Sensitivity analysis using an imputation method for missing binary data in clinical trials
Identifieur interne : 002149 ( Istex/Curation ); précédent : 002148; suivant : 002150Sensitivity analysis using an imputation method for missing binary data in clinical trials
Auteurs : Michael A. Proschan [États-Unis] ; Robert P. Mcmahon [États-Unis] ; Joanna H. Shih [États-Unis] ; Sally A. Hunsberger [États-Unis] ; Nancy L. Geller [États-Unis] ; Genell Knatterud [États-Unis] ; Janet Wittes [États-Unis]Source :
- Journal of Statistical Planning and Inference [ 0378-3758 ] ; 2001.
English descriptors
- Teeft :
- Acip, Available data, Baseline data, Binary data, Clinical trial, Clinical trials, Conditional, Conditional expectation, Conditional power, Conditional power calculations, Conservative assumption, Control patients, Control populations, Current trial, Elsevier science, Entire population, Erent, Erent models, Error rate, Event probabilities, Event probability, Event rate, Event rates, Full data, Future data, Imputation, Ischemia, Ischemia pilot, Medical therapy, Missingness, Multiple imputation, Null hypothesis, Opposite imputation, Opposite procedure, Outcome data, Particular realization, Proschan, Random sample, Respective arms, Revascularization, Revascularization arms, Same amount, Sample proportions, Sensitivity analysis, Simple methods, Standard deviation, Standard error, Statistical planning, Treatment arms, Treatment assignment, Treatment groups, Treatment strategies, Worst case analysis.
Abstract
Abstract: Missing data in clinical trials can result in biased treatment effect estimates and tests if the analysis includes only the observed data. Two simple methods of compensating for this potential bias in trials with a binary endpoint were suggested by Wittes et al. (Statist. Med. 8 (1989) 415–425). We study the statistical properties of these procedures and show that they are robust against certain model departures.
Url:
DOI: 10.1016/S0378-3758(00)00332-3
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<term>Binary data</term>
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<term>Clinical trials</term>
<term>Conditional</term>
<term>Conditional expectation</term>
<term>Conditional power</term>
<term>Conditional power calculations</term>
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<term>Event probabilities</term>
<term>Event probability</term>
<term>Event rate</term>
<term>Event rates</term>
<term>Full data</term>
<term>Future data</term>
<term>Imputation</term>
<term>Ischemia</term>
<term>Ischemia pilot</term>
<term>Medical therapy</term>
<term>Missingness</term>
<term>Multiple imputation</term>
<term>Null hypothesis</term>
<term>Opposite imputation</term>
<term>Opposite procedure</term>
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<term>Same amount</term>
<term>Sample proportions</term>
<term>Sensitivity analysis</term>
<term>Simple methods</term>
<term>Standard deviation</term>
<term>Standard error</term>
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<front><div type="abstract" xml:lang="en">Abstract: Missing data in clinical trials can result in biased treatment effect estimates and tests if the analysis includes only the observed data. Two simple methods of compensating for this potential bias in trials with a binary endpoint were suggested by Wittes et al. (Statist. Med. 8 (1989) 415–425). We study the statistical properties of these procedures and show that they are robust against certain model departures.</div>
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