Covariate Balance as a Quality Indicator for Propensity Score Analysis

Kara, Yusuf; Kamata, Akihito; GALLEGOS, Elisa; Patarapichayatham, Chalie; Potgieter, Cornelis J.

doi:10.21031/epod.993571

Cited by 1 publication

(1 citation statement)

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“…Propensity score methods and traditional regression modelling, such as G-computation, tend to yield similar results [11], but this has yet to be investigated within our field of interest. Simulation studies have revealed that the variable selection has a bearing on the result when using propensity scores [12][13][14][15], but for unemployment and health this have not previously been investigated.…”

Section: Introductionmentioning

confidence: 99%

Methodological perspectives on the study of the health effects of unemployment – reviewing the mode of unemployment, the statistical analysis method and the role of confounding factors

Norström

Hammarström²

2022

BMC Med Res Methodol

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Introduction Studying the relationship between unemployment and health raises many methodological challenges. In the current study, the aim was to evaluate the sensitivity of estimates based on different ways of measuring unemployment and the choice of statistical model. Methods The Northern Swedish cohort was used, and two follow-up surveys thereof from 1995 and 2007, as well as register data about unemployment. Self-reported current unemployment, self-reported accumulated unemployment and register-based accumulated unemployment were used to measure unemployment and its effect on self-reported health was evaluated. Analyses were conducted with G-computation, logistic regression and three estimators for the inverse probability weighting propensity scores, and 11 potentially confounding variables were part of the analyses. Results were presented with absolute differences in the proportion with poor self-reported health between unemployed and employed individuals, except when logistic regression was used alone. Results Of the initial 1083 pupils in the cohort, our analyses vary between 488–693 individuals defined as employed and 61–214 individuals defined as unemployed. In the analyses, the deviation was large between the unemployment measures, with a difference of at least 2.5% in effect size when unemployed was compared with employed for the self-reported and register-based unemployment modes. The choice of statistical method only had a small influence on effect estimates and the deviation was in most cases lower than 1%. When models were compared based on the choice of potential confounders in the analytical model, the deviations were rarely above 0.6% when comparing models with 4 and 11 potential confounders. Our variable for health selection was the only one that strongly affected estimates when it was not part of the statistical model. Conclusions How unemployment is measured is highly important when the relationship between unemployment and health is estimated. However, misspecifications of the statistical model or choice of analytical method might not matter much for estimates except for the inclusion of a variable measuring health status before becoming unemployed. Our results can guide researchers when analysing similar research questions. Model diagnostics is commonly lacking in publications, but they remain very important for validation of analyses.

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