Literature DB >> 12057878

Guidelines for the design of clinical trials with longitudinal outcomes.

Sally Galbraith1, Ian C Marschner.   

Abstract

A common objective of longitudinal clinical trials is to compare rates of change in a continuous response variable between two groups. The power realized for such a study is a function of both the number of people recruited and the planned number of measurements for each participant. By varying these two quantities in opposite directions, power can be kept at the desired level. We consider the problem of how best to choose the sample size and frequency of measurement, with a view to minimizing either the total number of measurements or the cost of a study. Some general guidelines are first developed for the situation in which all participants have complete observations. In practice, however, longitudinal studies often suffer from dropout, where a participant leaves the study permanently so that no further observations are possible. We therefore consider the impact of unanticipated dropout on power and also ways of allowing for dropout at the design stage. Based on our results, we propose some general design guidelines for longitudinal trials comparing rates of change when dropout is present.

Entities:  

Mesh:

Year:  2002        PMID: 12057878     DOI: 10.1016/s0197-2456(02)00205-2

Source DB:  PubMed          Journal:  Control Clin Trials        ISSN: 0197-2456


  12 in total

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5.  Designing clinical trials to test disease-modifying agents: application to the treatment trials of Alzheimer's disease.

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Journal:  Clin Trials       Date:  2011-02       Impact factor: 2.486

6.  Power analysis of longitudinal studies with piecewise linear growth and attrition.

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7.  Power and sample size calculations for longitudinal studies comparing rates of change with a time-varying exposure.

Authors:  X Basagaña; D Spiegelman
Journal:  Stat Med       Date:  2010-01-30       Impact factor: 2.373

8.  Accelerated longitudinal designs: An overview of modelling, power, costs and handling missing data.

Authors:  Sally Galbraith; Jack Bowden; Adrian Mander
Journal:  Stat Methods Med Res       Date:  2016-07-11       Impact factor: 3.021

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