Simulation driven inferences for multiply imputed longitudinal datasets*

Demirtas, Hakan

doi:10.1111/j.1467-9574.2004.00271.x

Cited by 74Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Simulation driven inferences for multiply imputed longitudinal datasets*

Demirtas, Hakan·2004·Statistica Neerlandica

DOI 10.1111/j.1467-9574.2004.00271.x

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Abstract

In this article, we demonstrate by simulations that rich imputation models for incomplete longitudinal datasets produce more calibrated estimates in terms of reduced bias and higher coverage rates without duly deflating the efficiency. We argue that the use of supplementary variables that are thought to be potential causes or correlates of missingness or outcomes in the imputation process may lead to better inferential results in comparison to simpler imputation models. The liberal use of these variables is recommended as opposed to the conservative strategy.

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Additional file 11 of A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis
Jahangiri, M., Kazemnejad, A., Goldfeld, K. S., Daneshpour, M. S., Mostafaei, S., Khalili, D., Moghadas, M. R., & Akbarzadeh, M. (2023) · Figshare · doi:10.6084/m9.figshare.23640054
A multiple imputation strategy for incomplete longitudinal data
Landrum, M. B., & Becker, M. P. (2001) · Statistics in Medicine · doi:10.1002/sim.740 · Cited by 26 · see citing works
Additional file 12 of A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis
Jahangiri, M., Kazemnejad, A., Goldfeld, K. S., Daneshpour, M. S., Mostafaei, S., Khalili, D., Moghadas, M. R., & Akbarzadeh, M. (2023) · Figshare · doi:10.6084/m9.figshare.23640057
Additional file 13 of A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis
Jahangiri, M., Kazemnejad, A., Goldfeld, K. S., Daneshpour, M. S., Mostafaei, S., Khalili, D., Moghadas, M. R., & Akbarzadeh, M. (2023) · Figshare · doi:10.6084/m9.figshare.23640060
Additional file 14 of A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis
Jahangiri, M., Kazemnejad, A., Goldfeld, K. S., Daneshpour, M. S., Mostafaei, S., Khalili, D., Moghadas, M. R., & Akbarzadeh, M. (2023) · Figshare · doi:10.6084/m9.figshare.23640063

Authors: Demirtas, H.
Venue: Statistica Neerlandica
Publisher: Wiley
Published: 2004-11
Volume: 58
Issue: 4
Pages: 466-482
ISSN: 0039-0402 (ISSN-L)
1467-9574
DOI: 10.1111/j.1467-9574.2004.00271.x
OpenAlex: W1993323182
Cited by: 74
References: 13
Citations by year: 2026: 2 · 2025: 1 · 2024: 2 · 2022: 1 · 2021: 3 · 2019: 1 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 3 · 2014: 12 · 2013: 3 · 2012: 6
FWCI: 3.05
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 35 d)
Locations: Statistica Neerlandica (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: multiple imputation, longitudinal data, missing data, imputation models, supplementary variables, bias mitigation, coverage rate, efficiency, simulation study

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Simulation driven inferences for multiply imputed longitudinal datasets*

Abstract

Integrity signals

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