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High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

ABSTRACT: BACKGROUND:Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. METHODS:We developed a multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure propensity score; and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared with nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). RESULTS:In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91-1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94 [0.78-1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88 [0.73-1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02 [0.85-1.21]). CONCLUSIONS:In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.

SUBMITTER: Schneeweiss S

PROVIDER: S-EPMC3077219 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

Schneeweiss Sebastian S Rassen Jeremy A JA Glynn Robert J RJ Avorn Jerry J Mogun Helen H Brookhart M Alan MA

Epidemiology (Cambridge, Mass.) 20090701 4

<h4>Background</h4>Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model.<h4>Methods</h4>We developed a multistep algorithm to implement high-dimensional proxy adj ...[more]

PMID: 19487948

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Project description:Background: The aim of this study was to evaluate the cardio-metabolic risk in schizophrenia patients treated by atypical antipsychotic drugs compared with that in those treated without atypical antipsychotic drugs using a nationwide insurance claims database and medical examination database in Japan.Methods: Eligible patients were defined as those meeting the following two criteria: (i) A diagnosis of schizophrenia (ICD-10 code: F20) was made between 1 January 2005 and 31 December 2017, with data available for at least 6?months before the diagnosis was made (index month), and (ii) health check-up data were available within ±3?months of the index month. The primary endpoint was changes in cardio-metabolic risk based on the Suita score at 1?year, and the secondary endpoints were changes in medical examination data related to cardio-metabolic risk (total cholesterol [TC], triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, body mass index [BMI], and hemoglobin A1c) at 1?year. The primary endpoint was evaluated by multivariate analysis, with the cumulative chlorpromazine equivalent amount and the baseline Suita score added as covariates.Results: One-hundred eighty five pairs of propensity score (PS)-matched patients were evaluated. Patients receiving atypical antipsychotic drugs exhibited a greater change in the Suita score and a risk of coronary heart disease based on the Suita score of 0.530 and 0.098%, respectively, than patients not receiving atypical antipsychotic drugs, but there was no significant difference (p =?0.412 and 0.610). The significant changes in TC and BMI were determined as 6.525?mg/dL and 0.380?kg/m2 greater, respectively, in patients treated with atypical antipsychotic drugs (p =?0.037 and 0.011).Conclusions: There were no significant increases in changes in the Suita score at 1?year by treatment with atypical antipsychotic drugs compared with treatment without atypical antipsychotic drugs. However, the TC and BMI were significantly higher in patients treated with atypical antipsychotic drugs.

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High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

Publications

High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

Similar Datasets

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