Statistics in medicine
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Statistics in medicine · Mar 2013
ReviewAnalysis of multicentre trials with continuous outcomes: when and how should we account for centre effects?
In multicentre trials, randomisation is often carried out using permuted blocks stratified by centre. It has previously been shown that stratification variables used in the randomisation process should be adjusted for in the analysis to obtain correct inference. For continuous outcomes, the two primary methods of accounting for centres are fixed-effects and random-effects models. ⋯ With small samples sizes, random-effects models maintained nominal coverage rates when a degree-of-freedom (DF) correction was used. We assessed the robustness of random-effects models when assumptions regarding the distribution of the centre effects were incorrect and found this had no impact on results. We conclude that random-effects models offer many advantages over fixed-effects models in certain situations and should be used more often in practice.
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Statistics in medicine · May 2008
ReviewA critical appraisal of propensity-score matching in the medical literature between 1996 and 2003.
Propensity-score methods are increasingly being used to reduce the impact of treatment-selection bias in the estimation of treatment effects using observational data. Commonly used propensity-score methods include covariate adjustment using the propensity score, stratification on the propensity score, and propensity-score matching. Empirical and theoretical research has demonstrated that matching on the propensity score eliminates a greater proportion of baseline differences between treated and untreated subjects than does stratification on the propensity score. ⋯ Thirteen (28 per cent) of the articles explicitly used statistical methods appropriate for the analysis of matched data when estimating the treatment effect and its statistical significance. Common errors included using the log-rank test to compare Kaplan-Meier survival curves in the matched sample, using Cox regression, logistic regression, chi-squared tests, t-tests, and Wilcoxon rank sum tests in the matched sample, thereby failing to account for the matched nature of the data. We provide guidelines for the analysis and reporting of studies that employ propensity-score matching.
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Statistics in medicine · Jan 2007
ReviewA 25-year review of sequential methodology in clinical studies.
This paper explores the theoretical developments and subsequent uptake of sequential methodology in clinical studies in the 25 years since Statistics in Medicine was launched. The review examines the contributions which have been made to all four phases into which clinical trials are traditionally classified and highlights major statistical advancements, together with assessing application of the techniques. The vast majority of work has been in the setting of phase III clinical trials and so emphasis will be placed here. Finally, comments are given indicating how the subject area may develop in the future.
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Statistics in medicine · May 2005
Review Comparative StudyIdentification and impact of outcome selection bias in meta-analysis.
The systematic review community has become increasingly aware of the importance of addressing the issues of heterogeneity and publication bias in meta-analyses. A potentially bigger threat to the validity of a meta-analysis appears relatively unnoticed. The within-study selective reporting of outcomes, defined as the selection of a subset of the original variables recorded for inclusion in publication of trials, can theoretically have a substantial impact on the results. ⋯ In cases where the level of suspicion was high, sensitivity analysis was undertaken to assess the robustness of the conclusion to this bias. Although within-study selection was evident or suspected in several trials, the impact on the conclusions of the meta-analyses was minimal. This paper deals with the identification of, sensitivity analysis for, and impact of within-study selective reporting in meta-analysis.
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Statistics in medicine · May 2004
ReviewPresentation of multivariate data for clinical use: The Framingham Study risk score functions.
The Framingham Heart Study has been a leader in the development and dissemination of multivariable statistical models to estimate the risk of coronary heart disease. These models quantify the impact of measurable and modifiable risk factors on the development of coronary heart disease and can be used to generate estimates of risk of coronary heart disease over a predetermined period, for example the next 10 years. ⋯ This system represents an effort to make available a tool for clinicians to aid in their decision-making process regarding treatment and to assist them in motivating patients toward healthy behaviours. The system is also readily available to patients who can easily estimate their own coronary heart disease risk and monitor this risk over time.