John W. Robinson, M.D., Ph.D., LLC
Statistics ▪ Analytics ▪ Data Science
Design Suggestions for Comparative Effectiveness Studies
Consider Competing Risks in Studies with Time-to-Event Outcomes
A competing risk, or competing event, is an event that alters the probability of the event of interest, in the context of a study in which the primary outcome is the time to the event of interest (survival time)… Investigators frequently ignore the effects of competing risks when comparing time-to-event outcomes between treatment groups and use methods that assume independent censoring, such as the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards regression… A better approach is to acknowledge the potential for competing risks when it exists and to plan to use methods that properly account for them... Read entire suggestion.
Take Advantage of Within-Subject Correlation in Longitudinal Studies
In longitudinal studies, the outcome variable is measured repeatedly, at intervals, after a baseline measurement… For comparing longitudinal outcomes between treatment groups, investigators often use methods such as a t-test, chi-square test, or simple analysis of variance (ANOVA) that disregard within-subject correlation and make a distinct, cross-sectional comparison at each measurement time… A better approach is to use a method that fully accounts for within-subject correlation, such as a linear mixed model or generalized linear mixed model fitted by maximum likelihood or a Bayesian strategy... Read entire suggestion.
Match on Estimated Propensity Score in Observational Studies
In an observational comparative effectiveness study, assignment to treatment group is determined by the choices of patients and providers; choices generally affected by numerous covariates… The approach most often used in an attempt to disentangle the effects of covariates from the effect of treatment is regression adjustment… Constructing treatment and control groups by matching subjects on estimated propensity score is a very effective means of balancing the distributions of observed covariates between the two groups. Furthermore, if all observed, potentially confounding covariates are well-balanced between the groups, regression adjustment is not only more reliable, but usually no longer necessary to reduce confounding… Read entire suggestion.