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Identification of novel biomarkers for risk prediction is important for disease prevention and optimal treatment selection. However, studies aiming to discover which biomarkers are useful for risk prediction often require the use of stored biological samples from large assembled cohorts, and thus the depletion of a finite and precious resource. To make efficient use of such stored samples, two-phase sampling designs are often adopted as resource-efficient sampling strategies, especially when the outcome of interest is rare. Existing methods for analyzing data from two-phase studies focus primarily on single marker analysis or fitting the Cox regression model to combine information from multiple markers. However, the Cox model may not fit the data well. Under model misspecification, the composite score derived from the Cox model may not perform well in predicting the outcome. Under a general two-phase stratified cohort sampling design, we present a novel approach to combining multiple markers to optimize prediction by fitting a flexible nonparametric transformation model. Using inverse probability weighting to account for the outcome-dependent sampling, we propose to estimate the model parameters by maximizing an objective function which can be interpreted as a weighted C-statistic for survival outcomes. Regardless of model adequacy, the proposed procedure yields a sensible composite risk score for prediction. A major obstacle for making inference under two phase studies is due to the correlation induced by the finite population sampling, which prevents standard inference procedures such as the bootstrap from being used for variance estimation. We propose a resampling procedure to derive valid confidence intervals for the model parameters and the C-statistic accuracy measure. We illustrate the new methods with simulation studies and an analysis of a two-phase study of high-density lipoprotein cholesterol (HDL-C) subtypes for predicting the risk of coronary heart disease.

Background Multivariable prediction models are important statistical tools for providing synthetic diagnosis and prognostic algorithms based on patients’ multiple characteristics. Their apparent measures for predictive accuracy usually have overestimation biases (known as ‘optimism’) relative to the actual performances for external populations. Existing statistical evidence and guidelines suggest that three bootstrap-based bias correction methods are preferable in practice, namely Harrell’s bias correction and the .632 and .632+ estimators. Although Harrell’s method has been widely adopted in clinical studies, simulation-based evidence indicates that the .632+ estimator may perform better than the other two methods. However, these methods’ actual comparative effectiveness is still unclear due to limited numerical evidence. Methods We conducted extensive simulation studies to compare the effectiveness of these three bootstrapping methods, particularly using various model building strategies: conventional logistic regression, stepwise variable selections, Firth’s penalized likelihood method, ridge, lasso, and elastic-net regression. We generated the simulation data based on the Global Utilization of Streptokinase and Tissue plasminogen activator for Occluded coronary arteries (GUSTO-I) trial Western dataset and considered how event per variable, event fraction, number of candidate predictors, and the regression coefficients of the predictors impacted the performances. The internal validity of C -statistics was evaluated. Results Under relatively large sample settings (roughly, events per variable ≥ 10), the three bootstrap-based methods were comparable and performed well. However, all three methods had biases under small sample settings, and the directions and sizes of biases were inconsistent. In general, Harrell’s and .632 methods had overestimation biases when event fraction become lager. Besides, .632+ method had a slight underestimation bias when event fraction was very small. Although the bias of the .632+ estimator was relatively small, its root mean squared error (RMSE) was comparable or sometimes larger than those of the other two methods, especially for the regularized estimation methods. Conclusions In general, the three bootstrap estimators were comparable, but the .632+ estimator performed relatively well under small sample settings, except when the regularized estimation methods are adopted.

Only a small proportion of people with a substance use disorder (SUD) receive treatment. The shortage of SUD treatment providers, particularly in rural areas, is an important driver of this treatment gap. Telemedicine could be a means of expanding access to treatment. However, several key regulatory and reimbursement barriers to greater use of telemedicine for SUD (tele-SUD) exist, and both Congress and the states are considering or have recently passed legislation to address them. To inform these efforts, we describe how tele-SUD is being used. Using claims data for 2010-17 from a large commercial insurer, we identified characteristics of tele-SUD users and examined how tele-SUD is being used in conjunction with in-person SUD care. Despite a rapid increase in tele-SUD over the study period, we found low use rates overall, particularly relative to the growth in telemental health. Tele-SUD is primarily used to complement in-person care and is disproportionately used by those with relatively severe SUD. Given the severity of the opioid epidemic, low rates of tele-SUD use represent a missed opportunity. As tele-SUD becomes more available, it will be important to monitor closely which tele-SUD delivery models are being used and their impact on access and outcomes.

BackgroundEnrollment of Medicare beneficiaries in medicare advantage (MA) plans has been steadily increasing. Prior research has shown differences in healthcare access and outcomes based on Medicare enrollment status. This study sought to compare utilization of minimally invasive colorectal cancer (CRC) surgery and postoperative outcomes between MA and Fee-for-Service (FFS) beneficiaries.MethodsA retrospective cohort study of beneficiaries  ≥ 65.5 years of age enrolled in FFS and MA plans was performed of patients undergoing a CRC resection from 2016 to 2019. The primary outcome was operative approach, defined as minimally invasive (laparoscopic) or open. Secondary outcomes included robotic assistance, hospital length-of-stay, mortality, discharge disposition, and hospital readmission. Using balancing weights, we performed a tapered analysis to examine outcomes with adjustment for potential confounders.ResultsMA beneficiaries were less likely to have lymph node (12.9 vs 14.4%, p < 0.001) or distant metastases (15.5% vs 17.0%, p < 0.001), and less likely to receive chemotherapy (6.2% vs 6.7%, p < 0.001), compared to FFS beneficiaries. MA beneficiaries had a higher risk-adjusted likelihood of undergoing laparoscopic CRC resection (OR 1.12 (1.10–1.15), p < 0.001), and similar rates of robotic assistance (OR 1.00 (0.97–1.03), p = 0.912), compared to FFS beneficiaries. There were no differences in risk-adjusted length-of-stay (β coefficient 0.03 (− 0.05–0.10), p = 0.461) or mortality at 30-60-and 90-days (OR 0.99 (0.95–1.04), p = 0.787; OR 1.00 (0.96–1.04), p = 0.815; OR 0.98 (0.95–1.02), p = 0.380). MA beneficiaries had a lower likelihood of non-routine disposition (OR 0.77 (0.75–0.78), p < 0.001) and readmission at 30-60-and 90-days (OR 0.76 (0.73–0.80), p < 0.001; OR 0.78 (0.75–0.81), p < 0.001; OR 0.79 (0.76–0.81), p < 0.001).ConclusionsMA beneficiaries had less advanced disease at the time of CRC resection and a greater likelihood of undergoing a laparoscopic procedure. MA enrollment is associated with improved health outcomes for elderly beneficiaries undergoing operative treatment for CRC.

