Adjusting for drop-out in clinical trials with repeated measures: design and analysis issues.
Recently, Wu and Follmann developed summary measures to adjust for informative drop-out in longitudinal studies where drop-out depends on the underlying true value of the response. In this paper we evaluate these procedures in the common situation where drop-out depends on the observed responses. We also discuss various design and analysis strategies which minimize the bias obtained with this type of drop-out. Of particular interest is the use of multiple measurements of the response at each visit to reduce bias. These strategies are evaluated with a simulation study. The results are highlighted with applications to both a hypertensive and a respiratory disease clinical trial, where multiple measurements of the primary response were made for all participants at each visit.
Wu
M C
MC
Office of Biostatistics Research, National Heart, Lung, and Blood Institute, 6701 Rockledge Drive, Bethesda, MD 20892-7938, USA.
Albert
P S
PS
Wu
B U
BU
D000959:Antihypertensive Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D005541:Forced Expiratory Volume / Q000187:drug effects; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D016014:Linear Models; D008137:Longitudinal Studies; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012107:Research Design; D000515:alpha 1-Antitrypsin / Q000627:therapeutic use; D019896:alpha 1-Antitrypsin Deficiency / Q000188:drug therapy
D000959:Antihypertensive Agents; D000515:alpha 1-Antitrypsin