A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals

Austin, Peter C.; Manca, Andrea; Zwarenstein, Merrick; Juurlink, David N.; Stanbrook, Matthew B.

doi:10.1016/j.jclinepi.2009.06.002

Cited by 146 publications

(163 citation statements)

References 162 publications

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“…While we describe on measures of treatment effect when outcomes are continuous, binary, or time-to-event in nature our primary focus is on binary or time-to-event outcomes, as they occur more frequently in published reports of RCTs in the medical literature (Austin et al, 2010a).…”

Section: Measures Of Treatment Effects In Rctsmentioning

confidence: 99%

A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review

Austin¹,

Laupacis²

2011

The International Journal of Biostatistics

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In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to be directly compared between treatment arms. When outcomes are binary, the effect of treatment can be summarized using relative risks, absolute risk reductions and the number needed to treat (NNT). When outcomes are time-to-event in nature, the effect of treatment on the absolute reduction of the risk of an event occurring within a specified duration of follow-up and the associated NNT can be estimated. In observational studies of the effect of treatments on health outcomes, treatment is frequently confounded with baseline covariates. Regression adjustment is commonly used to estimate the adjusted effect of treatment on outcomes. We highlight several limitations of measures of treatment effect that are directly obtained from regression models. We illustrate how both regression-based approaches and propensity-score based approaches allow one to estimate the same measures of treatment effect as those that are commonly reported in RCTs. The CONSORT statement recommends that both relative and absolute measures of treatment effects be reported for RCTs with dichotomous outcomes. The methods described in this paper will allow for similar reporting in observational studies.

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Section: Measures Of Treatment Effects In Rctsmentioning

confidence: 99%

A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review

Austin¹,

Laupacis²

2011

The International Journal of Biostatistics

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“…Since dichotomous outcomes occur frequently in health services, medical, epidemiological, and public health research (Austin et al, 2010), we focus our attention on multilevel logistic regression models. The paper is structured as follows: In Section 2, we describe a series of Monte Carlo simulations that were conducted to examine the performance of different statistical software procedures for estimating multilevel logistic regression models.…”

Section: Introductionmentioning

confidence: 99%

Estimating Multilevel Logistic Regression Models When the Number of Clusters is Low: A Comparison of Different Statistical Software Procedures

Austin¹

2010

The International Journal of Biostatistics

149

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Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

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“…It is well-established in statistical literature that hypothesis testing is inappropriate to evaluate differences in the distribution of baseline patient characteristics between treatment groups in RCTs. 3 Nevertheless, the authors decided, based on P values, that both groups were balanced and adjustment of potential confounders was not necessary. It needs to be emphasized that even nonsignificant (P Ͼ .05) imbalances of strong prognostic factors may still result in substantial bias and therefore requires adjustment.…”

mentioning

confidence: 99%

Increased Mortality Following Telemonitoring in Frail Elderly Patients: Look Before You Leap!

Trappenburg¹,

Schuurmans²

2012

Arch Intern Med

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A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals

Cited by 146 publications

References 162 publications

A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review

A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review

Estimating Multilevel Logistic Regression Models When the Number of Clusters is Low: A Comparison of Different Statistical Software Procedures

Increased Mortality Following Telemonitoring in Frail Elderly Patients: Look Before You Leap!

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