title: A comprehensive algorithm for determining whether a run-in strategy will be a cost-effective design modification in a randomized clinical trial.
abstract: In randomized clinical trials, poor compliance and treatment intolerance lead to reduced between-group differences, increased sample size requirements, and increased cost. A run-in strategy is intended to reduce these problems. In this paper, we develop a comprehensive set of measures specifically sensitive to the effect of a run-in on cost and sample size requirements, both before and after randomization. Using these measures, we describe a step-by-step algorithm through which one can estimate the cost-effectiveness of a potential run-in. Because the cost-effectiveness of a run-in is partly mediated by its effect on sample size, we begin by discussing the likely impact of a planned run-in on the required number of randomized, eligible, and screened subjects. Run-in strategies are most likely to be cost-effective when: (1) per patient costs during the post-randomization as compared to the screening period are high; (2) poor compliance is associated with a substantial reduction in response to treatment; (3) the number of screened patients needed to identify a single eligible patient is small; (4) the run-in is inexpensive; (5) for most patients, the run-in compliance status is maintained following randomization and, most importantly, (6) many subjects excluded by the run-in are treatment intolerant or non-compliant to the extent that we expect little or no treatment response. Our analysis suggests that conditions for the cost-effectiveness of run-in strategies are stringent. In particular, if the only purpose of a run-in is to exclude ordinary partial compliers, the run-in will frequently add to the cost of the trial. Often, the cost-effectiveness of a run-in requires that one can identify and exclude a substantial number of treatment intolerant or otherwise unresponsive subjects.
authors.affiliation: Washington University School of Medicine, Division of Biostatistics, St. Louis, MO 63110.
authors.forename: K B
authors.initials: KB
authors.lastname: Schechtman
authors.forename: M E
authors.initials: ME
authors.lastname: Gordon
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic / Q000191:economics*; D003362:Cost-Benefit Analysis; D006801:Humans; D010349:Patient Compliance; D016032:Randomized Controlled Trials as Topic / Q000191:economics*; D012107:Research Design