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Baseline risk as predictor of treatment benefit: three clinical meta-re-analyses
Statistics in medicine, 2000-12, Vol.19 (24), p.3497-3518
Arends, Lidia R.
Hoes, Arno W.
Lubsen, Jacobus
Grobbee, Diederik E.
Stijnen, Theo
2000
Details
Autor(en) / Beteiligte
Arends, Lidia R.
Hoes, Arno W.
Lubsen, Jacobus
Grobbee, Diederik E.
Stijnen, Theo
Titel
Baseline risk as predictor of treatment benefit: three clinical meta-re-analyses
Ist Teil von
Statistics in medicine, 2000-12, Vol.19 (24), p.3497-3518
Ort / Verlag
Chichester, UK: John Wiley & Sons, Ltd
Erscheinungsjahr
2000
Link zum Volltext
Quelle
Wiley Online Library Journals Frontfile Complete
Beschreibungen/Notizen
A relationship between baseline risk and treatment effect is increasingly investigated as a possible explanation of between‐study heterogeneity in clinical trial meta‐analysis. An approach that is still often applied in the medical literature is to plot the estimated treatment effects against the estimated measures of risk in the control groups (as a measure of baseline risk), and to compute the ordinary weighted least squares regression line. However, it has been pointed out by several authors that this approach can be seriously flawed. The main problem is that the observed treatment effect and baseline risk measures should be viewed as estimates rather than the true values. In recent years several methods have been proposed in the statistical literature to potentially deal with the measurement errors in the estimates. In this article we propose a vague priors Bayesian solution to the problem which can be carried out using the ‘Bayesian inference using Gibbs sampling’ (BUGS) implementation of Markov chain Monte Carlo numerical integration techniques. Different from other proposed methods, it uses the exact rather than an approximate likelihood, while it can handle many different treatment effect measures and baseline risk measures. The method differs from a recently proposed Bayesian method in that it explicitly models the distribution of the underlying baseline risks. We apply the method to three meta‐analyses published in the medical literature and compare the results with the outcomes of the other recently proposed methods. In particular we compare our approach to McIntosh's method, for which we show how it can be carried out using standard statistical software. We conclude that our proposed method offers a very general and flexible solution to the problem, which can be carried out relatively easily with existing Bayesian analysis software. A confidence band for the underlying relationship between true effect measure and baseline risk and a confidence interval for the value of the baseline risk measure for which there is no treatment effect are easily obtained by‐products of our approach. Copyright © 2000 John Wiley & Sons, Ltd.
Sprache
Englisch
Identifikatoren
ISSN: 0277-6715
eISSN: 1097-0258
DOI: 10.1002/1097-0258(20001230)19:24<3497::AID-SIM830>3.0.CO;2-H
Titel-ID: cdi_proquest_miscellaneous_72467711
Format
–
Schlagworte
Bayes Theorem
,
Biological and medical sciences
,
Case-Control Studies
,
Clinical Trials as Topic - statistics & numerical data
,
Computerized, statistical medical data processing and models in biomedicine
,
Female
,
Humans
,
Hypercholesterolemia - drug therapy
,
Hypertension - drug therapy
,
Medical sciences
,
Medical statistics
,
Meta-Analysis as Topic
,
Models, Statistical
,
Pregnancy
,
Regression Analysis
,
Risk
,
Tocolysis - methods
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