Literature DB >> 16947139

The design of simulation studies in medical statistics.

Andrea Burton1, Douglas G Altman, Patrick Royston, Roger L Holder.   

Abstract

Simulation studies use computer intensive procedures to assess the performance of a variety of statistical methods in relation to a known truth. Such evaluation cannot be achieved with studies of real data alone. Designing high-quality simulations that reflect the complex situations seen in practice, such as in prognostic factors studies, is not a simple process. Unfortunately, very few published simulation studies provide sufficient details to allow readers to understand fully all the processes required to design a simulation study. When planning a simulation study, it is recommended that a detailed protocol be produced, giving full details of how the study will be performed, analysed and reported. This paper details the important considerations necessary when designing any simulation study, including defining specific objectives of the study, determining the procedures for generating the data sets and the number of simulations to perform. A checklist highlighting the important considerations when designing a simulation study is provided. A small review of the literature identifies the current practices within published simulation studies. Copyright 2006 John Wiley & Sons, Ltd.

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Year:  2006        PMID: 16947139     DOI: 10.1002/sim.2673

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


  189 in total

1.  Impact of measurement error in radon exposure on the estimated excess relative risk of lung cancer death in a simulated study based on the French Uranium Miners' Cohort.

Authors:  Rodrigue S Allodji; Klervi Leuraud; Anne C M Thiébaut; Stéphane Henry; Dominique Laurier; Jacques Bénichou
Journal:  Radiat Environ Biophys       Date:  2012-02-07       Impact factor: 1.925

2.  Requiring an amyloid-beta1-42 biomarker for prodromal Alzheimer's disease or mild cognitive impairment does not lead to more efficient clinical trials.

Authors:  Lon S Schneider; Richard E Kennedy; Gary R Cutter
Journal:  Alzheimers Dement       Date:  2010-09       Impact factor: 21.566

3.  Missing data in alcohol clinical trials: a comparison of methods.

Authors:  Kevin A Hallgren; Katie Witkiewitz
Journal:  Alcohol Clin Exp Res       Date:  2013-07-24       Impact factor: 3.455

4.  The Choice of Analytical Strategies in Inverse-Probability-of-Treatment-Weighted Analysis: A Simulation Study.

Authors:  Shibing Yang; Juan Lu; Charles B Eaton; Spencer Harpe; Kate L Lapane
Journal:  Am J Epidemiol       Date:  2015-08-26       Impact factor: 4.897

5.  Randomization to randomization probability: Estimating treatment effects under actual conditions of use.

Authors:  Brandon J George; Peng Li; Harris R Lieberman; Greg Pavela; Andrew W Brown; Kevin R Fontaine; Madeline M Jeansonne; Gareth R Dutton; Adeniyi J Idigo; Mariel A Parman; Donald B Rubin; David B Allison
Journal:  Psychol Methods       Date:  2017-04-13

6.  Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

Authors:  Yoonsang Kim; Young-Ku Choi; Sherry Emery
Journal:  Am Stat       Date:  2013-08-01       Impact factor: 8.710

7.  Overview, hurdles, and future work in adaptive designs: perspectives from a National Institutes of Health-funded workshop.

Authors:  Christopher S Coffey; Bruce Levin; Christina Clark; Cate Timmerman; Janet Wittes; Peter Gilbert; Sara Harris
Journal:  Clin Trials       Date:  2012-12       Impact factor: 2.486

8.  The trend odds model for ordinal data.

Authors:  Ana W Capuano; Jeffrey D Dawson
Journal:  Stat Med       Date:  2012-12-06       Impact factor: 2.373

9.  Sample size requirements to detect an intervention by time interaction in longitudinal cluster randomized clinical trials with random slopes.

Authors:  Moonseong Heo; Xiaonan Xue; Mimi Y Kim
Journal:  Comput Stat Data Anal       Date:  2013-04-01       Impact factor: 1.681

10.  Bivariate random effects meta-analysis of diagnostic studies using generalized linear mixed models.

Authors:  Haitao Chu; Hongfei Guo; Yijie Zhou
Journal:  Med Decis Making       Date:  2009-12-03       Impact factor: 2.583

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