Literature DB >> 1912270

The effect of screening on some pretest-posttest test variances.

D A Follmann1.   

Abstract

The clinical trial design in which the endpoint is measured both at baseline and at the end of the study is used in a variety of situations. For two-group designs, test such as the t test or analysis of covariance are commonly used to evaluate treatment efficacy. Often such pretest-posttest trials restrict participation to subjects with a baseline measurement of the endpoint in a certain range. A range may define a disease, or it may be thought that subjects with extreme measurements are more responsive to treatment. This paper examines the effect of screening on the analysis of covariance and t-test variances relative to the population (i.e., unscreened) variances. Bivariate normal and bivariate gamma distributions are assumed for the (pretest, posttest) measurements. Because the sample size required to detect a specified difference between treatment and control is proportional to the variance, the results have direct application to setting sample size.

Mesh:

Year:  1991        PMID: 1912270

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  5 in total

1.  Detecting reliable cognitive change in individual patients with the MATRICS Consensus Cognitive Battery.

Authors:  Bradley E Gray; Robert P McMahon; Michael F Green; Larry J Seidman; Raquelle I Mesholam-Gately; Robert S Kern; Keith H Nuechterlein; Richard S Keefe; James M Gold
Journal:  Schizophr Res       Date:  2014-08-22       Impact factor: 4.939

2.  Semiparametric Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data.

Authors:  Marie Davidian; Anastasios A Tsiatis; Selene Leon
Journal:  Stat Sci       Date:  2005-08       Impact factor: 2.901

3.  Analysis of covariance with pre-treatment measurements in randomized trials under the cases that covariances and post-treatment variances differ between groups.

Authors:  Takashi Funatogawa; Ikuko Funatogawa; Yu Shyr
Journal:  Biom J       Date:  2011-05       Impact factor: 2.207

4.  Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest-Posttest Study.

Authors:  Chiung-Yu Huang; Jing Qin; Dean A Follmann
Journal:  J Am Stat Assoc       Date:  2008-09-01       Impact factor: 5.033

5.  The statistical analysis of a clinical trial when a protocol amendment changed the inclusion criteria.

Authors:  Christian Lösch; Markus Neuhäuser
Journal:  BMC Med Res Methodol       Date:  2008-04-08       Impact factor: 4.615

  5 in total

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