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Multiple-test procedures and smile plots

  • Roger Newson

    ()

    (King's College London)

  • The ALSPAC Study Team

    (University of Bristol)

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.

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Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 3 (2003)
Issue (Month): 2 (June)
Pages: 109-132

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Handle: RePEc:tsj:stataj:v:3:y:2003:i:2:p:109-132
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  1. Roger Newson, 2000. "A program for saving a model fit as a dataset," Stata Technical Bulletin, StataCorp LP, vol. 9(49).
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