Literature DB >> 20173101

Transparency and reproducibility in data analysis: the Prostate Cancer Prevention Trial.

Stuart G Baker1, Amy K Darke, Paul Pinsky, Howard L Parnes, Barnett S Kramer.   

Abstract

With the analysis of complex, messy data sets, the statistics community has recently focused attention on "reproducible research," namely research that can be readily replicated by others. One standard that has been proposed is the availability of data sets and computer code. However, in some situations, raw data cannot be disseminated for reasons of confidentiality or because the data are so messy as to make dissemination impractical. For one such situation, we propose 2 steps for reproducible research: (i) presentation of a table of data and (ii) presentation of a formula to estimate key quantities from the table of data. We illustrate this strategy in the analysis of data from the Prostate Cancer Prevention Trial, which investigated the effect of the drug finasteride versus placebo on the period prevalence of prostate cancer. With such an important result at stake, a transparent analysis was important.

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Year:  2010        PMID: 20173101      PMCID: PMC2883301          DOI: 10.1093/biostatistics/kxq004

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  6 in total

1.  Simple adjustments for randomized trials with nonrandomly missing or censored outcomes arising from informative covariates.

Authors:  Stuart G Baker; Garrett M Fitzmaurice; Laurence S Freedman; Barnett S Kramer
Journal:  Biostatistics       Date:  2005-05-27       Impact factor: 5.899

2.  Reproducible research and Biostatistics.

Authors:  Roger D Peng
Journal:  Biostatistics       Date:  2009-07       Impact factor: 5.899

3.  Does Finasteride Affect the Severity of Prostate Cancer? A Causal Sensitivity Analysis.

Authors:  Bryan E Shepherd; Mary W Redman; Donna P Ankerst
Journal:  J Am Stat Assoc       Date:  2008-12-01       Impact factor: 5.033

4.  Finasteride does not increase the risk of high-grade prostate cancer: a bias-adjusted modeling approach.

Authors:  Mary W Redman; Catherine M Tangen; Phyllis J Goodman; M Scott Lucia; Charles A Coltman; Ian M Thompson
Journal:  Cancer Prev Res (Phila)       Date:  2008-05-18

5.  Estimating rates of true high-grade disease in the prostate cancer prevention trial.

Authors:  Paul Pinsky; Howard Parnes; Leslie Ford
Journal:  Cancer Prev Res (Phila)       Date:  2008-05-18

6.  The influence of finasteride on the development of prostate cancer.

Authors:  Ian M Thompson; Phyllis J Goodman; Catherine M Tangen; M Scott Lucia; Gary J Miller; Leslie G Ford; Michael M Lieber; R Duane Cespedes; James N Atkins; Scott M Lippman; Susie M Carlin; Anne Ryan; Connie M Szczepanek; John J Crowley; Charles A Coltman
Journal:  N Engl J Med       Date:  2003-06-24       Impact factor: 91.245

  6 in total
  4 in total

1.  Maximum likelihood estimation with missing outcomes: From simplicity to complexity.

Authors:  Stuart G Baker
Journal:  Stat Med       Date:  2019-08-08       Impact factor: 2.373

Review 2.  A review of reproducible and transparent research practices in urology publications from 2014 to2018.

Authors:  Shelby Rauh; Bradley S Johnson; Aaron Bowers; Daniel Tritz; Benjamin Matthew Vassar
Journal:  BMC Urol       Date:  2022-07-11       Impact factor: 2.090

3.  How to interpret a small increase in AUC with an additional risk prediction marker: decision analysis comes through.

Authors:  Stuart G Baker; Ewoud Schuit; Ewout W Steyerberg; Michael J Pencina; Andrew Vickers; Andew Vickers; Karel G M Moons; Ben W J Mol; Karen S Lindeman
Journal:  Stat Med       Date:  2014-05-13       Impact factor: 2.373

4.  Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.

Authors:  Daniel Garijo; Sarah Kinnings; Li Xie; Lei Xie; Yinliang Zhang; Philip E Bourne; Yolanda Gil
Journal:  PLoS One       Date:  2013-11-27       Impact factor: 3.240

  4 in total

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