The role of covariates in estimating treatment effects and risk in long-term clinical trials

Ian Ford; John Norrie

doi:10.1002/sim.1294

The role of covariates in estimating treatment effects and risk in long-term clinical trials

Stat Med. 2002 Oct 15;21(19):2899-908. doi: 10.1002/sim.1294.

Authors

Ian Ford¹, John Norrie

Affiliation

¹ Robertson Centre for Biostatistics, Boyd Orr Building, University of Glasgow, Glasgow G12 8QQ, Scotland, UK. i.ford@stats.gla.ac.uk

PMID: 12325106
DOI: 10.1002/sim.1294

Abstract

This paper reviews previously published work showing that the impact of including covariates in models used to estimate the magnitude of treatment effects in long-term clinical trials is different from what would be predicted from results for the normal linear model. Typically, models with and without covariates cannot simultaneously be valid. A case is made for the use of data from clinical trials to model the future risk and potential benefits of treatment in individual subjects. The methods and results are illustrated using data from the West of Scotland Coronary Prevention Study.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Humans
Longitudinal Studies
Models, Biological*
Models, Statistical*
Randomized Controlled Trials as Topic / methods*
Risk Factors
Treatment Outcome