Literature DB >> 12325106

The role of covariates in estimating treatment effects and risk in long-term clinical trials.

Ian Ford1, John Norrie.   

Abstract

This paper reviews previously published work showing that the impact of including covariates in models used to estimate the magnitude of treatment effects in long-term clinical trials is different from what would be predicted from results for the normal linear model. Typically, models with and without covariates cannot simultaneously be valid. A case is made for the use of data from clinical trials to model the future risk and potential benefits of treatment in individual subjects. The methods and results are illustrated using data from the West of Scotland Coronary Prevention Study. Copyright 2002 John Wiley & Sons, Ltd.

Mesh：

Year:  2002        PMID： 12325106     DOI： 10.1002/sim.1294

Source DB:  PubMed          Journal:  Stat Med        ISSN： 0277-6715            Impact factor:   2.373


  14 in total

1.  Covariate adjustment increased power in randomized controlled trials: an example in traumatic brain injury.

Authors:  Elizabeth L Turner; Pablo Perel; Tim Clayton; Phil Edwards; Adrian V Hernández; Ian Roberts; Haleema Shakur; Ewout W Steyerberg
Journal:  J Clin Epidemiol       Date:  2011-12-09       Impact factor: 6.437

2.  Does the extended Glasgow Outcome Scale add value to the conventional Glasgow Outcome Scale?

Authors:  James Weir; Ewout W Steyerberg; Isabella Butcher; Juan Lu; Hester F Lingsma; Gillian S McHugh; Bob Roozenbeek; Andrew I R Maas; Gordon D Murray
Journal:  J Neurotrauma       Date:  2012-01-01       Impact factor: 5.269

3.  Combining adjusted and unadjusted findings in mixed research synthesis.

Authors:  Corrine I Voils; Jamie L Crandell; YunKyung Chang; Jennifer Leeman; Margarete Sandelowski
Journal:  J Eval Clin Pract       Date:  2010-10-06       Impact factor: 2.431

4.  Continuous covariate imbalance and conditional power for clinical trial interim analyses.

Authors:  Jody D Ciolino; Renee' H Martin; Wenle Zhao; Edward C Jauch; Michael D Hill; Yuko Y Palesch
Journal:  Contemp Clin Trials       Date:  2014-03-07       Impact factor: 2.226

5.  Measuring continuous baseline covariate imbalances in clinical trial data.

Authors:  Jody D Ciolino; Reneé H Martin; Wenle Zhao; Michael D Hill; Edward C Jauch; Yuko Y Palesch
Journal:  Stat Methods Med Res       Date:  2011-08-24       Impact factor: 3.021

6.  Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint.

Authors:  Steven D Lauzon; Wenle Zhao; Paul J Nietert; Jody D Ciolino; Michael D Hill; Viswanathan Ramakrishnan
Journal:  Stat Methods Med Res       Date:  2021-11-29       Impact factor: 2.494

7.  Covariate imbalance and adjustment for logistic regression analysis of clinical trial data.

Authors:  Jody D Ciolino; Renée H Martin; Wenle Zhao; Edward C Jauch; Michael D Hill; Yuko Y Palesch
Journal:  J Biopharm Stat       Date:  2013       Impact factor: 1.051

8.  Identifying the odds ratio estimated by a two-stage instrumental variable analysis with a logistic regression model.

Authors:  Stephen Burgess
Journal:  Stat Med       Date:  2013-06-03       Impact factor: 2.373

9.  Covariate adjustment had similar benefits in small and large randomized controlled trials.

Authors:  Douglas D Thompson; Hester F Lingsma; William N Whiteley; Gordon D Murray; Ewout W Steyerberg
Journal:  J Clin Epidemiol       Date:  2014-11-13       Impact factor: 6.437

Review 10.  Four layer bandage compared with short stretch bandage for venous leg ulcers: systematic review and meta-analysis of randomised controlled trials with data from individual patients.

Authors:  Susan O'Meara; Jayne Tierney; Nicky Cullum; J Martin Bland; Peter J Franks; Trevor Mole; Mark Scriven
Journal:  BMJ       Date:  2009-04-17
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