2020
DOI: 10.1001/jama.2020.12698
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Estimating Risk Ratios and Risk Differences

Abstract: The goal of many medical research studies is to estimate the direction and magnitude of the effect of an intervention or treatment on a clinical outcome (in clinical trials) or the association between an exposure and an outcome (in observational studies). This effect or association can be presented in various forms, depending on the measured outcome. For example, if the outcome is a continuous measure (eg, blood pressure), the effect or association could be represented as a mean difference between the groups. … Show more

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Cited by 50 publications
(38 citation statements)
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“…Baseline characteristics of patients were described using means with standard deviations or medians with interquartile ranges for continuous data and absolute numbers with percentages for categorical data. A modified Poisson regression model [34][35] was used to estimate risk differences (RDs) and risk ratios (RRs) to evaluate the difference in the clinical outcomes between younger-onset UC and older-onset UC. Multivariable analysis was adjusted for sex, disease duration, disease extent, disease severity, comorbidity, use of the concomitant drugs, and smoking status.…”
Section: Discussionmentioning
confidence: 99%
“…Baseline characteristics of patients were described using means with standard deviations or medians with interquartile ranges for continuous data and absolute numbers with percentages for categorical data. A modified Poisson regression model [34][35] was used to estimate risk differences (RDs) and risk ratios (RRs) to evaluate the difference in the clinical outcomes between younger-onset UC and older-onset UC. Multivariable analysis was adjusted for sex, disease duration, disease extent, disease severity, comorbidity, use of the concomitant drugs, and smoking status.…”
Section: Discussionmentioning
confidence: 99%
“…To assess the bivariate and multivariable associations between menstrual needs and dichotomous work consequences (absenteeism and desire to miss work) we used Poisson regressions with a robust variance estimator to provide prevalence ratios. 40 This method was selected as neither outcome was rare and thus odds ratios would represent a poor approximation of risk ratios. 41 To account for clustering at the level of the workplace we used generalized estimating equations with exchangeable correlation structure (assuming observations within the cluster are equally correlated) to provide a population-averaged effect.…”
Section: Discussionmentioning
confidence: 99%
“…The fixed effects specified for the main outcome model will be included in the analysis of secondary and exploratory outcomes. We will report the between-group mean differences for continuous measures and consider appropriate measures of association [ 33 ] (relative and absolute) for binary outcomes, along with 95% confidence intervals. Secondary outcome analyses will not be adjusted for prognostic variables.…”
Section: Discussionmentioning
confidence: 99%