Interim analysis of continuous long-term endpoints in clinical trials with longitudinal outcomes.
This paper discusses interim analysis for clinical trials where the primary endpoint is observed at a specific long-term follow-up time, but where repeated measures of the same outcome are also taken at earlier times. Methods are considered for improving the efficiency with which the long-term treatment difference is estimated, making use of information from shorter-term follow-up times. This approach to interim analysis has previously been studied for binary endpoints assessed at two time points during follow-up. Here we adapt and extend this methodology to include continuous endpoints assessed at an arbitrary number of follow-up times, making use of methods for analysing multivariate normal data subject to monotone missingness and unstructured mean and covariance relationships. The magnitude of efficiency gains obtained by using short-term measurements is considered, as well as how these gains depend on the number and timing of the short-term measurements. Sequential analysis of treatment differences is discussed, including the extent to which efficiency gains translate into reductions in the expected duration of a sequentially monitored trial. The methods are illustrated on a data set involving a placebo-controlled comparison of longitudinal cholesterol measurements.
Galbraith
Sally
S
School of Mathematics, The University of New South Wales, NSW 2052, Australia. sallyg@maths.unsw.edu.au
Marschner
Ian C
IC
D001315:Australia; D002784:Cholesterol / Q000097:blood; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care
D002784:Cholesterol