Significance levels for studies with correlated test statistics. Export

@article{citeulike:2460532, abstract = {When testing large numbers of null hypotheses, one needs to assess the evidence against the global null hypothesis that none of the hypotheses is false. Such evidence typically is based on the test statistic of the largest magnitude, whose statistical significance is evaluated by permuting the sample units to simulate its null distribution. Efron (2007) has noted that correlation among the test statistics can induce substantial interstudy variation in the shapes of their histograms, which may cause misleading tail counts. Here, we show that permutation-based estimates of the overall significance level also can be misleading when the test statistics are correlated. We propose that such estimates be conditioned on a simple measure of the spread of the observed histogram, and we provide a method for obtaining conditional significance levels. We justify this conditioning using the conditionality principle described by Cox and Hinkley (1974). Application of the method to gene expression data illustrates the circumstances when conditional significance levels are needed.}, address = {Department of Psychiatry and Behavioral Science, Stanford University School of Medicine, Stanford, CA 94305, USA.}, author = {Shi, Jianxin and Levinson, Douglas F F. and Whittemore, Alice S S.}, citeulike-article-id = {2460532}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/biostatistics/kxm047}, citeulike-linkout-1 = {http://biostatistics.oxfordjournals.org/cgi/content/abstract/9/3/458}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18089626}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18089626}, day = {18}, doi = {10.1093/biostatistics/kxm047}, issn = {1465-4644}, journal = {Biostatistics}, keywords = {mtp}, month = {December}, posted-at = {2008-06-18 18:42:13}, priority = {2}, title = {Significance levels for studies with correlated test statistics.}, url = {http://dx.doi.org/10.1093/biostatistics/kxm047}, year = {2007} }

TY - JOUR ID - citeulike:2460532 L3 - citeulike-article-id:2460532 N2 - When testing large numbers of null hypotheses, one needs to assess the evidence against the global null hypothesis that none of the hypotheses is false. Such evidence typically is based on the test statistic of the largest magnitude, whose statistical significance is evaluated by permuting the sample units to simulate its null distribution. Efron (2007) has noted that correlation among the test statistics can induce substantial interstudy variation in the shapes of their histograms, which may cause misleading tail counts. Here, we show that permutation-based estimates of the overall significance level also can be misleading when the test statistics are correlated. We propose that such estimates be conditioned on a simple measure of the spread of the observed histogram, and we provide a method for obtaining conditional significance levels. We justify this conditioning using the conditionality principle described by Cox and Hinkley (1974). Application of the method to gene expression data illustrates the circumstances when conditional significance levels are needed. JF - Biostatistics SN - 1465-4644 AD - Department of Psychiatry and Behavioral Science, Stanford University School of Medicine, Stanford, CA 94305, USA. TI - Significance levels for studies with correlated test statistics. KW - mtp AU - Shi, Jianxin AU - Levinson, Douglas F AU - Whittemore, Alice S PY - 2007/12/18/ UR - http://dx.doi.org/10.1093/biostatistics/kxm047 ER -