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Adaptive propensity score procedure improves matching in prospective observational trials.

ABSTRACT: BACKGROUND:Randomized controlled trials are the gold-standard for clinical trials. However, randomization is not always feasible. In this article we propose a prospective and adaptive matched case-control trial design assuming that a control group already exists. METHODS:We propose and discuss an interim analysis step to estimate the matching rate using a resampling step followed by a sample size recalculation. The sample size recalculation is based on the observed mean resampling matching rate. We applied our approach in a simulation study and to a real data set to evaluate the characteristics of the proposed design and to compare the results to a naive approach. RESULTS:The proposed design achieves at least 10% higher matching rate than the naive approach at final analysis, thus providing a better estimation of the true matching rate. A good choice for the interim analysis seems to be a fraction of around [Formula: see text] to [Formula: see text] of the control patients. CONCLUSION:The proposed resampling step in a prospective matched case-control trial design leads to an improved estimate of the final matching rate and, thus, to a gain in power of the approach due to sensible sample size recalculation.

SUBMITTER: Weber D

PROVIDER: S-EPMC6636117 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Adaptive propensity score procedure improves matching in prospective observational trials.

Weber Dorothea D Uhlmann Lorenz L Schönenberger Silvia S Kieser Meinhard M

BMC medical research methodology 20190716 1

<h4>Background</h4>Randomized controlled trials are the gold-standard for clinical trials. However, randomization is not always feasible. In this article we propose a prospective and adaptive matched case-control trial design assuming that a control group already exists.<h4>Methods</h4>We propose and discuss an interim analysis step to estimate the matching rate using a resampling step followed by a sample size recalculation. The sample size recalculation is based on the observed mean resampling ...[more]

PMID: 31311500

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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:BackgroundThe rapidly increased spending on insulin is a major public health issue in the United States. Industry marketing might be one of the upstream determinants of physicians' prescription of long-acting insulin-the most commonly used and costly type of insulin, but the evidence is lacking. We therefore aimed to investigate the association between industry payments to physicians and subsequent prescriptions of long-acting insulin.Methods and findingsUsing the databases of Open Payments and Medicare Part D, we examined the association between the receipt of industry payments for long-acting insulin in 2016 and (1) the number of claims; (2) the costs paid for all claims; and (3) the costs per claim of long-acting insulin in 2017. We also examined the association between the receipt of payments and the change in these outcomes from 2016 to 2017. We employed propensity score matching to adjust for the physician-level characteristics (sex, years in practice, specialty, and medical school attended). Among 145,587 eligible physicians treating Medicare beneficiaries, 51,851 physicians received industry payments for long-acting insulin worth $22.3 million. In the propensity score-matched analysis including 102,590 physicians, we found that physicians who received the payments prescribed a higher number of claims (adjusted difference, 57.8; 95% CI, 55.8 to 59.7), higher costs for total claims (adjusted difference, +$22,111; 95% CI, $21,387 to $22,836), and higher costs per claim (adjusted difference, +$71.1; 95% CI, $69.0 to $73.2) of long-acting insulin, compared with physicians who did not receive the payments. The association was also found for changes in these outcomes from 2016 to 2017. Limitations to our study include limited generalizability, confounding, and possible reverse causation.ConclusionsIndustry marketing payments to physicians for long-acting insulin were associated with the physicians' prescriptions and costs of long-acting insulin in the subsequent year. Future research is needed to assess whether policy interventions on physician-industry financial relationships will help to ensure appropriate prescriptions and limit overall costs of this essential drug for diabetes care.

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Adaptive propensity score procedure improves matching in prospective observational trials.

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Adaptive propensity score procedure improves matching in prospective observational trials.

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