A comprehensive algorithm for determining whether a run‐in strategy will be a cost‐effective design modification in a randomized clinical trial

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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.