Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data
(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to d...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | International Journal of Environmental Research and Public Health |
Subjects: | |
Online Access: | https://www.mdpi.com/1660-4601/18/13/6694 |
id |
doaj-6643d8e7c8874c82a67b2ab657904136 |
---|---|
record_format |
Article |
spelling |
doaj-6643d8e7c8874c82a67b2ab6579041362021-07-15T15:34:02ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012021-06-01186694669410.3390/ijerph18136694Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing DataDaniele Bottigliengo0Giulia Lorenzoni1Honoria Ocagli2Matteo Martinato3Paola Berchialla4Dario Gregori5Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, ItalyDepartment of Clinical and Biological Sciences, University of Torino, 10124 Torino, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective registry for the treatment of unprotected left main coronary artery disease. The performances were assessed in terms of the overall balance of baseline covariates. (3) Results: Methods that explicitly deal with missing data were superior to classical complete case analysis. The best balance was observed when propensity scores were estimated with a method that accounts for missing data using a stochastic approximation of the expectation-maximization algorithm. (4) Conclusions: If missing at random mechanism is plausible, methods that use missing data to estimate propensity score or impute them should be preferred. Sensitivity analyses are encouraged to evaluate the implications methods used to handle missing data and estimate propensity score.https://www.mdpi.com/1660-4601/18/13/6694propensity scoremissing datanon-interventional studies |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniele Bottigliengo Giulia Lorenzoni Honoria Ocagli Matteo Martinato Paola Berchialla Dario Gregori |
spellingShingle |
Daniele Bottigliengo Giulia Lorenzoni Honoria Ocagli Matteo Martinato Paola Berchialla Dario Gregori Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data International Journal of Environmental Research and Public Health propensity score missing data non-interventional studies |
author_facet |
Daniele Bottigliengo Giulia Lorenzoni Honoria Ocagli Matteo Martinato Paola Berchialla Dario Gregori |
author_sort |
Daniele Bottigliengo |
title |
Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data |
title_short |
Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data |
title_full |
Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data |
title_fullStr |
Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data |
title_full_unstemmed |
Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data |
title_sort |
propensity score analysis with partially observed baseline covariates: a practical comparison of methods for handling missing data |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1661-7827 1660-4601 |
publishDate |
2021-06-01 |
description |
(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective registry for the treatment of unprotected left main coronary artery disease. The performances were assessed in terms of the overall balance of baseline covariates. (3) Results: Methods that explicitly deal with missing data were superior to classical complete case analysis. The best balance was observed when propensity scores were estimated with a method that accounts for missing data using a stochastic approximation of the expectation-maximization algorithm. (4) Conclusions: If missing at random mechanism is plausible, methods that use missing data to estimate propensity score or impute them should be preferred. Sensitivity analyses are encouraged to evaluate the implications methods used to handle missing data and estimate propensity score. |
topic |
propensity score missing data non-interventional studies |
url |
https://www.mdpi.com/1660-4601/18/13/6694 |
work_keys_str_mv |
AT danielebottigliengo propensityscoreanalysiswithpartiallyobservedbaselinecovariatesapracticalcomparisonofmethodsforhandlingmissingdata AT giulialorenzoni propensityscoreanalysiswithpartiallyobservedbaselinecovariatesapracticalcomparisonofmethodsforhandlingmissingdata AT honoriaocagli propensityscoreanalysiswithpartiallyobservedbaselinecovariatesapracticalcomparisonofmethodsforhandlingmissingdata AT matteomartinato propensityscoreanalysiswithpartiallyobservedbaselinecovariatesapracticalcomparisonofmethodsforhandlingmissingdata AT paolaberchialla propensityscoreanalysiswithpartiallyobservedbaselinecovariatesapracticalcomparisonofmethodsforhandlingmissingdata AT dariogregori propensityscoreanalysiswithpartiallyobservedbaselinecovariatesapracticalcomparisonofmethodsforhandlingmissingdata |
_version_ |
1721299673096912896 |