Doubly robust conditional logistic regression.
Sign Up to like & getrecommendations! Published in 2019 at "Statistics in medicine"
DOI: 10.1002/sim.8332
Abstract: Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control… read more here.
Keywords: conditional logistic; logistic regression; robust conditional; odds ratio ... See more keywords
Doubly robust estimation of the causal effects in the causal inference with missing outcome data
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-018-0957-2
Abstract: The goal of this article is to attempt to develop doubly robust (DR) estimator in the causal inference with ignorable missing outcome data. In the causal inference with missing outcome data, an estimator is doubly… read more here.
Keywords: outcome data; estimator; causal inference; missing outcome ... See more keywords
Doubly robust difference-in-differences estimators
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2020.06.003
Abstract: Abstract This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but… read more here.
Keywords: differences estimators; proposed estimators; difference differences; doubly robust ... See more keywords
Doubly robust kernel density estimation when group membership is missing at random
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Statistical Planning and Inference"
DOI: 10.1016/j.jspi.2019.09.010
Abstract: Abstract When there are subjects with subpopulation memberships missing, the kernel density estimates of the subpopulations based on the subjects with verified memberships may not be valid unless the missingness of the memberships satisfies the… read more here.
Keywords: missing random; prediction model; kernel density; doubly robust ... See more keywords
Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Evaluation in Clinical Practice"
DOI: 10.1111/jep.12714
Abstract: RATIONALE, AIMS AND OBJECTIVES When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall… read more here.
Keywords: treatment; estimator; model; propensity score ... See more keywords
A doubly robust approach for cost–effectiveness estimation from observational data
Sign Up to like & getrecommendations! Published in 2018 at "Statistical Methods in Medical Research"
DOI: 10.1177/0962280217693262
Abstract: Estimation of common cost–effectiveness measures, including the incremental cost–effectiveness ratio and the net monetary benefit, is complicated by the need to account for informative censoring and inherent skewness of the data. In addition, since the… read more here.
Keywords: approach cost; cost; robust approach; cost effectiveness ... See more keywords
Doubly robust pseudo-likelihood for incomplete hierarchical binary data
Sign Up to like & getrecommendations! Published in 2018 at "Statistical Modelling"
DOI: 10.1177/1471082x18808611
Abstract: Missing data is almost inevitable in correlated-data studies. For non-Gaussian outcomes with moderate to large sequences, direct-likelihood methods can involve complex, hard-to-manipulate likelihoods. Popular alternative approaches, like generalized estimating equations, that are frequently used to… read more here.
Keywords: hierarchical binary; robust pseudo; pseudo likelihood; binary data ... See more keywords