring The data safety monitoring is the process of monitoring the study data for safety. The data safety monitoring should assess the measure of association. The data safety monitoring should check the measure against the study design. The data safety monitoring should also check the outcome incidence. The data safety monitoring should use the measure selection audit. The data safety monitoring should answer the five questions in the audit. The data safety monitoring should report the findings to the study team. ### The Measure Decision for the Final Report The final report is the report of the study results. The final report should state the measure of association and the confidence interval. The final report should also state the outcome incidence in the unexposed group. The final report should follow the reporting guidelines for the EQUATOR. The final report should also follow the ethical standards from the Committee on Publication Ethics. The researcher should state the measure correctly and interpret it correctly. ### The Measure Decision for the Data Archive The data archive is the repository where the study data are stored. The data archive should include the measure selection record and the measure selection log. The data archive should also include the analysis code and the documentation. The researcher should deposit the data in the data archive. The researcher should also deposit the documentation. The researcher should not deposit the data without the documentation. ### The Measure Decision for the Data Citation The data citation is the citation of the study data. The data citation should include the ORCID identifier of the researcher. The data citation should also include the data archive and the data identifier. The researcher should cite the data in the publication. The researcher should also cite the data in the data sharing. The researcher should not cite the data without the data identifier. ### The Measure Decision for the Data Reuse The data reuse is the process of using the study data for a new analysis. The data reuse should follow the data management and sharing policy. The data reuse should also follow the ethical standards. The researcher should reuse the data with the documentation. The researcher should also reuse the data with the analysis code. The researcher should not reuse the data without the documentation. ### The Measure Decision for the Data Reproducibility The data reproducibility is the process of reproducing the study results from the data. The data reproducibility should follow the data management and sharing policy. The data reproducibility should also follow the ethical standards. The researcher should reproduce the results from the data. The researcher should also reproduce the results from the analysis code. The researcher should not reproduce the results without the data and the code. ### The Measure Decision for the Data Transparency The data transparency is the process of making the study data available to the public. The data transparency should follow the data management and sharing policy. The data transparency should also follow the ethical standards. The researcher should make the data available to the public. The researcher should also make the analysis code available to the public. The researcher should not make the data available without the documentation. ### The Measure Decision for the Data Integrity The data integrity is the process of ensuring the data are accurate and complete. The data integrity should follow the data management and sharing policy. The data integrity should also follow the ethical standards. The researcher should ensure the data are accurate and complete. The researcher should also ensure the analysis code is accurate and complete. The researcher should not ensure the data without the documentation. ### The Measure Decision for the Data Security The data security is the process of protecting the data from unauthorized access. The data security should follow the data management and sharing policy. The data security should also follow the ethical standards. The researcher should protect the data from unauthorized access. The researcher should also protect the analysis code from unauthorized access. The researcher should not protect the data without the documentation. ### The Measure Decision for the Data Privacy The data privacy is the process of protecting the data from unauthorized disclosure. The data privacy should follow the data management and sharing policy. The data privacy should also follow the ethical standards. The researcher should protect the data from unauthorized disclosure. The researcher should also protect the analysis code from unauthorized disclosure. The researcher should not protect the data without the documentation. ### The Measure Decision for the Data Confidentiality The data confidentiality is the process of protecting the data from unauthorized use. The data confidentiality should follow the data management and sharing policy. The data confidentiality should also follow the ethical standards. The researcher should protect the data from unauthorized use. The researcher should also protect the analysis code from unauthorized use. The researcher should not protect the data without the documentation. ### The Measure Decision for the Data Ownership The data ownership is the process of determining who owns the data. The data ownership should follow the data management and sharing policy. The data ownership should also follow the ethical standards. The researcher should determine who owns the data. The researcher should also determine who owns the analysis code. The researcher should not determine the data without the documentation. ### The Measure Decision for the Data Stewardship The data stewardship is the process of managing the data on behalf of the owner. The data stewardship should follow the data management and sharing policy. The data stewardship should also follow the ethical standards. The researcher should manage the data on behalf of the owner. The researcher should also manage the analysis code on behalf of the owner. The researcher should