Multiple-test procedures and smile plots

Roger Newson and The ALSPAC Study Team
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The ALSPAC Study Team: University of Bristol

Stata Journal, 2003, vol. 3, issue 2, 109-132

Abstract: multproc carries out multiple-test procedures, taking as input a list of p-values and an uncorrected critical p-value, and calculating a corrected overall critical pvalue for rejection of null hypotheses. These procedures define a conÞdence region for a set-valued parameter, namely the set of null hypotheses that are true. They aim to control either the family-wise error rate (FWER) or the false discovery rate (FDR) at a level no greater than the uncorrected critical p-value. smileplot calls multproc and then creates a smile plot, with data points corresponding to estimated parameters, the p-values (on a reverse log scale) on the y-axis, and the parameter estimates (or another variable) on the x-axis. There are y-axis reference lines at the uncorrected and corrected overall critical p-values. The reference line for the corrected overall critical p-value, known as the parapet line, is an informal Òupper confidence limitÓ for the set of null hypotheses that are true and defines a boundary between data mining and data dredging. A smile plot summarizes a set of multiple analyses just as a Cochrane forest plot summarizes a meta-analysis. Copyright 2003 by Stata Corporation.

Keywords: smile plot; multiple-test procedure; closed testing procedure; data mining; family-wise error rate; false discovery rate; Bonferroni; Sidak; Holm; Holland; Copenhaver; Hochberg; Rom; Simes; Benjamini; Yekutieli; Krieger; Liu (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (17)

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Working Paper: Multiple test procedures and smile plots (2003)
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