ArticleNursing research
Which Propensity Score Method Best Reduces Confounder Imbalance? An Example From a Retrospective Evaluation of a Childhood Obesity Intervention.
Article in Nursing research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Article
- The impact of change in neighborhood poverty on BMI trajectory of 37,544 New York City youth: a longitudinal study.BMC public health · 2020Article
- Does unemployment contribute to poorer health-related quality of life among Swedish adults?BMC public health · 2019Article
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundPropensity score (PS) methods are increasingly being employed by researchers to reduce bias arising from confounder imbalance when using observational data to examine intervention effects.
objectiveThe purpose of this study was to examine PS theory and methodology and compare application of three PS methods (matching, stratification, weighting) to determine which best improves confounder balance.
methodsBaseline characteristics of a sample of 20,518 school-aged children with severe obesity (of whom 1,054 received an obesity intervention) were assessed prior to PS application. Three PS methods were then applied to the data to determine which showed the greatest improvement in confounder balance between the intervention and control group. The effect of each PS method on the outcome variable-body mass index percentile change at one year-was also examined. SAS 9.4 and Comprehensive Meta-analysis statistical software were used for analyses.
resultsPrior to PS adjustment, the intervention and control groups differed significantly on seven of 11 potential confounders. PS matching removed all differences. PS stratification and weighting both removed one difference but created two new differences. Sensitivity analyses did not change these results. Body mass index percentile at 1 year decreased in both groups. The size of the decrease was smaller in the intervention group, and the estimate of the decrease varied by PS method. DISCUSSION: Selection of a PS method should be guided by insight from statistical theory and simulation experiments, in addition to observed improvement in confounder balance. For this data set, PS matching worked best to correct confounder imbalance. Because each method varied in correcting confounder imbalance, we recommend that multiple PS methods be compared for ability to improve confounder balance before implementation in evaluating treatment effects in observational data.
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Registered trials
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