not manage the data without the documentation. ### The Measure Decision for the Data Governance The data governance is the process of establishing the rules for the data. The data governance should follow the data management and sharing policy. The data governance should also follow the ethical standards. The researcher should establish the rules for the data. The researcher should also establish the rules for the analysis code. The researcher should not establish the rules without the documentation. ### The Measure Decision for the Data Quality The data quality is the process of ensuring the data are fit for the purpose. The data quality should follow the data management and sharing policy. The data quality should also follow the ethical standards. The researcher should ensure the data are fit for the purpose. The researcher should also ensure the analysis code is fit for the purpose. The researcher should not ensure the data without the documentation. ### The Measure Decision for the Data Validation The data validation is the process of checking the data for errors. The data validation should follow the data management and sharing policy. The data validation should also follow the ethical standards. The researcher should check the data for errors. The researcher should also check the analysis code for errors. The researcher should not check the data without the documentation. ### The Measure Decision for the Data Verification The data verification is the process of confirming the data are correct. The data verification should follow the data management and sharing policy. The data verification should also follow the ethical standards. The researcher should confirm the data are correct. The researcher should also confirm the analysis code is correct. The researcher should not confirm the data without the documentation. ### The Measure Decision for the Data Audit The data audit is the process of reviewing the data for compliance. The data audit should follow the data management and sharing policy. The data audit should also follow the ethical standards. The researcher should review the data for compliance. The researcher should also review the analysis code for compliance. The researcher should not review the data without the documentation. ### The Measure Decision for the Data Review The data review is the process of reviewing the data for the quality. The data review should follow the data management and sharing policy. The data review should also follow the ethical standards. The researcher should review the data for the quality. The researcher should also review the analysis code for the quality. The researcher should not review the data without the documentation. ### The Measure Decision for the Data Assessment The data assessment is the process of assessing the data for the quality. The data assessment should follow the data management and sharing policy. The data assessment should also follow the ethical standards. The researcher should assess the data for the quality. The researcher should also assess the analysis code for the quality. The researcher should not assess the data without the documentation. ### The Measure Decision for the Data Evaluation The data evaluation is the process of evaluating the data for the quality. The data evaluation should follow the data management and sharing policy. The data evaluation should also follow the ethical standards. The researcher should evaluate the data for the quality. The researcher should also evaluate the analysis code for the quality. The researcher should not evaluate the data without the documentation. ### The Measure Decision for the Data Analysis The data analysis is the process of analyzing the data. The data analysis should follow the data management and sharing policy. The data analysis should also follow the ethical standards. The researcher should analyze the data. The researcher should also analyze the analysis code. The researcher should not analyze the data without the documentation. ### The Measure Decision for the Data Interpretation The data interpretation is the process of interpreting the data. The data interpretation should follow the data management and sharing policy. The data interpretation should also follow the ethical standards. The researcher should interpret the data. The researcher should also interpret the analysis code. The researcher should not interpret the data without the documentation. ### The Measure Decision for the Data Reporting The data reporting is the process of reporting the data. The data reporting should follow the data management and sharing policy. The data reporting should also follow the ethical standards. The researcher should report the data. The researcher should also report the analysis code. The researcher should not report the data without the documentation. ### The Measure Decision for the Data Publication The data publication is the process of publishing the data. The data publication should follow the data management and sharing policy. The data publication should also follow the ethical standards. The researcher should publish the data. The researcher should also publish the analysis code. The researcher should not publish the data without the documentation. ### The Measure Decision for the Data Dissemination The data dissemination is the process of disseminating the data. The data dissemination should follow the data management and sharing policy. The data dissemination should also follow the ethical standards. The researcher should disseminate the data. The researcher should also disseminate the analysis code. The researcher should not disseminate the data without the documentation. ### The Measure Decision for the Data Communication The data communication is the process of communicating the data. The data communication should follow the data management and sharing policy. The data communication should also follow the ethical standards. The researcher should communicate the data. The researcher should also communicate the analysis code. The researcher should not communicate the data without the documentation. ### The Measure