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10.1093/aje/kwx348 http://scihub22266oqcxt.onion/10.1093/aje/kwx348 C5860376!5860376!29165572     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Am+J+Epidemiol 2018 ; 187 (3): 568-75 Nephropedia Template TP {{tp|p=29165572|t=2018. Principled Approaches to Missing Data in Epidemiologic Studies |pdf=|usr=}}{{29165572}} gab.com Text Principled Approaches to Missing Data in Epidemiologic Studies JO: Am J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=29165572 #FOAvip Principled methods with which to appropriately analyze missing data have long existed; however, broad implementation of these methods remains challenging. In this and 2 companion papers (Am J Epidemiol. 2018;187(3):576?584 and Am J Epidemiol. 2018;187(3):585?591), we discuss issues pertaining to missing data in the epidemiologic literature. We provide details regarding missing-data mechanisms and nomenclature and encourage the conduct of principled analyses through a detailed comparison of multiple imputation and inverse probability weighting. Data from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are used to create a masked data-analytical challenge with missing data induced by known mechanisms. We illustrate the deleterious effects of missing data with naive methods and show how principled methods can sometimes mitigate such effects. For example, when data were missing at random, naive methods showed a spurious protective effect of smoking on the risk of spontaneous abortion (odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.19, 0.93), while implementation of principled methods multiple imputation (OR = 1.30, 95% CI: 0.95, 1.77) or augmented inverse probability weighting (OR = 1.40, 95% CI: 1.00, 1.97) provided estimates closer to the ?true? full-data effect (OR = 1.31, 95% CI: 1.05, 1.64). We call for greater acknowledgement of and attention to missing data and for the broad use of principled missing-data methods in epidemiologic research. Twit Text FOAVip Principled Approaches to Missing Data in Epidemiologic Studies ????Am J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=29165572 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29165572 #FOAvip English Wikipedia <ref>{{Cite pmid|29165572}}</ref> Principled Approaches to Missing Data in Epidemiologic Studies #MMPMID29165572 Perkins NJ; Cole SR; Harel O; Tchetgen Tchetgen EJ; Sun B; Mitchell EM; Schisterman EF Am J Epidemiol 2018[Mar]; 187 (3): 568-75 PMID29165572show ga Principled methods with which to appropriately analyze missing data have long existed; however, broad implementation of these methods remains challenging. In this and 2 companion papers (Am J Epidemiol. 2018;187(3):576?584 and Am J Epidemiol. 2018;187(3):585?591), we discuss issues pertaining to missing data in the epidemiologic literature. We provide details regarding missing-data mechanisms and nomenclature and encourage the conduct of principled analyses through a detailed comparison of multiple imputation and inverse probability weighting. Data from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are used to create a masked data-analytical challenge with missing data induced by known mechanisms. We illustrate the deleterious effects of missing data with naive methods and show how principled methods can sometimes mitigate such effects. For example, when data were missing at random, naive methods showed a spurious protective effect of smoking on the risk of spontaneous abortion (odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.19, 0.93), while implementation of principled methods multiple imputation (OR = 1.30, 95% CI: 0.95, 1.77) or augmented inverse probability weighting (OR = 1.40, 95% CI: 1.00, 1.97) provided estimates closer to the ?true? full-data effect (OR = 1.31, 95% CI: 1.05, 1.64). We call for greater acknowledgement of and attention to missing data and for the broad use of principled missing-data methods in epidemiologic research. äPrincipled Approaches to Missing Data in Epidemiologic Studies (epmc).pdf DeepDyve Pubget Overpricing