On variance estimate for covariate adjustment by propensity score analysis

Zou, Baiming; Zou, Fei; Shuster, Jonathan J.; Tighe, Patrick J.; Koch, Gary G.; Zhou, Haibo

doi:10.1002/sim.6943

Cited by 19 publications

(18 citation statements)

References 21 publications

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“…This method does not account for the fact that PPS is estimated from the data, which may lead to too‐conservative confidence intervals . This limitation also applies to PPS‐adjustment , PPS‐stratification and PPS‐matching methods for which variance formulae have been recently developed. Bootstrap estimation of the variance has also been proposed .…”

Section: Discussionmentioning

confidence: 99%

Estimation of conditional and marginal odds ratios using the prognostic score

et al. 2016

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Introduced by Hansen in 2008, the prognostic score (PGS) has been presented as 'the prognostic analogue of the propensity score' (PPS). PPS-based methods are intended to estimate marginal effects. Most previous studies evaluated the performance of existing PGS-based methods (adjustment, stratification and matching using the PGS) in situations in which the theoretical conditional and marginal effects are equal (i.e., collapsible situations). To support the use of PGS framework as an alternative to the PPS framework, applied researchers must have reliable information about the type of treatment effect estimated by each method. We propose four new PGS-based methods, each developed to estimate a specific type of treatment effect. We evaluated the ability of existing and new PGS-based methods to estimate the conditional treatment effect (CTE), the (marginal) average treatment effect on the whole population (ATE), and the (marginal) average treatment effect on the treated population (ATT), when the odds ratio (a non-collapsible estimator) is the measure of interest. The performance of PGS-based methods was assessed by Monte Carlo simulations and compared with PPS-based methods and multivariate regression analysis. Existing PGS-based methods did not allow for estimating the ATE and showed unacceptable performance when the proportion of exposed subjects was large. When estimating marginal effects, PPS-based methods were too conservative, whereas the new PGS-based methods performed better with low prevalence of exposure, and had coverages closer to the nominal value. When estimating CTE, the new PGS-based methods performed as well as traditional multivariate regression. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Estimation of conditional and marginal odds ratios using the prognostic score

et al. 2016

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“…However, Fig 2C and 2D show a slight under-coverage of the CI for TSC when the confounders are non-Gaussian (86.6% and 87.4% instead of 95%). This is explained by the slight underestimation of the standard error (ASE compared to ESE in case 1.b in S1 Table) because the variance of estimated parameters from the propensity score model is neglected [29]. By using the true parameter value for the propensity score model instead of the estimates obtained on the validation sample, this underestimation disappears (results not shown).…”

Section: Resultsmentioning

confidence: 99%

Health administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data

et al. 2019

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BackgroundStudies using health administrative databases (HAD) may lead to biased results since information on potential confounders is often missing. Methods that integrate confounder data from cohort studies, such as multivariate imputation by chained equations (MICE) and two-stage calibration (TSC), aim to reduce confounding bias. We provide new insights into their behavior under different deviations from representativeness of the cohort.MethodsWe conducted an extensive simulation study to assess the performance of these two methods under different deviations from representativeness of the cohort. We illustrate these approaches by studying the association between benzodiazepine use and fractures in the elderly using the general sample of French health insurance beneficiaries (EGB) as main database and two French cohorts (Paquid and 3C) as validation samples.ResultsWhen the cohort was representative from the same population as the HAD, the two methods are unbiased. TSC was more efficient and faster but its variance could be slightly underestimated when confounders were non-Gaussian. If the cohort was a subsample of the HAD (internal validation) with the probability of the subject being included in the cohort depending on both exposure and outcome, MICE was unbiased while TSC was biased. The two methods appeared biased when the inclusion probability in the cohort depended on unobserved confounders.ConclusionWhen choosing the most appropriate method, epidemiologists should consider the origin of the cohort (internal or external validation) as well as the (anticipated or observed) selection biases of the validation sample.

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“…The field is still wrestling with issues of covariate and model selection, as well as variance estimation, and machine-learning algorithms are being re-tuned away from minimizing prediction error towards improving the quality of the treatment effect estimate. [111, 112] Many of these new and exciting developments will need to be explored through further simulation and empirical examples, but together they represent a bright path ahead in overcoming many of the design and analytic challenges in the study of therapeutic effects and harms.…”

Section: Discussionmentioning

confidence: 99%

Propensity Scores in Pharmacoepidemiology: Beyond the Horizon

2017

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Purpose of review Propensity score methods have become commonplace in pharmacoepidemiology over the past decade. Their adoption has confronted formidable obstacles that arise from pharmacoepidemiology's reliance on large healthcare databases of considerable heterogeneity and complexity. These include identifying clinically meaningful samples, defining treatment comparisons, and measuring covariates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment decisions in the face of variation in healthcare practice, and dealing with missing information and unmeasured confounding. In this review, we examine the application of propensity score methods in pharmacoepidemiology with particular attention to these and other issues, with an eye towards standards of practice, recent methodological advances, and opportunities for future progress. Recent findings Propensity score methods have matured in ways that can advance comparative effectiveness and safety research in pharmacoepidemiology. These include natural extensions for categorical treatments, matching algorithms that can optimize sample size given design constraints, weighting estimators that asymptotically target matched and overlap samples, and the incorporation of machine learning to aid in covariate selection and model building. Summary These recent and encouraging advances should be further evaluated through simulation and empirical studies, but nonetheless represent a bright path ahead for the observational study of treatment benefits and harms.

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On variance estimate for covariate adjustment by propensity score analysis

Cited by 19 publications

References 21 publications

Estimation of conditional and marginal odds ratios using the prognostic score

Estimation of conditional and marginal odds ratios using the prognostic score

Health administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data

Propensity Scores in Pharmacoepidemiology: Beyond the Horizon

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