A review of the use of propensity score diagnostics in papers published in high-ranking medical journals

Abstract Background Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The...

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Main Authors: Emily Granger, Tim Watkins, Jamie C. Sergeant, Mark Lunt
Format: Article
Language:English
Published: BMC 2020-05-01
Series:BMC Medical Research Methodology
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12874-020-00994-0
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spelling doaj-356c94e7ec7241b39a6e376b1ba1cbdf2020-11-25T02:15:38ZengBMCBMC Medical Research Methodology1471-22882020-05-012011910.1186/s12874-020-00994-0A review of the use of propensity score diagnostics in papers published in high-ranking medical journalsEmily Granger0Tim Watkins1Jamie C. Sergeant2Mark Lunt3Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterDepartment of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South WalesCentre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterCentre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterAbstract Background Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The current use of propensity score diagnostics in the medical literature is unknown. The objectives of this study are to (1) assess the use of propensity score diagnostics in medical studies published in high-ranking journals, and (2) assess whether the use of propensity score diagnostics differs between studies (a) in different research areas and (b) using different propensity score methods. Methods A PubMed search identified studies published in high-impact journals between Jan 1st 2014 and Dec 31st 2016 using propensity scores to answer an applied medical question. From each study we extracted information regarding how propensity scores were assessed and which propensity score method was used. Research area was defined using the journal categories from the Journal Citations Report. Results A total of 894 papers were included in the review. Of these, 187 (20.9%) failed to report whether the propensity score had been assessed. Commonly reported diagnostics were p-values from hypothesis tests (36.6%) and the standardised mean difference (34.6%). Statistical tests provided marginally stronger evidence for a difference in diagnostic use between studies in different research areas (p = 0.033) than studies using different propensity score methods (p = 0.061). Conclusions The use of diagnostics in the propensity score medical literature is far from optimal, with different diagnostics preferred in different areas of medicine. The propensity score literature may improve with focused efforts to change practice in areas where suboptimal practice is most common.http://link.springer.com/article/10.1186/s12874-020-00994-0Covariate balanceConfoundingPropensity scoresDiagnosticsEpidemiology
collection DOAJ
language English
format Article
sources DOAJ
author Emily Granger
Tim Watkins
Jamie C. Sergeant
Mark Lunt
spellingShingle Emily Granger
Tim Watkins
Jamie C. Sergeant
Mark Lunt
A review of the use of propensity score diagnostics in papers published in high-ranking medical journals
BMC Medical Research Methodology
Covariate balance
Confounding
Propensity scores
Diagnostics
Epidemiology
author_facet Emily Granger
Tim Watkins
Jamie C. Sergeant
Mark Lunt
author_sort Emily Granger
title A review of the use of propensity score diagnostics in papers published in high-ranking medical journals
title_short A review of the use of propensity score diagnostics in papers published in high-ranking medical journals
title_full A review of the use of propensity score diagnostics in papers published in high-ranking medical journals
title_fullStr A review of the use of propensity score diagnostics in papers published in high-ranking medical journals
title_full_unstemmed A review of the use of propensity score diagnostics in papers published in high-ranking medical journals
title_sort review of the use of propensity score diagnostics in papers published in high-ranking medical journals
publisher BMC
series BMC Medical Research Methodology
issn 1471-2288
publishDate 2020-05-01
description Abstract Background Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The current use of propensity score diagnostics in the medical literature is unknown. The objectives of this study are to (1) assess the use of propensity score diagnostics in medical studies published in high-ranking journals, and (2) assess whether the use of propensity score diagnostics differs between studies (a) in different research areas and (b) using different propensity score methods. Methods A PubMed search identified studies published in high-impact journals between Jan 1st 2014 and Dec 31st 2016 using propensity scores to answer an applied medical question. From each study we extracted information regarding how propensity scores were assessed and which propensity score method was used. Research area was defined using the journal categories from the Journal Citations Report. Results A total of 894 papers were included in the review. Of these, 187 (20.9%) failed to report whether the propensity score had been assessed. Commonly reported diagnostics were p-values from hypothesis tests (36.6%) and the standardised mean difference (34.6%). Statistical tests provided marginally stronger evidence for a difference in diagnostic use between studies in different research areas (p = 0.033) than studies using different propensity score methods (p = 0.061). Conclusions The use of diagnostics in the propensity score medical literature is far from optimal, with different diagnostics preferred in different areas of medicine. The propensity score literature may improve with focused efforts to change practice in areas where suboptimal practice is most common.
topic Covariate balance
Confounding
Propensity scores
Diagnostics
Epidemiology
url http://link.springer.com/article/10.1186/s12874-020-00994-0
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