A methodological review of the high-dimensional propensity score in comparative-effectiveness and safety-of-interventions research finds incomplete reporting relative to algorithm development and robustness

Guillaume Louis Martin; Camille Petri; Julian Rozenberg; Noémie Simon; David Hajage; Julien Kirchgesner; Florence Tubach; Louis Létinier; Agnès Dechartres

doi:10.1016/j.jclinepi.2024.111305

A methodological review of the high-dimensional propensity score in comparative-effectiveness and safety-of-interventions research finds incomplete reporting relative to algorithm development and robustness

J Clin Epidemiol. 2024 Feb 28:169:111305. doi: 10.1016/j.jclinepi.2024.111305. Online ahead of print.

Authors

Guillaume Louis Martin¹, Camille Petri², Julian Rozenberg³, Noémie Simon⁴, David Hajage⁴, Julien Kirchgesner⁵, Florence Tubach⁴, Louis Létinier⁶, Agnès Dechartres⁴

Affiliations

¹ Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, Paris, France; Synapse Medicine, Bordeaux, France. Electronic address: guillaume.martin.md@gmail.com.
² UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK.
³ Sorbonne Université, AP-HP, Paris, France.
⁴ Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, Paris, France.
⁵ Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Saint-Antoine, Département de Gastroentérologie et Nutrition, Paris, France.
⁶ Synapse Medicine, Bordeaux, France.

PMID: 38417583
DOI: 10.1016/j.jclinepi.2024.111305

Abstract

Objectives: The use of secondary databases has become popular for evaluating the effectiveness and safety of interventions in real-life settings. However, the absence of important confounders in these databases is challenging. To address this issue, the high-dimensional propensity score (hdPS) algorithm was developed in 2009. This algorithm uses proxy variables for mitigating confounding by combining information available across several healthcare dimensions. This study assessed the methodology and reporting of the hdPS in comparative effectiveness and safety research.

Study design and setting: In this methodological review, we searched PubMed and Google Scholar from July 2009 to May 2022 for studies that used the hdPS for evaluating the effectiveness or safety of healthcare interventions. Two reviewers independently extracted study characteristics and assessed how the hdPS was applied and reported. Risk of bias was evaluated with the Rrisk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool.

Results: In total, 136 studies met the inclusion criteria; the median publication year was 2018 (Q1-Q3 2016-2020). The studies included 192 datasets, mostly North American databases (n = 132, 69%). The hdPS was used in primary analysis in 120 studies (88%). Dimensions were defined in 101 studies (74%), with a median of 5 (Q1-Q3 4-6) dimensions included. A median of 500 (Q1-Q3 200-500) empirically identified covariates were selected. Regarding hdPS reporting, only 11 studies (8%) reported all recommended items. Most studies (n = 81, 60%) had a moderate overall risk of bias.

Conclusion: There is room for improvement in the reporting of hdPS studies, especially regarding the transparency of methodological choices that underpin the construction of the hdPS.

Keywords: Bias; Confounding; Methodology; Real-world evidence; Reporting; hdPS.