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Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:121-9. doi: 10.1002/pds.3242.

Assessing the applicability of trial evidence to a target sample in the presence of heterogeneity of treatment effect.

Author information

  • 1Department of Family Medicine, College of Human Medicine, Michigan State University, East Lansing, MI, USA. cweiss9@jhmi.edu

Abstract

PURPOSE:

To propose methods for the quantitative assessment of the applicability of evidence from a trial to a target sample using individual data.

METHODS:

Demonstration was with a trial of drug therapy to prevent mortality and an accompanying registry of people with heart failure. Principal components analysis with biplots did not identify measurement discrepancies. Multiple imputation with chained equations addressed missing predictor values. A proportional hazards model with interaction term, including graphical interpretation and a multivariate interaction test, identified heterogeneity of treatment effect. An interval of homogeneity of treatment effect was the interval of the baseline risk of outcome in which no two treatment effects were statistically significantly different. Absolute risk reduction for individuals was estimated for both benefit and harm outcomes and presented in a bivariate treatment effects scatterplot.

RESULTS:

Overall, the trial evidence applied to most of the registry according to overlapping distributions of estimated benefit and harm. However, 52% of trial and 33% of registry participants were estimated to have net benefit, and 14% of trial and 36% of registry participants were estimated to have strong net harmful treatment effect, that is, the individual estimate of harm was more than twice the estimate of benefit.

CONCLUSIONS:

The proposed methods provide quantitative assessment of the applicability of trial evidence to a target sample. They combine the strengths of different study designs, namely, unbiased effects estimation from trials and representation in observational studies, while addressing the practical challenges of combining information, namely, measurement discrepancies and missing data.

Copyright © 2012 John Wiley & Sons, Ltd.

PMID:
22552987
[PubMed - indexed for MEDLINE]
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