Multiple test procedures and smile plots
AbstractScientists often have good reasons for wanting to calculate multiple confidence intervals and/or p-values, especially when scanning a genome. However, if we do this, then the probability of not observing at least one "significant" difference tends to fall, even if all null hypotheses are true. A skeptical public will rightly ask whether a difference is "significant" when considered as one of a large number of parameters estimated. This presentation demonstrates some solutions to this problem, using the unofficial Stata packages parmest and smileplot. The parmest package allows the calculation of Bonferroni-corrected or Sidak-corrected confidence intervals for multiple estimated parameters. The smileplot package contains two programs, multproc (which carries out multiple test procedures) and smileplot (which presents their results graphically by plotting the p-value on a reverse log scale on the vertical axis against the parameter estimate on the horizontal axis). A multiple test procedure takes, as input, a set of estimates and p-values, and rejects a subset (possibly empty) of the null hypotheses corresponding to these p-values. Multiple test procedures have traditionally controlled the family-wise error rate (FWER), typically enabling the user to be 95% confident that all the rejected null hypotheses are false, and that all the corresponding "discoveries" are real. The price of this confidence is that the power to detect a difference of a given size tends to zero as the number of measured parameters become large. Therefore, recent work has concentrated on procedures that control the false disco very rate (FDR), such as the Simes procedure and the Yekutieli-Benjamini procedure. FDR-controlling procedures attempt to control the number of false discoveries as a proportion of the number of true discoveries, typically enabling the user to be 95% confident that some of the discoveries are real, or 90% confident that most of the discoveries are real. This less stringent requirement causes power to "bottom out" at a non-zero level as the number of tests becomes large. The smileplot package offers a selection of multiple test procedures of both kinds.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2003 with number 16.
Date of creation: 16 Mar 2003
Date of revision:
Other versions of this item:
- NEP-ALL-2003-05-18 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Roger Newson, 2000. "A program for saving a model fit as a dataset," Stata Technical Bulletin, StataCorp LP, vol. 9(49).
- Fink, Günther & McConnell, Margaret & Vollmer, Sebastian, 2011. "Testing for Heterogeneous Treatment Effects in Experimental Data: False Discovery Risks and Correction Procedures," Diskussionspapiere der Wirtschaftswissenschaftlichen FakultÃ¤t der Leibniz UniversitÃ¤t Hannover dp-477, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Bell, Suzanne & Prata, Ndola & Lahiff, Maureen & Eskenazi, Brenda, 2012. "Civil unrest and birthweight: An exploratory analysis of the 2007/2008 Kenyan Crisis," Social Science & Medicine, Elsevier, vol. 74(9), pages 1324-1330.
- Roger Newson, 2008. "parmest and extensions," United Kingdom Stata Users' Group Meetings 2008 07, Stata Users Group.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum).
If references are entirely missing, you can add them using this form.