Group sequential clinical trials for longitudinal data with analyses using summary statistics

• Published in: Statistics in medicine 2005-08, Vol.24 (16), p.2457-2475
• Main Authors: Kittelson, John M., Sharples, Katrina, Emerson, Scott S.
• Format: Article
• Language: English
• Subjects: area under curve; clinical trial; Clinical trials; Data analysis; Humans; Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use; Linear Models; longitudinal endpoint; Longitudinal Studies; mixed-effects model; Peripheral Vascular Diseases - drug therapy; Randomized Controlled Trials as Topic - methods; Software; Statistical analysis; Walking - physiology
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.2127

Longitudinal endpoints are used in clinical trials, and the analysis of the results is often conducted using within‐individual summary statistics. When these trials are monitored, interim analyses that include subjects with incomplete follow‐up can give incorrect decisions due to bias by non‐linearity in the true time trajectory of the treatment effect. Linear mixed‐effects models can be used to remove this bias, but there is a lack of software to support both the design and implementation of monitoring plans in this setting. This paper considers a clinical trial in which the measurement time schedule is fixed (at least for pre‐trial design), and the scientific question is parameterized by a contrast across these measurement times. This setting assures generalizable inference in the presence of non‐linear time trajectories. The distribution of the treatment effect estimate at the interim analyses using the longitudinal outcome measurements is given, and software to calculate the amount of information at each interim analysis is provided. The interim information specifies the analysis timing thereby allowing standard group sequential design software packages to be used for trials with longitudinal outcomes. The practical issues with implementation of these designs are described; in particular, methods are presented for consistent estimation of treatment effects at the interim analyses when outcomes are not measured according to the pre‐trial schedule. Splus/R functions implementing this inference using appropriate linear mixed‐effects models are provided. These designs are illustrated using a clinical trial of statin treatment for the symptoms of peripheral arterial disease. Copyright © 2005 John Wiley & Sons, Ltd.