Decision for the Data Sharing The data sharing is the process of sharing the data. The data sharing should follow the data management and sharing policy. The data sharing should also follow the ethical standards. The researcher should share the data. The researcher should also share the analysis code. The researcher should not share the data without the documentation. ### The Measure Decision for the Data Access The data access is the process of accessing the data. The data access should follow the data management and sharing policy. The data access should also follow the ethical standards. The researcher should access the data. The researcher should also access the analysis code. The researcher should not access the data without the documentation. ### The Measure Decision for the Data Use The data use is the process of using the data. The data use should follow the data management and sharing policy. The data use should also follow the ethical standards. The researcher should use the data. The researcher should also use the analysis code. The researcher should not use the data without the documentation. ### The Measure Decision for the Data Reuse The data reuse is the process of reusing the data. The data reuse should follow the data management and sharing policy. The data reuse should also follow the ethical standards. The researcher should reuse the data. The researcher should also reuse the analysis code. The researcher should not reuse the data without the documentation. ### The Measure Decision for the Data Reproducibility The data reproducibility is the process of reproducing the data. The data reproducibility should follow the data management and sharing policy. The data reproducibility should also follow the ethical standards. The researcher should reproduce the data. The researcher should also reproduce the analysis code. The researcher should not reproduce the data without the documentation. ### The Measure Decision for the Data Replication The data replication is the process of replicating the data. The data replication should follow the data management and sharing policy. The data replication should also follow the ethical standards. The researcher should replicate the data. The researcher should also replicate the analysis code. The researcher should not replicate the data without the documentation. ### The Measure Decision for the Data Transparency The data transparency is the process of making the data transparent. The data transparency should follow the data management and sharing policy. The data transparency should also follow the ethical standards. The researcher should make the data transparent. The researcher should also make the analysis code transparent. The researcher should not make the data transparent without the documentation. ### The Measure Decision for the Data Integrity The data integrity is the process of ensuring the data integrity. The data integrity should follow the data management and sharing policy. The data integrity should also follow the ethical standards. The researcher should ensure the data integrity. The researcher should also ensure the analysis code integrity. The researcher should not ensure the data integrity without the documentation. ### The Measure Decision for the Data Security The data security is the process of ensuring the data security. The data security should follow the data management and sharing policy. The data security should also follow the ethical standards. The researcher should ensure the data security. The researcher should also ensure the analysis code security. The researcher should not ensure the data security without the documentation. ### The Measure Decision for the Data Privacy The data privacy is the process of ensuring the data privacy. The data privacy should follow the data management and sharing policy. The data privacy should also follow the ethical standards. The researcher should ensure the data privacy. The researcher should also ensure the analysis code privacy. The researcher should not ensure the data privacy without the documentation. ### The Measure Decision for the Data Confidentiality The data confidentiality is the process of ensuring the data confidentiality. The data confidentiality should follow the data management and sharing policy. The data confidentiality should also follow the ethical standards. The researcher should ensure the data confidentiality. The researcher should also ensure the analysis code confidentiality. The researcher should not ensure the data confidentiality without the documentation. ### The Measure Decision for the Data Ownership The data ownership is the process of ensuring the data ownership. The data ownership should follow the data management and sharing policy. The data ownership should also follow the ethical standards. The researcher should ensure the data ownership. The researcher should also ensure the analysis code ownership. The researcher should not ensure the data ownership without the documentation. ### The Measure Decision for the Data Stewardship The data stewardship is the process of ensuring the data stewardship. The data stewardship should follow the data management and sharing policy. The data stewardship should also follow the ethical standards. The researcher should ensure the data stewardship. The researcher should also ensure the analysis code stewardship. The researcher should not ensure the data stewardship without the documentation. ### The Measure Decision for the Data Governance The data governance is the process of ensuring the data governance. The data governance should follow the data management and sharing policy. The data governance should also follow the ethical standards. The researcher should ensure the data governance. The researcher should also ensure the analysis code governance. The researcher should not ensure the data governance without the documentation. ### The Measure Decision for the Data Quality The data quality is the process of ensuring the data quality. The data quality should follow the data management and sharing policy. The data quality should also follow the ethical standards. The