On reporting results from randomized controlled trials with recurrent events.
BACKGROUND
Evidence-based medicine has been advanced by the use of standards for reporting the design and methodology of randomized controlled trials (RCT). Indeed, without this information it is difficult to assess the quality of evidence from an RCT. Although a variety of statistical methods are available for the analysis of recurrent events, reporting the effect of an intervention on outcomes that recur is an area that remains poorly understood in clinical research. The purpose of this paper is to outline guidelines for reporting results from RCTs where the outcome of interest is a recurrent event.


METHODS
We used a simulation study to relate an event process and results from analyses of the gamma-Poisson, independent-increment, conditional, and marginal Cox models. We reviewed the utility of regression models for the rate of a recurrent event by articulating the associated study questions, preenting the risk sets, and interpreting the regression coefficients.


RESULTS
Based on a single data set produced by simulation, we reported and contrasted results from statistical methods for evaluating treatment effect from an RCT with a recurrent outcome. We showed that each model has different study questions, assumptions, risk sets, and rate ratio interpretation, and so inferences should consider the appropriateness of the model for the RCT.


CONCLUSIONS
Our guidelines for reporting results from an RCT involving a recurrent event suggest that the study question and the objectives of the trial, such as assessing comparable groups and estimating effect size, should determine the statistical methods. The guidelines should allow clinical researchers to report appropriate measures from an RCT for understanding the effect of intervention on the occurrence of a recurrent event.
Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, Canada. lisa.kuramoto@vch.ca
Lisa
L
Kuramoto
Boris G
BG
Sobolev
Meghan G
MG
Donaldson
D000058:Accidental Falls / Q000517:prevention & control* / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis; D012107:Research Design; D018570:Risk Assessment; D055502:Secondary Prevention