Literature DB >> 22545023

osDesign: An R Package for the Analysis, Evaluation, and Design of Two-Phase and Case-Control Studies.

Sebastien Haneuse1, Takumi Saegusa, Thomas Lumley.   

Abstract

The two-phase design has recently received attention in the statistical literature as an extension to the traditional case-control study for settings where a predictor of interest is rare or subject to missclassification. Despite a thorough methodological treatment and the potential for substantial efficiency gains, the two-phase design has not been widely adopted. This may be due, in part, to a lack of general-purpose, readily-available software. The osDesign package for R provides a suite of functions for analyzing data from a two-phase and/or case-control design, as well as evaluating operating characteristics, including bias, efficiency and power. The evaluation is simulation-based, permitting flexible application of the package to a broad range of scientific settings. Using lung cancer mortality data from Ohio, the package is illustrated with a detailed case-study in which two statistical goals are considered: (i) the evaluation of small-sample operating characteristics for two-phase and case-control designs and (ii) the planning and design of a future two-phase study.

Entities:  

Year:  2011        PMID: 22545023      PMCID: PMC3337215          DOI: 10.18637/jss.v043.i11

Source DB:  PubMed          Journal:  J Stat Softw        ISSN: 1548-7660            Impact factor:   6.440


  11 in total

1.  Two-stage case-control studies: precision of parameter estimates and considerations in selecting sample size.

Authors:  James A Hanley; Ilona Csizmadi; Jean-Paul Collet
Journal:  Am J Epidemiol       Date:  2005-11-03       Impact factor: 4.897

2.  Optimal design and efficiency of two-phase case-control studies with error-prone and error-free exposure measures.

Authors:  R McNamee
Journal:  Biostatistics       Date:  2005-04-28       Impact factor: 5.899

3.  The Monte Carlo method.

Authors:  N METROPOLIS; S ULAM
Journal:  J Am Stat Assoc       Date:  1949-09       Impact factor: 5.033

4.  A planning tool for two-phase case-control studies.

Authors:  Walter Schill; Pascal Wild; Iris Pigeot
Journal:  Comput Methods Programs Biomed       Date:  2007-09-14       Impact factor: 5.428

5.  Two-stage sampling for etiologic studies. Sample size and power.

Authors:  D Schaubel; J Hanley; J P Collet; J F Bolvin; C Sharpe; H I Morrison; Y Mao
Journal:  Am J Epidemiol       Date:  1997-09-01       Impact factor: 4.897

6.  Design of two-phase prevalence surveys of rare disorders.

Authors:  P E Shrout; S C Newman
Journal:  Biometrics       Date:  1989-06       Impact factor: 2.571

7.  Optimal sampling strategies for two-stage studies.

Authors:  M Reilly
Journal:  Am J Epidemiol       Date:  1996-01-01       Impact factor: 4.897

8.  A two stage design for the study of the relationship between a rare exposure and a rare disease.

Authors:  J E White
Journal:  Am J Epidemiol       Date:  1982-01       Impact factor: 4.897

9.  On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses.

Authors:  Elizabeth Koehler; Elizabeth Brown; Sebastien J-P A Haneuse
Journal:  Am Stat       Date:  2009-05-01       Impact factor: 8.710

10.  Spatio-temporal models with errors in covariates: mapping Ohio lung cancer mortality.

Authors:  H Xia; B P Carlin
Journal:  Stat Med       Date:  1998-09-30       Impact factor: 2.373

View more
  11 in total

1.  A two-stage strategy to accommodate general patterns of confounding in the design of observational studies.

Authors:  Sebastien Haneuse; Jonathan Schildcrout; Daniel Gillen
Journal:  Biostatistics       Date:  2011-11-30       Impact factor: 5.899

2.  Power and sample size for multivariate logistic modeling of unmatched case-control studies.

Authors:  Mitchell H Gail; Sebastien Haneuse
Journal:  Stat Methods Med Res       Date:  2017-11-16       Impact factor: 3.021

3.  Novel two-phase sampling designs for studying binary outcomes.

Authors:  Le Wang; Matthew L Williams; Yong Chen; Jinbo Chen
Journal:  Biometrics       Date:  2019-11-14       Impact factor: 2.571

4.  Using the Whole Cohort in the Analysis of Case-Control Data: Application to the Women's Health Initiative.

Authors:  Norman E Breslow; Gustavo Amorim; Mary B Pettinger; Jacques Rossouw
Journal:  Stat Biosci       Date:  2013-11-01

5.  Power/sample size calculations for assessing correlates of risk in clinical efficacy trials.

Authors:  Peter B Gilbert; Holly E Janes; Yunda Huang
Journal:  Stat Med       Date:  2016-03-31       Impact factor: 2.373

6.  Evaluating Public Health Interventions: 3. The Two-Stage Design for Confounding Bias Reduction-Having Your Cake and Eating It Two.

Authors:  Donna Spiegelman; Claudia L Rivera-Rodriguez; Sebastien Haneuse
Journal:  Am J Public Health       Date:  2016-07       Impact factor: 9.308

7.  Analysis of Generalized Semiparametric Regression Models for Cumulative Incidence Functions with Missing Covariates.

Authors:  Unkyung Lee; Yanqing Sun; Thomas H Scheike; Peter B Gilbert
Journal:  Comput Stat Data Anal       Date:  2018-02-02       Impact factor: 1.681

8.  Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling.

Authors:  Rong Fu; Peter B Gilbert
Journal:  Lifetime Data Anal       Date:  2016-03-23       Impact factor: 1.588

9.  Strategies for monitoring and evaluation of resource-limited national antiretroviral therapy programs: the two-phase design.

Authors:  Sebastien Haneuse; Bethany Hedt-Gauthier; Frank Chimbwandira; Simon Makombe; Lyson Tenthani; Andreas Jahn
Journal:  BMC Med Res Methodol       Date:  2015-04-07       Impact factor: 4.615

Review 10.  A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.

Authors:  Jacobo Pardo-Seco; Jorge Amigo; Wenceslao González-Manteiga; Antonio Salas
Journal:  PLoS One       Date:  2013-09-27       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.