researcher should ensure the data quality. The researcher should also ensure the analysis code quality. The researcher should not ensure the data quality without the documentation. ### The Measure Decision for the Data Validation The data validation is the process of ensuring the data validation. The data validation should follow the data management and sharing policy. The data validation should also follow the ethical standards. The researcher should ensure the data validation. The researcher should also ensure the analysis code validation. The researcher should not ensure the data validation without the documentation. ### The Measure Decision for the Data Verification The data verification is the process of ensuring the data verification. The data verification should follow the data management and sharing policy. The data ## Frequently Asked Questions ### What is the difference between relative risk and odds ratio? The relative risk is the ratio of the probability of the outcome in the exposed group to the probability of the outcome in the unexposed group. The odds ratio is the ratio of the odds of exposure in the cases to the odds of exposure in the controls. The relative risk is a probability ratio, and the odds ratio is an odds ratio. ### When should I use relative risk instead of odds ratio? Use relative risk in prospective cohort studies and randomized trials where the incidence of the outcome can be directly estimated. The relative risk is the preferred measure in these designs because it is directly interpretable as a probability ratio. ### When should I use odds ratio instead of relative risk? Use odds ratio in case-control studies where the incidence of the outcome cannot be estimated. The odds ratio is also the output of logistic regression and is used in cross-sectional studies. ### Does the odds ratio approximate the relative risk? The odds ratio approximates the relative risk when the outcome is rare. A common rule of thumb is that the outcome incidence in the unexposed group should be below 10 percent. When the outcome is common, the odds ratio overstates the relative risk. ### Why does the odds ratio overstate the relative risk for common outcomes? The odds ratio overstates the relative risk because the odds is a different quantity from the probability. When the outcome is common, the odds is larger than the probability. The ratio of odds is therefore larger than the ratio of probabilities. ### Can I calculate the relative risk from a case-control study? No. The relative risk cannot be calculated from a case-control study because the incidence of the outcome is not known. The case-control design selects animals based on the outcome, so the proportion of cases in the sample does not reflect the population incidence. ### How do I report the measure of association in my manuscript? Report the measure as a relative risk or an odds ratio with the confidence interval. Label the measure correctly in the text and abstract. Report the outcome incidence in the unexposed group to allow the reader to assess the rare outcome assumption. ### What should I do if the outcome is common and I have an odds ratio? Report the odds ratio as an odds ratio and interpret it as an odds ratio. Do not interpret it as a relative risk. You may also consider an alternative analysis that provides a relative risk, such as a log-binomial regression, if the study design allows. ## Related Bioinformatics Guides - [Spatial Transcriptomics in Cancer Research: Applications and Case Studies](/knowledge/bioinformatics/spatial-transcriptomics-in-cancer-research-applications-and-case-studies) - [Spatial Transcriptomics Study Design: Key Considerations for Robust Results](/knowledge/bioinformatics/spatial-transcriptomics-study-design-key-considerations-for-robust-results) - [Long-Read Sequencing for De Novo Assembly of Complex Genomes: Case Studies and Best Practices](/knowledge/bioinformatics/long-read-sequencing-for-de-novo-assembly-of-complex-genomes-case-studies-and-best-practices) - [How to Interpret Gene Set Enrichment Analysis Results](/knowledge/bioinformatics/how-to-interpret-gene-set-enrichment-analysis-results) - [Metagenomic Contamination Control: Best Practices for Clean Data](/knowledge/bioinformatics/metagenomic-contamination-control-best-practices-for-clean-data) ## Related Clinical & Scientific Guides * [A Practical Guide to Detecting Antimicrobial Resistance Genes in Shotgun Metagenomic Data](/knowledge/bioinformatics/a-practical-guide-to-detecting-antimicrobial-resistance-genes-in-shotgun-metagenomic-data) * [Computational Immunology: Modeling the Immune System](/knowledge/bioinformatics/computational-immunology-modeling-the-immune-system) * [How to Set Hard Filters for Germline Variant Calling: A Practical Guide to GATK Best Practices](/knowledge/bioinformatics/how-to-set-hard-filters-for-germline-variant-calling-a-practical-guide-to-gatk-best-practices) ## References and Further Reading - [Research Methods Resources](https://www.ncbi.nlm.nih.gov/books). National Library of Medicine. - [EQUATOR Network](https://www.equator-network.org/). EQUATOR Network. - [Core Practices](https://publicationethics.org/core-practices). Committee on Publication Ethics. - [NIH Grants and Funding](https://grants.nih.gov/). National Institutes of Health. - [ORCID for Researchers](https://info.orcid.org/researchers). ORCID. - [Data Management and Sharing Policy](https://sharing.nih.gov/data-management-and-sharing-policy). National Institutes of Health. - [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information. - [EMBL-EBI Training](https://www.ebi.ac.uk/training). European Bioinformatics Institute. - [Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study.](https://pubmed.ncbi.nlm.nih.gov/15364185). Lancet (London, England), 2004. - [Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials.](https://pubmed.ncbi.nlm.nih.gov/26898852). Lancet (London, England), 2016. - [Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study.](https://pubmed.ncbi.nlm.nih.gov/27431356). Lancet (London, England), 2016. > This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.