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1.
Stat Med. 2014 May 30;33(12):2062-76. doi: 10.1002/sim.6076. Epub 2013 Dec 15.

The SIMEX approach to measurement error correction in meta-analysis with baseline risk as covariate.

Author information

1
University of Verona, via dell'Artigliere 19, I-37129, Verona, Italy.

Abstract

This paper investigates the use of SIMEX, a simulation-based measurement error correction technique, for meta-analysis of studies involving the baseline risk of subjects in the control group as explanatory variable. The approach accounts for the measurement error affecting the information about either the outcome in the treatment group or the baseline risk available from each study, while requiring no assumption about the distribution of the true unobserved baseline risk. This robustness property, together with the feasibility of computation, makes SIMEX very attractive. The approach is suggested as an alternative to the usual likelihood analysis, which can provide misleading inferential results when the commonly assumed normal distribution for the baseline risk is violated. The performance of SIMEX is compared to the likelihood method and to the moment-based correction through an extensive simulation study and the analysis of two datasets from the medical literature.

KEYWORDS:

SIMEX; baseline risk; likelihood analysis; measurement error; meta-analysis; moment-based correction

PMID:
24339017
DOI:
10.1002/sim.6076
[Indexed for MEDLINE]
2.
Stat Methods Med Res. 2013 Jun;22(3):307-23. doi: 10.1177/0962280211412244. Epub 2011 Sep 8.

Modelling the effect of baseline risk in meta-analysis: a review from the perspective of errors-in-variables regression.

Author information

1
Trial and Statistics-HOVON Data Center, Erasmus MC- Daniel den Hoed Cancer Center, Rotterdam, The Netherlands. w.ghideyalemayehu@erasmusmc.nl

Abstract

In meta-analysis of clinical trials, investigating the relationship between the baseline risk and the treatment benefit is often of interest in order to explain the between trials heterogeneity with respect to treatment effect. The relationship is commonly described with a linear model taking into account the fact that the latent baseline risk is estimated from a finite sample and thus subjected to measurement error. Depending on the specific assumption about the latent baseline risks, two different classes of methods can be pursued. In the literature, it is commonly assumed that the latent baseline risks are sampled from a (normal) distribution. Such methods are often criticised for needing a distribution. Here, we propose the use of methods that require no distributional assumption on the baseline risks. A number of alternative methods are reviewed and are illustrated via simulation and by application to a published meta-analysis data.

KEYWORDS:

Baseline risk; conditional score; corrected score; measurement error models; meta-analysis

PMID:
21908417
DOI:
10.1177/0962280211412244
[Indexed for MEDLINE]
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3.
J Clin Epidemiol. 2004 Jul;57(7):683-97.

Meta-regression detected associations between heterogeneous treatment effects and study-level, but not patient-level, factors.

Author information

1
Institute for Clinical Research and Health Policy Studies, Tufts-New England Medical Center and Tufts University School of Medicine, 750 Washington Street, Boston, MA 02111, USA. cschmid@tufts-nemc.org

Abstract

OBJECTIVE:

Two investigations evaluate Bayesian meta-regression for detecting treatment interactions.

STUDY DESIGN AND SETTING:

The first compares analyses of aggregate and individual patient data on 1,860 subjects from 11 trials testing angiotensin converting enzyme (ACE) inhibitors for nondiabetic kidney disease. The second explores meta-regression for detecting treatment interaction on 671 covariates, including the baseline risk, from 232 meta-analyses of binary outcomes compiled from the Cochrane Collaboration and the medical literature.

RESULTS:

In the ACE inhibitor study, treatment effects were homogeneous so meta-regression identified no interactions. Analysis of individual patient data using a multilevel model, however, discovered that treatment reduced glomerular filtration rate (GFR) more among patients with higher baseline proteinuria. The second investigation found meta-regression most effective for detecting treatment interactions with study-level factors in meta-analyses with >10 studies, heterogeneous treatment effects, or significant overall treatment effects. Under all three conditions, 46% of meta-regressions produced strong interactions (posterior probability >0.995) compared with 6% otherwise. Baseline risk was associated with the odds ratio in 6% of meta-analyses, half the rate found using maximum likelihood.

CONCLUSION:

Meta-regression can detect interactions of treatment with study-level factors when treatment effects are heterogeneous. Individual patient data are needed for patient-level factors and homogeneous effects.

PMID:
15358396
DOI:
10.1016/j.jclinepi.2003.12.001
[Indexed for MEDLINE]
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4.
Stat Med. 1998 Sep 15;17(17):1923-42.

An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials.

Author information

1
Department of Medicine, New England Medical Center, Boston, MA 02111, USA. cschmid@es.nemc.org

Abstract

If the control rate (CR) in a clinical trial represents the incidence or the baseline severity of illness in the study population, the size of treatment effects may tend to very with the size of control rates. To investigate this hypothesis, we examined 115 meta-analyses covering a wide range of medical applications for evidence of a linear relationship between the CR and three treatment effect (TE) measures: the risk difference (RD); the log relative risk (RR), and the log odds ratio (OR). We used a hierarchical model that estimates the true regression while accounting for the random error in the measurement of and the functional dependence between the observed TE and the CR. Using a two standard error rule of significance, we found the control rate was about two times more likely to be significantly related to the RD (31 per cent) than to the RR (13 per cent) or the OR (14 per cent). Correlations between TE and CR were more likely when the meta-analysis included 10 or more trials and if patient follow-up was less than six months and homogeneous. Use of weighted linear regression (WLR) of the observed TE on the observed CR instead of the hierarchical model underestimated standard errors and overestimated the number of significant results by a factor of two. The significant correlation between the CR and the TE suggests that, rather than merely pooling the TE into a single summary estimate, investigators should search for the causes of heterogeneity related to patient characteristics and treatment protocols to determine when treatment is most beneficial and that they should plan to study this heterogeneity in clinical trials.

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