Estimation of a Relative Risk Effect Size when Using Continuous Outcomes Data: An Application of Methods in the Prevention of Major Depression and Eating Disorders.

INTRODUCTION: The raw mean difference (RMD) and standardized mean difference (SMD) are continuous effect size measures that are not readily usable in decision-analytic models of health care interventions. This study compared the predictive performance of 3 methods by which continuous outcomes data collected using psychiatric rating scales can be used to calculate a relative risk (RR) effect size.

METHODS: Three methods to calculate RR effect sizes from continuous outcomes data are described: the RMD, SMD, and Cochrane conversion methods. Each conversion method was validated using data from randomized controlled trials (RCTs) examining the efficacy of interventions for the prevention of depression in youth (aged ≤17 years) and adults (aged ≥18 years) and the prevention of eating disorders in young women (aged ≤21 years). Validation analyses compared predicted RR effect sizes to actual RR effect sizes using scatterplots, correlation coefficients ( r), and simple linear regression. An applied analysis was also conducted to examine the impact of using each conversion method in a cost-effectiveness model.

RESULTS: The predictive performances of the RMD and Cochrane conversion methods were strong relative to the SMD conversion method when analyzing RCTs involving depression in adults (RMD: r = 0.89-0.90; Cochrane: r = 0.73; SMD: r = 0.41-0.67) and eating disorders in young women (RMD: r = 0.89; Cochrane: r = 0.96). Moderate predictive performances were observed across the 3 methods when analyzing RCTs involving depression in youth (RMD: r = 0.50; Cochrane: r = 0.47; SMD: r = 0.46-0.46). Negligible differences were observed between the 3 methods when applied to a cost-effectiveness model.

CONCLUSION: The RMD and Cochrane conversion methods are both valid methods for predicting RR effect sizes from continuous outcomes data. However, further validation and refinement are required before being applied more broadly.