Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment (⁠|$T$|⁠) is often measured with error. In the context of a validation data where treatment assignment is accurate, we show that misclassification of treatment assignment can impact three distinct stages of a propensity score analysis: (i) propensity score estimation; (ii) propensity score implementation; and (iii) outcome analysis conducted conditional on the estimated propensity score and its implementation. We examine how the error in |$T$| impacts each stage in the context of three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). Using validation data, we propose a two-step likelihood-based approach which fully adjusts for treatment misclassification bias under subclassification. This approach relies on two common measurement error-assumptions; non-differential measurement error and transportability of the measurement error model. We use simulation studies to assess the performance of the adjustment under subclassification, and also investigate the method’s performance under matching or IPTW. We apply the methods to Medicare Part A hospital claims data to estimate the effect of resection versus biopsy on 1-year mortality among |$10\,284$| Medicare beneficiaries diagnosed with brain tumors. The ICD9 billing codes from Medicare Part A inaccurately reflect surgical treatment, but SEER-Medicare validation data are available with more accurate information.

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.

Mismeasured time-to-event data used as a predictor in risk prediction models will lead to inaccurate predictions. This arises in the context of self-reported family history, a time-to-event predictor often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for this type of error. We estimate the measurement error process using a nonparametric smoothed Kaplan–Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian and multivariate survival prediction models. Simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our method mitigates the effects of measurement error mainly by improving calibration and total accuracy. We illustrate our method in the context of Mendelian risk prediction models focusing on misreporting of breast cancer, fitting the measurement error model on data from the University of California at Irvine, and applying our method to counselees from the Cancer Genetics Network. We show that our method improves overall calibration, especially in low risk deciles. Supplementary materials for this article are available online.

Lockdown measures taken in response to the COVID-19 pandemic produced sudden social and economic changes. We examined the extent of air pollution reduction that was attained under these extreme circumstances, whether these reductions occurred everywhere in the US, and the local factors that drove them. Employing counterfactual time series analysis based on seasonal autoregressive integrated moving average models, we found that these extreme lockdown measures led to a reduction in the weekly PM _2.5 average by up to 3.4 µg m ^-3 and the weekly NO ₂ average by up to 11 ppb. These values represent a substantial fraction of the annual mean NAAQS values of 12 µg m ^-3 and 53 ppb, respectively. We found evidence of a statistically significant decline in NO ₂ concentrations following the state-level emergency declaration in almost all states. However, statistically significant declines in PM _2.5 occurred mostly in the West Coast and the Northeast. Certain states experienced a decline in NO ₂ but an increase in PM _2.5 concentrations, indicating that these two pollutants arise from dissimilar sources in these states. Finally, we found evidence that states with a higher percentage of mobile source emissions prior to the emergency measures experienced a greater decline in NO ₂ levels during the pandemic. Although the current social and economic restrictions are not sustainable, our results provide a benchmark to estimate the extent to which air pollution reductions can be achieved. We also identify factors that contributed to the magnitude of pollutant reductions, which can help guide future state-level policies to sustainably reduce air pollution. Significance statement: We quantified the reduction in air pollution levels achieved under the extreme social and economic measures that were put into place as part of COVID-19 state-level emergency declarations. We found a reduction in the weekly average PM _2.5 of up to 3.4 µg m ^-3 and the weekly average of NO ₂ of up to 11 ppb. These values represent a substantial fraction of the annual mean NAAQS values of 12 µg m ^-3 and 53 ppb, respectively. States with a larger fraction of mobile source emissions (e.g., air and road traffic) prior to the pandemic experienced larger declines in NO ₂ emissions, whereas PM _2.5 decline was seen in areas with a higher pre-pandemic proportion of emissions from mobile, stationary (e.g., industrial) and fire sources.