S D Walter1, Gordon Guyatt, Victor M Montori, R Cook, K Prasad. 1. Department of Clinical Epidemiology and Biostatistics, McMaster University, HSC-2C16, 1200 Main St West, Hamilton, Ontario L8N 3Z5, Canada. walter@mcmaster.ca
Abstract
BACKGROUND AND OBJECTIVES: Development of a new method of analysis to evaluate the acceptability of (or preferences for) the treatments in a randomized trial, and the benefit of treatment among compliers. MATERIALS AND METHODS: We characterize trial participants through the groups who would: accept either treatment if offered (compliers); refuse one treatment but accept the other if it is offered to them (two groups of preferers); or prefer one treatment and insist on it if it is not offered to them initially (two groups of insisters). RESULTS: We show that in our framework, one can always estimate the proportions of patients in these five preference groups. However, constraints are required to estimate the corresponding outcome rates, and thus estimate the treatment effect in the compliers. We propose two possible sets of constraints and illustrate them by numerical examples. CONCLUSIONS: The traditional intention-to-treat analysis avoids biases associated with the alternative per-protocol or as-treated approaches, but it provides imperfect information about the expected treatment effect among patients who are committed to taking the treatment. Many physicians and patients want to know the expected benefit if they adhere to the therapy. Our preference-based analysis provides an estimate of treatment benefit among such patients.
RCT Entities:
BACKGROUND AND OBJECTIVES: Development of a new method of analysis to evaluate the acceptability of (or preferences for) the treatments in a randomized trial, and the benefit of treatment among compliers. MATERIALS AND METHODS: We characterize trial participants through the groups who would: accept either treatment if offered (compliers); refuse one treatment but accept the other if it is offered to them (two groups of preferers); or prefer one treatment and insist on it if it is not offered to them initially (two groups of insisters). RESULTS: We show that in our framework, one can always estimate the proportions of patients in these five preference groups. However, constraints are required to estimate the corresponding outcome rates, and thus estimate the treatment effect in the compliers. We propose two possible sets of constraints and illustrate them by numerical examples. CONCLUSIONS: The traditional intention-to-treat analysis avoids biases associated with the alternative per-protocol or as-treated approaches, but it provides imperfect information about the expected treatment effect among patients who are committed to taking the treatment. Many physicians and patients want to know the expected benefit if they adhere to the therapy. Our preference-based analysis provides an estimate of treatment benefit among such patients.
Authors: Moonseong Heo; Paul Meissner; Alain H Litwin; M Diane McKee; Alison Karasz; Earle C Chambers; Ming-Chin Yeh; Judith Wylie-Rosett Journal: Stat Methods Med Res Date: 2018-04-10 Impact factor: 3.021