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Veridical Causal Inference using Propensity Score Methods for Comparative Effectiveness Research with Medical Claims.

ABSTRACT: Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally-varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

SUBMITTER: Ross RD

PROVIDER: S-EPMC8142944 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCardinality matching (CM), a novel matching technique, finds the largest matched sample meeting prespecified balance criteria thereby overcoming limitations of propensity score matching (PSM) associated with limited covariate overlap, which are especially pronounced in studies with small sample sizes. The current study proposes a framework for large-scale CM (LS-CM); and compares large-scale PSM (LS-PSM) and LS-CM in terms of post-match sample size, covariate balance and residual confounding at progressively smaller sample sizes.MethodsEvaluation of LS-PSM and LS-CM within a comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and thiazide or thiazide-like diuretic monotherapy identified from a U.S. insurance claims database. Candidate covariates included patient demographics, and all observed prior conditions, drug exposures and procedures. Propensity scores were calculated using LASSO regression, and candidate covariates with non-zero beta coefficients in the propensity model were defined as matching covariates for use in LS-CM. One-to-one matching was performed using progressively tighter parameter settings. Covariate balance was assessed using standardized mean differences. Hazard ratios for negative control outcomes perceived as unassociated with treatment (i.e., true hazard ratio of 1) were estimated using unconditional Cox models. Residual confounding was assessed using the expected systematic error of the empirical null distribution of negative control effect estimates compared to the ground truth. To simulate diverse research conditions, analyses were repeated within 10 %, 1 and 0.5 % subsample groups with increasingly limited covariate overlap.ResultsA total of 172,117 patients (ACEI: 129,078; thiazide: 43,039) met the study criteria. As compared to LS-PSM, LS-CM was associated with increased sample retention. Although LS-PSM achieved balance across all matching covariates within the full study population, substantial matching covariate imbalance was observed within the 1 and 0.5 % subsample groups. Meanwhile, LS-CM achieved matching covariate balance across all analyses. LS-PSM was associated with better candidate covariate balance within the full study population. Otherwise, both matching techniques achieved comparable candidate covariate balance and expected systematic error.ConclusionsLS-CM found the largest matched sample meeting prespecified balance criteria while achieving comparable candidate covariate balance and residual confounding. We recommend LS-CM as an alternative to LS-PSM in studies with small sample sizes or limited covariate overlap.

Project description:Background Extrathyroidal extension is a major risk factor for poor prognosis in papillary thyroid cancer. However, the effect of different degrees of extrathyroidal extension on prognosis remains controversial. We performed a retrospective study to elucidate how the extent of extrathyroidal extension in papillary thyroid cancer affected the clinical prognosis of patients and its covariates. Methods The study included 108,426 patients with papillary thyroid cancer. We categorized the extent of extension into none, capsule, strap muscles, and other organs. Three causal inference methods for retrospective studies, namely, inverse probability of treatment weighting, standardized mortality ratio weighting, and propensity score matching analysis, were used to minimize potential selection bias. Kaplan–Meier analysis and univariate Cox regression analyses were applied to analyze the precise effect of ETE on survival in papillary thyroid cancer patients. Results In the Kaplan–Meier survival analysis, only extrathyroidal extension into or beyond the strap muscles was statistically significant for both overall survival (OS) and thyroid cancer-specific survival (TCSS). In univariate Cox regression analyses before and after matching or weighting based on causal inference, extrathyroidal extension into soft tissues or other organs is a high-risk factor for both overall survival and thyroid cancer-specific survival. Sensitivity analysis revealed that lower overall survival was observed in patients with older age (≥55) and larger tumor size (>2 cm) of papillary thyroid cancer with extrathyroidal extension into or beyond the strap muscles. Conclusions Our study indicates that extrathyroidal extension into soft tissues or other organs is a high-risk factor in all papillary thyroid cancer. Even though invasion into the strap muscles did not seem to be a marker for poor prognosis, it still impaired the overall survival of patients with older age (≥55 years old) or larger tumor size (>2 cm). Further investigation is needed to confirm our results and to clarify further risk factors independent of extrathyroidal extension.

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Veridical Causal Inference using Propensity Score Methods for Comparative Effectiveness Research with Medical Claims.

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