Evidence map›Paper›PMID 40751124›Full record

ArticleBMC medical research methodology2025

Inverse probability weighting for causal inference in hierarchical data.

Lin Hu, Jie Yu, Chunxia Yang, Miaoshuang Chen, Zihuan Tang, Rujun Liao, Chunnong Jike, Ju Wang, Ruobing Wang, Qiang Liao and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Lin HuDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Jie YuDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Chunxia YangDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Miaoshuang ChenDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Zihuan TangDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Rujun LiaoSichuan Center for Disease Control and Prevention, Chengdu, Sichuan, 610041, China.
Chunnong JikeLiangshan Center for Disease Control and Prevention, Xichang, Sichuan, 615000, China.
Ju WangLiangshan Center for Disease Control and Prevention, Xichang, Sichuan, 615000, China.
Ruobing WangLiangshan Center for Disease Control and Prevention, Xichang, Sichuan, 615000, China.
Qiang LiaoLiangshan Center for Disease Control and Prevention, Xichang, Sichuan, 615000, China.
Tao ZhangDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. statzhangtao@scu.edu.cn.

Funding

Chengdu Science and Technology Program 2024-YF05-01784-SNSichuan Science and Technology Program 2024ZYD0102, 2025YFHZ0069
6 · The paper itself

Abstract

objectiveThe aim of this study was to explore the impact of model misspecification, balance, and extreme weights on average treatment effect (ATE) estimation in hierarchical data with unmeasured cluster-level confounders using the multilevel propensity score model and inverse probability weight (IPW).

methodsWe simulated 48 hierarchical data scenarios with unmeasured cluster-level confounders, fitting nine ATE estimation strategies. These strategies were combined with IPW, which used both marginal stabilized weights and cluster-mean stabilized weights. Extreme weights were handled by truncation. Moreover, these models were applied to data from patients co-infected with Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Liangshan Prefecture, Sichuan, China, to estimate the ATE of TB treatment delay on treatment outcomes.

resultsThe simulation study revealed that FEM-Marginal tended to generate the most extreme weights, whereas BART-FE-Marginal considerably reduced the extreme weights in a large number of small clusters. When the data satisfied the positivity assumption, the marginal stabilized weight strategy had the largest absolute percentage bias and RMSE, whereas the cluster-mean stabilized weight strategy had the smallest. Case studies applying different ATE strategies have shown that among HIV-TB co-infected patients, TB treatment delay was a risk factor for treatment outcome.

conclusionsTo better control unmeasured cluster-level confounders, it was more important to consider cluster characteristics when estimating ATE. The use of Bayesian additive regression trees (BART) for constructing multilevel propensity score models, or of cluster-mean stabilized weights is recommended. However, if marginal stabilized weights are used, extreme weight handling methods are necessary to improve effect estimation. In hierarchical data with unmeasured cluster-level confounders, reducing extreme weights, weight variability, and model misspecification while enhancing balance effectively minimizes estimation bias. The case study revealed that TB treatment delay remained associated with treatment outcomes even after accounting for unmeasured cluster-level confounders.

Indexed as

HIV InfectionsModels, StatisticalTuberculosisCausalityChinaCluster AnalysisCoinfectionComputer SimulationHumansProbabilityPropensity ScoreTreatment OutcomeBART modelExtreme weightInverse probability weightMultilevel propensity score modelUnmeasured cluster-level confounder

Identifiers

PMID40751124
PMCPMC12317457

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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.