{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cole SR"],"funding":["National Institute of Allergy and Infectious Diseases","NIAID NIH HHS"],"pagination":["1643-1651"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8484780"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["190(8)"],"pubmed_abstract":["We use simple examples to show how the bias and standard error of an estimator depend in part on the type of estimator chosen from among parametric, nonparametric, and semiparametric candidates. We estimated the cumulative distribution function in the presence of missing data with and without an auxiliary variable. Simulation results mirrored theoretical expectations about the bias and precision of candidate estimators. Specifically, parametric maximum likelihood estimators performed best but must be \"omnisciently\" correctly specified. An augmented inverse probability-weighted (IPW) semiparametric estimator performed best among candidate estimators that were not omnisciently correct. In one setting, the augmented IPW estimator reduced the standard error by nearly 30%, compared with a stand"],"journal":["American journal of epidemiology"],"pubmed_title":["Comparing Parametric, Nonparametric, and Semiparametric Estimators: The Weibull Trials."],"pmcid":["PMC8484780"],"funding_grant_id":["P30 AI050410","K01 AI125087","P30AI50410","R01 AI157758","R01AI157758","K01AI125087"],"pubmed_authors":["Cole SR","Breskin A","Hudgens MG","Edwards JK"],"additional_accession":[]},"is_claimable":false,"name":"Comparing Parametric, Nonparametric, and Semiparametric Estimators: The Weibull Trials.","description":"We use simple examples to show how the bias and standard error of an estimator depend in part on the type of estimator chosen from among parametric, nonparametric, and semiparametric candidates. We estimated the cumulative distribution function in the presence of missing data with and without an auxiliary variable. Simulation results mirrored theoretical expectations about the bias and precision of candidate estimators. Specifically, parametric maximum likelihood estimators performed best but must be \"omnisciently\" correctly specified. An augmented inverse probability-weighted (IPW) semiparametric estimator performed best among candidate estimators that were not omnisciently correct. In one setting, the augmented IPW estimator reduced the standard error by nearly 30%, compared with a stand","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Aug","modification":"2025-04-04T19:57:45.236Z","creation":"2025-04-04T19:57:45.236Z"},"accession":"S-EPMC8484780","cross_references":{"pubmed":["33569578"],"doi":["10.1093/aje/kwab024"]}}