Association of a Bundled-Payment Program With Cost and Outcomes in Full-Cycle Breast Cancer Care.

IMPORTANCE: Value-driven payment system reform is a potential tool for aligning economic incentives with the improvement of quality and efficiency of health care and containment of cost. Such a payment system has not been researched satisfactorily in full-cycle cancer care. OBJECTIVE: To examine the association of outcomes and medical expenditures with a bundled-payment pay-for-performance program for breast cancer in Taiwan compared with a fee-for-service (FFS) program. DESIGN, SETTING, AND PARTICIPANTS: Data were obtained from the Taiwan Cancer Database, National Health Insurance Claims Data, the National Death Registry, and the bundled-payment enrollment file. Women with newly diagnosed breast cancer and a documented first cancer treatment from January 1, 2004, to December 31, 2008, were selected from the Taiwan Cancer Database and followed up for 5 years, with the last follow-up data available on December 31, 2013. Patients in the bundled-payment program were matched at a ratio of 1:3 with control individuals in an FFS program using a propensity score method. The final sample of 17 940 patients included 4485 (25%) in the bundled-payment group and 13 455 (75%) in the FFS group. MAIN OUTCOMES AND MEASURES: Rates of adherence to quality indicators, survival rates, and medical payments (excluding bonuses paid in the bundled-payment group). The Kaplan-Meier method was used to calculate 5-year overall and event-free survival rates by cancer stage, and the Cox proportional hazards regression model was used to examine the effect of the bundled-payment program on overall and event-free survival. Sensitivity analysis for bonus payments in the bundled-payment group was also performed. RESULTS: The study population included 17 940 women (mean [SD] age, 52.2 [10.3] years). In the bundled-payment group, 1473 of 4215 patients (34.9%) with applicable quality indicators had full (100%) adherence to quality indicators compared with 3438 of 12 506 patients (27.5%) with applicable quality indicators in the FFS group (P < .001). The 5-year event-free survival rates for patients with stages 0 to III breast cancer were 84.48% for the bundled-payment group and 80.88% for the FFS group (P < .01). Although the 5-year medical payments of the bundled-payment group remained stable, the cumulative medical payments for the FFS group steadily increased from $16 000 to $19 230 and exceeded pay-for-performance bundled payments starting in 2008. CONCLUSIONS AND RELEVANCE: In Taiwan, compared with the regular FFS program, bundled payment may lead to better adherence to quality indicators, better outcomes, and more effective cost-control over time.

Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies.

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.

Bayesian random effects for interrater and test-retest reliability with nested clinical observations.

OBJECTIVE: The assessment of inter- and intrarater reliability usually involves more than one level of nesting structures in the collected data, where repeated observations are made by multiple raters. Most approaches, however, are not designed to accommodate both inter- and intrarater reliability jointly, not to mention further difficulties arising when modeling with dichotomous responses. The multiple sources of dependence because of nesting structures and the existence of covariates can result in complexity in inference. STUDY DESIGN AND SETTING: We first establish the equivalence between correlation and kappa under common positive correlation models for multiple raters and then apply a Bayesian generalized linear mixed-effects model to interpret simultaneously both types of reproducibility through different annotations of similarity. In addition to marginal correlations, the correlated random effects among raters are adopted to infer similarity between raters, whereas the correlation for random time effects may contribute to test-retest reliability. RESULTS: This model accounts for individual covariates and random effects because of subjects, raters, and time, and it covers a wide variety of data structures and types. An application of endodontic radiographic examinations is illustrated. CONCLUSION: This Bayesian hierarchical correlation model offers a wide applicability, flexibility, and feasibility in modeling inter- and intrarater reliability together.