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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedSun B, Perkins NJ, Cole SR
AHRQ Author: Mitchell EM
Inverse-probability-weighted estimation for monotone and nonmonotone missing data.
The goal of this study was to examine the issue of missing data in epidemiologic research by estimating the association of maternal smoking behavior with spontaneous abortion. Three data sets with induced missing values from the Collaborative Perinatal Project are provided in the article as examples of prototypical epidemiologic studies with missing data. The article also describes a proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random that can be used in constructing the weights in inverse probability weighting complete-case estimation.
AHRQ-authored.
Citation: Sun B, Perkins NJ, Cole SR .
Inverse-probability-weighted estimation for monotone and nonmonotone missing data.
Am J Epidemiol 2018 Mar;187(3):585-91. doi: 10.1093/aje/kwx350..
Keywords: Data, Health Services Research (HSR), Pregnancy, Research Methodologies
Margulis AV, Palmsten K, Andrade SE
Beginning and duration of pregnancy in automated health care databases: review of estimation methods and validation results.
The authors describe methods reported in the literature to estimate the beginning or duration of pregnancy in automated health care data, and to present results of validation exercises where available. They concluded that a number of methods of varying complexity are available to estimate them. Validation studies have been performed for many of them and can serve as a guide for method selection for a particular study.
AHRQ-funded; HS018533.
Citation: Margulis AV, Palmsten K, Andrade SE .
Beginning and duration of pregnancy in automated health care databases: review of estimation methods and validation results.
Pharmacoepidemiol Drug Saf 2015 Apr;24(4):335-42. doi: 10.1002/pds.3743..
Keywords: Pregnancy, Comparative Effectiveness, Data