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

被引：4

作者：

Baker, Stuart G. ^{[1
]}

Darke, Amy K. ^{[2
]}

Pinsky, Paul ^{[3
]}

Parnes, Howard L. ^{[4
]}

Kramer, Barnett S. ^{[5
]}

机构：

[1] NCI, Biometry Res Grp, Canc Prevent Div, Bethesda, MD 20892 USA

[2] Fred Hutchinson Canc Res Ctr, Ctr Stat, Seattle, WA 98109 USA

[3] NCI, Early Detect Res Grp, Canc Prevent Div, Bethesda, MD 20892 USA

[4] NCI, Prostate & Urol Canc Res Grp, Canc Prevent Div, Bethesda, MD 20892 USA

[5] NIH, Off Dis Prevent, Bethesda, MD 20892 USA

来源：

BIOSTATISTICS | 2010年 / 11卷 / 03期

基金：

美国国家卫生研究院;

关键词：

Categorical data; Maximum likelihood; Missing data; Multinomial-Poisson transformation; Propensity-to-be-missing score; Randomized trials; FINASTERIDE;

D O I：

10.1093/biostatistics/kxq004

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：413 / 418

页数：6

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