A central question in the debate over privatized Medicare is whether increased government payments to private Medicare Advantage (MA) plans generate lower premiums for consumers or higher profits for producers. Using difference-in-differences variation brought about by a sharp legislative change, we find that MA insurers pass through 45 percent of increased payments in lower premiums and an additional 9 percent in more generous benefits. We show that advantageous selection into MA cannot explain this incomplete pass-through. Instead, our evidence suggests that market power is important, with premium pass-through rates of 13 percent in the least competitive markets and 74 percent in the most competitive.

Traditional fee-for-service (FFS) Medicare's prospective payment systems for postacute care provide little incentive to coordinate care or control costs. In contrast, Medicare Advantage plans pay for postacute care out of monthly capitated payments and thus have stronger incentives to use it efficiently. We compared the use of postacute care in skilled nursing and inpatient rehabilitation facilities by enrollees in Medicare Advantage and FFS Medicare after hospital discharge for three high-volume conditions: lower extremity joint replacement, stroke, and heart failure. After accounting for differences in patient characteristics at discharge, we found lower intensity of postacute care for Medicare Advantage patients compared to FFS Medicare patients discharged from the same hospital, across all three conditions. Medicare Advantage patients also exhibited better outcomes than their FFS Medicare counterparts, including lower rates of hospital readmission and higher rates of return to the community. These findings suggest that payment reforms such as bundling in FFS Medicare may reduce the intensity of postacute care without adversely affecting patient health.

Unlike fee-for-service (FFS) Medicare, most Medicare Advantage (MA) plans have a preferred network of care providers that serve most of a plan's enrollees. Little is known about how the quality of care MA enrollees receive differs from that of FFS Medicare enrollees. This article evaluates the differences in the quality of skilled nursing facilities (SNFs) that Medicare Advantage and FFS beneficiaries entered in the period 2012-14. After we controlled for patients' clinical, demographic, and residential neighborhood effects, we found that FFS Medicare patients have substantially higher probabilities of entering higher-quality SNFs (those rated four or five stars by Nursing Home Compare) and those with lower readmission rates, compared to MA enrollees. The difference between MA and FFS Medicare SNF selections was less for enrollees in higher-quality MA plans than those in lower-quality plans, but Medicare Advantage still guided patients to lower-quality facilities.

Medicare Advantage (MA) plans are increasingly enrolling veterans. Because MA plans receive full capitated payments regardless of whether or not veterans use Medicare services, the federal government can incur substantial duplicative, wasteful spending if veterans in MA plans predominantly seek care through the Veterans Health Administration (VHA) system. The recent growth of MA plans that disproportionately enroll veterans could further exacerbate such wasteful spending. Using national data, we found that veterans increasingly enrolled in MA between 2016 and 2022, including in a growing number of MA plans in which 20 percent or more of the enrollees were veterans. Notably, about one in five VHA enrollees in these high-veteran MA plans did not incur any Medicare services paid by MA within a given year-a rate 2.5 times that of VHA enrollees in other MA plans and 5.7 times that of the general MA population. Meanwhile, VHA enrollees in high-veteran MA plans were significantly more likely to receive VHA-funded care. In 2020, the Centers for Medicare and Medicaid Services paid more than $1.32 billion to MA plans for VHA enrollees who did not use any Medicare services, with 19.1 percent going to high-veteran MA plans.

Most Medicare beneficiaries obtain supplemental insurance or enroll in Medicare Advantage (MA) to protect against potentially high cost sharing in traditional Medicare (TM). We examined changes in Medicare supplemental insurance coverage in the context of MA growth. Repeated cross-sectional analysis of the Medicare Current Beneficiary Survey from 2005 to 2019. We determined whether Medicare beneficiaries 65 years and older were enrolled in MA (without Medicaid), TM without supplemental coverage, TM with employer-sponsored supplemental coverage, TM with Medigap, or Medicaid (in TM or MA). From 2005 to 2019, beneficiaries with TM and supplemental insurance provided by their former (or current) employer declined by approximately half (31.8% to 15.5%) while the share in MA (without Medicaid) more than doubled (13.4% to 35.1%). The decline in supplemental employer-sponsored insurance use was greater for White and for higher-income beneficiaries. Over the same period, beneficiaries in TM without supplemental coverage declined by more than a quarter (13.9% to 10.1%). This decline was largest for Black, Hispanic, and lower-income beneficiaries. The rapid rise in MA enrollment from 2005 to 2019 was accompanied by substantial changes in supplemental insurance with TM. Our results emphasize the interconnectedness of different insurance choices made by Medicare beneficiaries.

Background When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. Methods An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Results Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. Conclusions The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.