BACKGROUND: Nonreported and selectively reported information and the use of different definitions may introduce biases in the literature of prognostic factors. We probed these biases in a meta-analysis of a prognostic factor for head and neck squamous cell cancer (HNSCC) mortality that has drawn wide attention--the status of the tumor suppressor protein TP53. METHODS: We compared results of meta-analyses that included published data plus unpublished data retrieved from investigators; published data; and only published data indexed with "survival" or "mortality" in MEDLINE/EMBASE, with or without standardized definitions. We also evaluated whether previously published meta-analyses on mortality predictors for various malignancies addressed issues of retrieval and standardized information. All statistical tests were two-sided. RESULTS: For the 18 studies with 1364 patients that included published and indexed data, we obtained a highly statistically significant association between TP53 status and mortality. When we used the definitions preferred by each publication, the association was stronger (risk ratio [RR] = 1.38, 95% confidence interval [CI] = 1.13 to 1.67; P = .001) than when we standardized definitions (RR = 1.27, 95% CI = 1.06 to 1.53; P = .011). The addition of 13 studies with 1028 subjects that included published but not indexed data reduced the observed association (RR = 1.23, 95% CI = 1.03 to 1.47; P = .02). Finally, when we obtained data from investigators (11 studies with 996 patients) and analyzed it with all other data, statistical significance was lost (RR = 1.16, 95% CI = 0.99 to 1.35; P = .06). Among 18 published meta-analyses of 37 cancer prognostic factors, 13 (72%) did not use standardized definitions and 16 (89%) did not retrieve additional information. CONCLUSIONS: Selective reporting may spuriously inflate the importance of postulated prognostic factors for various malignancies. We recommend that meta-analyses thereof should maximize retrieval of information and standardize definitions.
BACKGROUND: Nonreported and selectively reported information and the use of different definitions may introduce biases in the literature of prognostic factors. We probed these biases in a meta-analysis of a prognostic factor for head and neck squamous cell cancer (HNSCC) mortality that has drawn wide attention--the status of the tumor suppressor protein TP53. METHODS: We compared results of meta-analyses that included published data plus unpublished data retrieved from investigators; published data; and only published data indexed with "survival" or "mortality" in MEDLINE/EMBASE, with or without standardized definitions. We also evaluated whether previously published meta-analyses on mortality predictors for various malignancies addressed issues of retrieval and standardized information. All statistical tests were two-sided. RESULTS: For the 18 studies with 1364 patients that included published and indexed data, we obtained a highly statistically significant association between TP53 status and mortality. When we used the definitions preferred by each publication, the association was stronger (risk ratio [RR] = 1.38, 95% confidence interval [CI] = 1.13 to 1.67; P = .001) than when we standardized definitions (RR = 1.27, 95% CI = 1.06 to 1.53; P = .011). The addition of 13 studies with 1028 subjects that included published but not indexed data reduced the observed association (RR = 1.23, 95% CI = 1.03 to 1.47; P = .02). Finally, when we obtained data from investigators (11 studies with 996 patients) and analyzed it with all other data, statistical significance was lost (RR = 1.16, 95% CI = 0.99 to 1.35; P = .06). Among 18 published meta-analyses of 37 cancer prognostic factors, 13 (72%) did not use standardized definitions and 16 (89%) did not retrieve additional information. CONCLUSIONS: Selective reporting may spuriously inflate the importance of postulated prognostic factors for various malignancies. We recommend that meta-analyses thereof should maximize retrieval of information and standardize definitions.
Authors: Valentina Gallo; Matthias Egger; Valerie McCormack; Peter B Farmer; John P A Ioannidis; Micheline Kirsch-Volders; Giuseppe Matullo; David H Phillips; Bernadette Schoket; Ulf Stromberg; Roel Vermeulen; Christopher Wild; Miquel Porta; Paolo Vineis Journal: Eur J Epidemiol Date: 2011-10-29 Impact factor: 8.082
Authors: A Cecile Jw Janssens; John Pa Ioannidis; Cornelia M van Duijn; Julian Little; Muin J Khoury Journal: Genome Med Date: 2011-03-15 Impact factor: 11.117
Authors: Reyna L VanGilder; Danielle M Davidov; Kyle R Stinehart; Jason D Huber; Ryan C Turner; Karen S Wilson; Eric Haney; Stephen M Davis; Paul D Chantler; Laurie Theeke; Charles L Rosen; Todd J Crocco; Laurie Gutmann; Taura L Barr Journal: J Clin Neurosci Date: 2013-08-23 Impact factor: 1.961