Journal ArticleDOI
Use of analysis of covariance in clinical trials: a clarification.
TLDR
If one assumes that treatment groups have been successfully balanced by randomization, then ANCOVA is more powerful than the t-test under a broad range of conditions; this is true even in the model assumed by the t -test, and regardless of the presence of measurement error.About:
This article is published in Controlled Clinical Trials.The article was published on 1986-12-01. It has received 15 citations till now. The article focuses on the topics: Analysis of covariance.read more
Citations
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Journal ArticleDOI
Change from baseline and analysis of covariance revisited
TL;DR: It is shown that it is not a necessary condition for groups to be equal at baseline, not even in expectation, for ANCOVA to provide unbiased estimates of treatment effects and it is very difficult to imagine circumstances under which SACS would be unbiased and a causal interpretation could be made.
Journal ArticleDOI
Adjusting for baseline: change or percentage change?
TL;DR: This paper provides guidance on the choice between change from baseline or percentagechange from baseline as the analysis variable by means of plots of these variables versus baseline.
Journal ArticleDOI
Methods for Analysis of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods
Nathaniel O'Connell,Lin Dai,Yunyun Jiang,Jaime L. Speiser,Ralph C. Ward,Wei Wei,Rachel Carroll,Mulugeta Gebregziabher +7 more
TL;DR: The results demonstrate that each method leads to unbiased treatment effect estimates, and based on precision of estimates, 95% coverage probability, and power, ANCOVA modeling of either change scores or post-treatment score as the outcome, prove to be the most effective.
Journal ArticleDOI
Meta-analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual participant data
Richard D Riley,Iram Kauser,Martin Bland,Lutgarde Thijs,Jan A. Staessen,Jan A. Staessen,Ji-Guang Wang,François Gueyffier,Jonathan J Deeks +8 more
TL;DR: Without IPD and with unavailable ANCOVA estimates, reviewers should limit meta-analyses to those trials with baseline balance, and it is shown that the other approaches can give substantially different meta-analysis results.
Journal ArticleDOI
A randomized, double-blind, placebo-controlled trial to evaluate the effect of enalapril in patients with clinical diabetic nephropathy.
John H. Bauer,Garry P. Reams,John E. Hewett,David M. Klachko,Alisa Lau,Catherine Messina,Vicki Knaus +6 more
TL;DR: In the absence of changes in blood pressure, the addition of an ACE inhibitor to patients with clinical diabetic nephropathy could not be shown to confer a unique renal protective effect.
References
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Book
Statistical Principles in Experimental Design
TL;DR: In this article, the authors introduce the principles of estimation and inference: means and variance, means and variations, and means and variance of estimators and inferors, and the analysis of factorial experiments having repeated measures on the same element.
Journal ArticleDOI
Statistical Principles in Experimental Design
TL;DR: This chapter discusses design and analysis of single-Factor Experiments: Completely Randomized Design and Factorial Experiments in which Some of the Interactions are Confounded.
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A paradox in the interpretation of group comparisons.
TL;DR: It is common practice in behavioral research, and in other areas, to apply the analysis of covariance in the investigation of preexisting natural groups to rule out the possibility that observed differences in the dependent variable might logically be caused by Differences in the independent variable.
Book
Statistical Methods for Comparative Studies Techniques for Bias Reduction
TL;DR: This work presents a meta-analysis of Randomized and Nonrandomized Studies on the treatment effect of Premeasure/Postmeasure Designs on the basis of Covariance, Association and Causality, and some general Considerations in Controlling Bias.
Journal ArticleDOI
How much of the placebo 'effect' is really statistical regression?
TL;DR: It is argued that most improvements attributed to the placebo effect are actually instances of statistical regression, and caution is urged in interpreting patient improvements as causal effects of the authors' actions and should avoid the conceit of assuming that their personal presence has strong healing powers.