Causal inference in survival analysis under deterministic missingness of confounders in register data
Sign Up to like & getrecommendations! Published in 2023 at "Statistics in Medicine"
DOI: 10.1002/sim.9706
Abstract: Long‐term register data offer unique opportunities to explore causal effects of treatments on time‐to‐event outcomes, in well‐characterized populations with minimum loss of follow‐up. However, the structure of the data may pose methodological challenges. Motivated by… read more here.
Keywords: causal inference; missingness; survival; entry date ... See more keywords
Authors’ Reply: A comparison of different methods to handle missing data in the context of propensity score analysis
Sign Up to like & getrecommendations! Published in 2019 at "European Journal of Epidemiology"
DOI: 10.1007/s10654-019-00553-y
Abstract: In this Reply we will discuss the reason why we left out a scenario where missingness is dependent of the outcome and show that our simulation results are consistent when there is a non-null treatment… read more here.
Keywords: authors reply; analysis; reply comparison; complete case ... See more keywords
Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data
Sign Up to like & getrecommendations! Published in 2022 at "Epidemiology"
DOI: 10.1097/ede.0000000000001578
Abstract: Background: Missing data are common in studies using electronic health records (EHRs)-derived data. Missingness in EHR data is related to healthcare utilization patterns, resulting in complex and potentially missing not at random missingness mechanisms. Prior… read more here.
Keywords: missingness; random; denoising autoencoders; electronic health ... See more keywords
Implications of systematic nominator missingness for peer nomination data
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Behavioral Development"
DOI: 10.1177/0165025416664431
Abstract: Missing data are a persistent problem in psychological research. Peer nomination data present a unique missing data problem, because a nominator’s nonparticipation results in missing data for other individuals in the study. This study examined… read more here.
Keywords: nomination data; nominator missingness; nomination; peer nomination ... See more keywords
Multiple imputation with missing data indicators.
Sign Up to like & getrecommendations! Published in 2021 at "Statistical methods in medical research"
DOI: 10.1177/09622802211047346
Abstract: Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we… read more here.
Keywords: imputation; multiple imputation; regression multiple; sequential regression ... See more keywords
Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure
Sign Up to like & getrecommendations! Published in 2020 at "BMC Medical Research Methodology"
DOI: 10.1186/s12874-020-01053-4
Abstract: Background Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values… read more here.
Keywords: estimation; imputation; propensity score; propensity ... See more keywords
Reading Profiles in Multi-Site Data With Missingness
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DOI: 10.3389/fpsyg.2018.00644
Abstract: Children with reading disability exhibit varied deficits in reading and cognitive abilities that contribute to their reading comprehension problems. Some children exhibit primary deficits in phonological processing, while others can exhibit deficits in oral language… read more here.
Keywords: multi site; reading profiles; reading; missing data ... See more keywords