ArticleHealth services & outcomes research methodology2012
Bias and variance trade-offs when combining propensity score weighting and regression: with an application to HIV status and homeless men.
Article in Health services & outcomes research methodology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
16 citing papers in PubMed, 42 citations in OpenAlex.
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- Doubly Robust Estimation of Marginal Cumulative Incidence Curves for Competing Risk Analysis.Statistics in medicine · 2025Article
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- Matching-adjusted indirect comparison of the pelabresib-ruxolitinib combination vs JAKi monotherapy in myelofibrosis.Blood advances · 2023Article
- Outcome of liver transplantation with grafts from brain-dead donors treated with dual hypothermic oxygenated machine perfusion, with particular reference to elderly donors.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2022Article
- Informing power and sample size calculations when using inverse probability of treatment weighting using the propensity score.Statistics in medicine · 2021Article
- Observational
- Adherence to Antiretroviral Treatment Among People Who Started Treatment on the Same-Day of HIV Diagnosis in Ethiopia: A Multicenter Observational Study.HIV/AIDS (Auckland, N.Z.) · 2021Article
- Effectiveness of same-day antiretroviral therapy initiation in retention outcomes among people living with human immunodeficiency virus in Ethiopia: empirical evidence.BMC public health · 2020Article
- An evaluation of whether propensity score adjustment can remove the self-selection bias inherent to web panel surveys addressing sensitive health behaviours.BMC medical research methodology · 2020Article
- Long-term outcomes of colorectal cancer surgery for elderly patients: a propensity score-matched analysis.Surgery today · 2020Article
- Optimizing Variance-Bias Trade-off in the TWANG Package for Estimation of Propensity Scores.Health services & outcomes research methodology · 2017Article
- Long-term survival in laparoscopic vs open resection for colorectal liver metastases: inverse probability of treatment weighting using propensity scores.HPB : the official journal of the International Hepato Pancreato Biliary Association · 2016Observational
- In-office magnetic resonance imaging (MRI) equipment ownership and MRI volume among medicare patients in orthopedic practices.Health economics review · 2015Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as "good balance". Good balance guarantees less biased estimates of the treatment effect. However, the cost of achieving good balance is that the variance of the estimates increases due to a reduction in effective sample size, either through the introduction of propensity score weights or dropping cases when propensity score matching. In this paper, we investigate whether it is best to optimize the balance or to settle for a less than optimal balance and use double robust estimation to adjust for remaining differences. We compare treatment effect estimates from regression, propensity score weighting, and double robust estimation with varying levels of effort expended to achieve balance using data from a study about the differences in outcomes by HIV status in heterosexually active homeless men residing in Los Angeles. Because of how costly data collection efforts are for this population, it is important to find an alternative estimation method that does not reduce effective sample size as much as methods that aggressively aim to optimize balance. Results from a simulation study suggest that there are instances in which we can obtain more precise treatment effect estimates without increasing bias too much by using a combination of regression and propensity score weights that achieve a less than optimal balance. There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.