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Tytuł pozycji:

Reflection on modern methods: combining weights for confounding and missing data.

Tytuł:
Reflection on modern methods: combining weights for confounding and missing data.
Autorzy:
Ross RK; Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA.
Breskin A; Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA.; NoviSci Inc., Durham, NC, USA and.
Breger TL; Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA.; Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Westreich D; Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA.
Źródło:
International journal of epidemiology [Int J Epidemiol] 2022 May 09; Vol. 51 (2), pp. 679-684.
Typ publikacji:
Journal Article; Research Support, N.I.H., Extramural
Język:
English
Imprint Name(s):
Original Publication: [London] Oxford University Press.
MeSH Terms:
Models, Statistical*
Bias ; Computer Simulation ; Humans ; Probability
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Grant Information:
R01 AG056479 United States AG NIA NIH HHS; T32 HD052468 United States HD NICHD NIH HHS
Contributed Indexing:
Keywords: Inverse probability weights; confounding; missing data
Entry Date(s):
Date Created: 20210918 Date Completed: 20220511 Latest Revision: 20220919
Update Code:
20240105
PubMed Central ID:
PMC9082798
DOI:
10.1093/ije/dyab205
PMID:
34536004
Czasopismo naukowe
Inverse probability weights are increasingly used in epidemiological analysis, and estimation and application of weights to address a single bias are well discussed in the literature. Weights to address multiple biases simultaneously (i.e. a combination of weights) have almost exclusively been discussed related to marginal structural models in longitudinal settings where treatment weights (estimated first) are combined with censoring weights (estimated second). In this work, we examine two examples of combined weights for confounding and missingness in a time-fixed setting in which outcome or confounder data are missing, and the estimand is the marginal expectation of the outcome under a time-fixed treatment. We discuss the identification conditions, construction of combined weights and how assumptions of the missing data mechanisms affect this construction. We use a simulation to illustrate the estimation and application of the weights in the two examples. Notably, when only outcome data are missing, construction of combined weights is straightforward; however, when confounder data are missing, we show that in general we must follow a specific estimation procedure which entails first estimating missingness weights and then estimating treatment probabilities from data with missingness weights applied. However, if treatment and missingness are conditionally independent, then treatment probabilities can be estimated among the complete cases.
(© The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

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