Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
Open accessAbstract
Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure effects of intervention or treatment on outcomes. They are also the designs of choice for health technology assessment (HTA). Randomization ensures comparability, in both measured and unmeasured pretreatment characteristics, of individuals assigned to treatment and control or comparator. However, even adequately powered RCTs are not always feasible for several reasons such as cost, time, practical and ethical constraints, and limited generalizability. RCTs rely on data collected on selected, homogeneous population under highly controlled conditions; hence, they provide evidence on efficacy of interventions rather than on effectiveness. Alternatively, observational studies can provide evidence on the relative effectiveness or safety of a health technology compared to one or more alternatives when provided under the setting of routine health care practice. In observational studies, however, treatment assignment is a non-random process based on an individual's baseline characteristics; hence, treatment groups may not be comparable in their pretreatment characteristics. As a result, direct comparison of outcomes between treatment groups might lead to biased estimate of the treatment effect. Propensity score approaches have been used to achieve balance or comparability of treatment groups in terms of their measured pretreatment covariates thereby controlling for confounding bias in estimating treatment effects. Despite the popularity of propensity scores methods and recent important methodological advances, misunderstandings on their applications and limitations are all too common. In this article, we present a review of the propensity scores methods, extended applications, recent advances, and their strengths and limitations.
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Such approach has also been recommended for a more generalisable randomised trials where certain covariates exist by chance.
“Such approach has also been recommended for a more generalisable randomised trials where certain covariates exist by chance.52,53”
From Mindfulness-based interventions for psychological wellbeing and quality of life in men with prostate cancer: A systematic review and meta-analysis (2021) · cites this paper as [52]
The Variable Selection and Propensity Score Estimation section says propensity scores in randomized trials are known by design, apart from chance variations, and describes using propensity scores to balance measured covariates. It recommends estimating them for observational studies, however, not for making randomized trials more generalizable or addressing chance covariate imbalances in those trials. No page number is available in our copy. (LLM, conf=0.78)
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Self-citationVijverberg, S. J. H., Hernandez‐Pacheco, N., Farzan, N., et al. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4275 · Cited by 15 · see citing works
Myers et al. Respond to "Understanding Bias Amplification"
Myers, J. A., Rassen, J. A., Gagne, J. J., Huybrechts, K. F., Schneeweiß, S., Rothman, K. J., & Glynn, R. J. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr353 · Cited by 11 · see citing works
Pharmacoepidemiology and drug safety
(1993) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.2630020212 · Cited by 11 · see citing works
The specification of the propensity score in multilevel observational studies
Arpino, B., & Mealli, F. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.11.008 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Ali M. S. Douglas I. J. Williamson E. Prieto-Alhambra D. Smeeth L. (2018b). “ A joint application of disease risk score and propensity score to control for confounding: A clinical example,” in Pharmacoepidemiology and drug safety vol. 27 (NJ USA: Wiley 111 River St, Hoboken 07030-5774), 27– 27.
- Ali M. S. Khalid S. Collins G. Prieto-Alhambra D. (2017b). The comparative performance of logistic regression and random forest in propensity score methods: A simulation study. Pharmacoepidemiol. Drug Saf. 26:(Suppl.2): 3– 636. 10.1002/pds.4275 28547787 · DOI
- Ali M. S. Khalid S. Groenwold R. Collins G. S. Klungel O. Prieto-Alhambra D. (2017c). Instrumental variables to test for unmeasured confounding: a precautionary note. Pharmacoepidemiol. Drug Saf. 26:(Suppl.2): 3– 636. 10.1002/pds.4275 28547787 · DOI
- Pearl J. (2012). On a class of bias-amplifying variables that endanger effect estimates. arXiv e-prints e1203.3503. https://ui.adsabs.harvard.edu/abs/2012arXiv1203.3503P.
Cited by
Sorted by citations
Nnate, D. A., Anyachukwu, C. C., Igwe, S. E., & Abaraogu, U. O. (2021) · Psycho-Oncology · doi:10.1002/pon.5749 · Cited by 24 · see citing works
“Such approach has also been recommended for a more generalisable randomised trials where certain covariates exist by chance.(52,53)”
BackgroundYamakawa, S., Nagayama, H., Tomori, K., Ikeda, K., & Niimi, A. (2023) · Frontiers in Rehabilitation Sciences · doi:10.3389/fresc.2022.1045231 · Cited by 10 · see citing works
- Authors
- Ali, M. S., Prieto-Alhambra, D., Lopes, L. C., Ramos, D., Bispo, N., Ichihara, M. Y., Pescarini, J. M., Williamson, E., Fiaccone, R. L., Barreto, M. L., & Smeeth, L.
- Affiliations
- University of Oxford (GB)
- Universidade de Sorocaba (BR)
- Fundação Oswaldo Cruz (BR)
- Universidade Federal da Bahia (BR)
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Ali, M Sanni
- ORCID
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2019-09-18
- Volume
- 10
- Pages
- 973-973
- ISSN
- 1663-9812 (ISSN-L)
- PMC
- PMC6760465
- OpenAlex
- W2965008065
- Cited by
- 200
- References
- 114
- Citations by year
- 2026: 11 · 2025: 23 · 2024: 40 · 2023: 42 · 2022: 41 · 2021: 33 · 2020: 10
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Pharmacology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- Americanae (AECID Library) (submittedVersion, other-oa)
- Arca - Repositório Institucional da Fiocruz (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- Frontiers A Journal of Women Studies
- Funders
- Wellcome Trust (202912, 202912/B/16/Z)
- Medical Research Council (MR/M013278/1)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- propensity score, health technology assessment, observational studies, confounding, treatment effect estimation, generalizability, efficacy versus effectiveness, covariate balance, causal inference
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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