Ari M Lipsky1, Sander Greenland. 1. Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel Hashomer 52621, Israel. aril@alum.mit.edu
Abstract
BACKGROUND: While adaptive trials tend to improve efficiency, they are also subject to some unique biases. PURPOSE: We address a bias that arises from adaptive randomization in the setting of a time trend in disease incidence. METHODS: We use a potential-outcome model and directed acyclic graphs to illustrate the bias that arises from a changing subject allocation ratio with a concurrent change in background risk. RESULTS: In a trial that uses adaptive randomization, time trends in risk can bias the crude effect estimate obtained by naively combining the data from the different stages of the trial. We illustrate how the bias arises from an interplay of departures from exchangeability among groups and the changing randomization proportions. LIMITATIONS: We focus on risk-ratio and risk-difference analysis. CONCLUSIONS: Analysis of trials using adaptive randomization should involve attention to or adjustment for possible trends in background risk. Numerous modeling strategies are available for that purpose, including stratification, trend modeling, inverse-probability-of-treatment weighting, and hierarchical regression.
BACKGROUND: While adaptive trials tend to improve efficiency, they are also subject to some unique biases. PURPOSE: We address a bias that arises from adaptive randomization in the setting of a time trend in disease incidence. METHODS: We use a potential-outcome model and directed acyclic graphs to illustrate the bias that arises from a changing subject allocation ratio with a concurrent change in background risk. RESULTS: In a trial that uses adaptive randomization, time trends in risk can bias the crude effect estimate obtained by naively combining the data from the different stages of the trial. We illustrate how the bias arises from an interplay of departures from exchangeability among groups and the changing randomization proportions. LIMITATIONS: We focus on risk-ratio and risk-difference analysis. CONCLUSIONS: Analysis of trials using adaptive randomization should involve attention to or adjustment for possible trends in background risk. Numerous modeling strategies are available for that purpose, including stratification, trend modeling, inverse-probability-of-treatment weighting, and hierarchical regression.
Authors: Paul Gallo; Christy Chuang-Stein; Vladimir Dragalin; Brenda Gaydos; Michael Krams; José Pinheiro Journal: J Biopharm Stat Date: 2006-05 Impact factor: 1.051