Factors associated with clinical trial participation for patients with renal cell carcinoma.

OBJECTIVE: Recruitment of a diverse and representative study population is critical to the external validity of oncology clinical trials. The primary objective of this study was to characterize the factors associated with clinical trial participation for patients with renal cell carcinoma and the secondary objective was to examine differences in survival outcomes. MATERIALS AND METHODS: We used a matched case-control design by querying the National Cancer Database for patients with renal cell carcinoma who were coded as having enrolled in a clinical trial. Trial patients were matched in a 1:5 ratio to the control cohort based on clinical stage and then sociodemographic variables were compared between the 2 groups. Multivariable conditional logistic regression models evaluated factors associated with clinical trial participation. The trial patient cohort was then matched again in a 1:10 ratio based on age, clinical stage, and comorbidities. Log-rank test was used to compare overall survival (OS) between these groups. RESULTS: From 2004 to 2014, 681 patients enrolled in clinical trials were identified. Clinical trial patients were significantly younger and had a lower Charlson-Deyo comorbidity score. On multivariate analysis, male patients and white patients were more likely to participate compared to their Black counterparts. Having Medicaid or Medicare negatively associated with trial participation. Median OS was greater among clinical trial participants. CONCLUSION: Patient sociodemographic factors remain significantly associated with clinical trial participation and trial participants experienced superior OS to their matched counterparts.

Influence of government-driven quality assessment program on patients with chronic obstructive pulmonary disease.

BACKGROUND: The Korean Health Insurance Review and Assessment Service (HIRA) has launched the Chronic Obstructive Pulmonary Disease (COPD) Quality Assessment Program (CQAP) since 2014. We aimed to reveal the influence of this national program on clinical outcomes and the burden of COPD in Korea. METHODS: The CQAP is conducted annually. We used healthcare claims data linked with the results of the program provided by HIRA between May 2014 and April 2017. Patients were considered to have COPD if they visited a hospital for COPD management during the assessment term. Those who visited a medical institution for COPD and were prescribed COPD medications at least twice were assessed by the CQAP (assessed subjects, AS; not-assessed subjects, NAS). CQAP evaluated the pulmonary function test conduction rate, regular visitation rate, and prescription rates of COPD medications. RESULTS: Among the 560,000 patients with COPD, about 140,000 were assessed by the CQAP annually. In both groups, the pulmonary function test conduction rate and inhaled bronchodilator prescription rate improved since 2014. Compared to the NAS group, the risk of admission and all-cause mortality rate in the AS group were significantly reduced by 21.2% and 40.7%, respectively. In patients who were assessed for 3 consecutive years, all of the above variables were high at baseline and were not improved much from implementation of CQAP. In matching analysis, we observed this improvement to be limited in the COPD quality assessment year. CONCLUSIONS: The CQAP by the health insurance bureau has improved the management protocol and prognosis of COPD.

Impact of the Asthma Quality Assessment Program on Burden of Asthma.

BACKGROUND: In 2013, the Korean Health Insurance Review and Assessment Service launched the Asthma Quality Assessment Program (AQAP) to assess whether patients with asthma were well managed. OBJECTIVE: To show the impact of the AQAP on the medical utilization behavior for asthma. METHODS: We used claims data linked with the results of the AQAP performed between July 2013 and June 2017. Asthma was determined when subjects visited hospital for asthma. Subjects who used asthma medications on at least 2 different occasions were assessed by the AQAP (assessed subjects [Ass]; not-assessed subjects [NASs]). The AQAP evaluated conduction rate of the pulmonary function test, routine visit rate, and prescription rate of asthma medications. Primary clinics were classified as "good" and "not-good" clinics according to the results of the AQAP. RESULTS: Of the 4.3 million subjects with asthma screened, about 0.8 million were assessed by the AQAP annually. Both the conduction rate of pulmonary function test and the prescription rate of inhaled corticosteroids in the ASs and NASs were improved. In addition, the risk of admission and yearly all-cause mortality were significantly reduced (risk reduction rate, 17.1 and 24.4%, respectively, both P < .001) in the AS group compared with the NAS group. However, effects of the AQAP varied according to the subgroup, and they were restricted to the assessment term. In addition, the admission rate and all-cause mortality were decreased by 94.7% and 45.3%, respectively, in "good" clinics as opposed to the "not-good" clinics. CONCLUSIONS: Performing the AQAP improved both the management protocol and prognosis of asthma in Korea.

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter. BIOLOGICAL SIGNIFICANCE: QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics.