QUANTILE REGRESSION MODELS FOR INTERVAL-CENSORED FAILURE TIME DATA

Quantile regression models the conditional quantile as a function of independent variables providing a complete association between the response and predictors. Quantile regression can describe the association at different quantiles yielding more information than the least squares method which only detects associations with the conditional mean. Quantile regression models have gained popularity in many disciplines including medicine, finance, economics, and ecology as they can accommodate heteroscedasticity. A specific type of failure time data is called interval-censored where the failure time is only known to have occurred between certain observation times. Such data appears commonly in medical or longitudinal studies because disease onset is known to have occurred between scheduled visits but the exact time is unknown. Quantile regression has been extended to survival analysis with random censoring time. Most methods focus on survival analysis with right-censored data while a few were developed for data with other censoring mechanisms. Despite the fact that the development for censored quantile regression flourishes, limited work has been done to handle interval-censored failure time data under the quantile regression framework. In this dissertation, we developed a new method to analyze interval-censored failure time data using conditional quantile regression models. Our method can handle both Case I and Case II interval-censored data and allow the censoring time to depend on covariates. We developed an estimation procedure that is computationally efficient and easy to implement with inference performed using a subsampling method. The consistency and asymptotic distribution of the resulting estimators were established using modern empirical process theory. The developed method was extended as a computational tool to analyze interval-censored data for accelerated failure time models. The estimators from different quantiles were combined to increase the efficiency of the estimators. The small sample performances were demonstrated via simulation studies. The proposed methods were illustrated with current status datasets, data from the Voluntary HIV-1 Counseling and Testing Efficacy Study Group and calcification study, and Case II interval-censored data, data from the Atherosclerosis Risk in Communities Study and breast cosmesis data.Doctor of Philosoph

Quantile regression models for current status data

Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging

Evaluation of intratumoral response heterogeneity in metastatic colorectal cancer and its impact on patient overall survival : findings from 10,551 patients in the ARCAD database

Metastatic colorectal cancer (mCRC) is a heterogeneous disease that can evoke discordant responses to therapy among different lesions in individual patients. The Response Evaluation Criteria in Solid Tumors (RECIST) criteria do not take into consideration response heterogeneity. We explored and developed lesion-based measurement response criteria to evaluate their prognostic effect on overall survival (OS). Patients and Methods: Patients enrolled in 17 first-line clinical trials, who had mCRC with ≥ 2 lesions at baseline, and a restaging scan by 12 weeks were included. For each patient, lesions were categorized as a progressing lesion (PL: > 20% increase in the longest diameter (LD)), responding lesion (RL: > 30% decrease in LD), or stable lesion (SL: neither PL nor RL) based on the 12-week scan. Lesion-based response criteria were defined for each patient as follows: PL only, SL only, RL only, and varied responses (mixture of RL, SL, and PL). Lesion-based response criteria and OS were correlated using stratified multivariable Cox models. The concordance between OS and classifications was measured using the C statistic. Results: Among 10,551 patients with mCRC from 17 first-line studies, varied responses were noted in 51.6% of patients, among whom, 3.3% had RL/PL at 12 weeks. Among patients with RL/SL, 52% had stable disease (SD) by RECIST 1.1, and they had a longer OS (median OS (mOS) = 19.9 months) than those with SL only (mOS = 16.8 months, HR (95% CI) = 0.81 (0.76, 0.85), p < 0.001), although a shorter OS than those with RL only (mOS = 25.8 months, HR (95% CI) = 1.42 (1.32, 1.53), p < 0.001). Among patients with SL/PL, 74% had SD by RECIST 1.1, and they had a longer OS (mOS = 9.0 months) than those with PL only (mOS = 8.0 months, HR (95% CI) = 0.75 (0.57, 0.98), p = 0.040), yet a shorter OS than those with SL only (mOS = 16.8 months, HR (95% CI) = 1.98 (1.80, 2.18), p < 0.001). These associations were consistent across treatment regimen subgroups. The lesion-based response criteria showed slightly higher concordance than RECIST 1.1, although it was not statistically significant. Conclusion: Varied responses at first restaging are common among patients receiving first-line therapy for mCRC. Our lesion-based measurement criteria allowed for better mortality discrimination, which could potentially be informative for treatment decision-making and influence patient outcomes

Quantifying the Learning Curve in the Use of a Novel Vascular Closure Device An Analysis of the NCDR (National Cardiovascular Data Registry) CathPCI Registry

ObjectivesThis study sought to quantify the learning curve for the safety and effectiveness of a newly introduced vascular closure device through evaluation of the NCDR (National Cardiovascular Data Registry) CathPCI clinical outcomes registry.BackgroundThe impact of learning on the clinical outcomes complicates the assessment of the safety and efficacy during the early experience with newly introduced medical devices.MethodsWe performed a retrospective analysis of the relationship between cumulative institutional experience and clinical device success, defined as device deployment success and freedom from any vascular complications, for the StarClose vascular closure device (Abbott Vascular, Redwood City, California). Generalized estimating equation modeling was used to develop risk-adjusted clinical success predictions that were analyzed to quantify learning curve rates.ResultsA total of 107,710 procedures used at least 1 StarClose deployment, between January 1, 2006, and December 31, 2007, with overall clinical success increasing from 93% to 97% during the study period. The learning curve was triphasic, with an initial rapid learning phase, followed by a period of declining rates of success, followed finally by a recovery to a steady-state rate of improved device success. The rates of learning were influenced positively by diagnostic (vs. percutaneous coronary intervention) procedure use and teaching status and were affected inversely by annual institutional volume.ConclusionsAn institutional-level learning curve for the initial national experience of StarClose was triphasic, likely indicating changes in patient selection and expansion of number of operators during the initial phases of device adoption. The rate of learning was influenced by several institutional factors, including overall procedural volume, utilization for percutaneous coronary intervention procedures, and teaching status

Development and Characterization of Polymorphic Microsatellite Markers (SSRs) for an Endemic Plant, Pseudolarix amabilis (Nelson) Rehd. (Pinaceae)

Pseudolarix (Pinaceae) is a vulnerable (sensu IUCN) monotypic genus restricted to southeastern China. To better understand levels of genetic diversity, population structure and gene flow among populations of P. amabilis, we developed five compound SSR markers and ten novel polymorphic expressed sequence tags (EST) derived microsatellites. The results showed that all 15 loci were polymorphic with the number of alleles per locus ranging from two to seven. The expected and observed heterozygosities varied from 0.169 to 0.752, and 0.000 to 1.000, respectively. The inbreeding coefficient ranged from −0.833 to 1.000. These markers will contribute to research on genetic diversity and population genetic structure of P. amabilis, which in turn will contribute to the species conservation

Patient- and Trial-Specific Barriers to Participation in Cardiovascular Randomized Clinical Trials

OBJECTIVES: The purpose of this study was to quantitatively examine the association of patient- and trial-specific factors with participation in cardiovascular randomized clinical trials. BACKGROUND: Randomized clinical trials are central to evidenced-based medicine, but low patient participation rates and potentially modifiable barriers are not well understood. METHODS: At a large U.S. academic health system, we examined screening logs from December 1, 2005, to February 28, 2011, from 15 cardiovascular randomized clinical trials. We identified 655 patients who were screened and potentially eligible for participation in at least 1 trial. We used multivariable Poisson regression to quantify the risk of not participating in a trial associated with patient- and trial-specific factors. RESULTS: The median age was 63 years (interquartile range: 54 to 72), 35% were women, and the median Charlson Index was 2 (interquartile range: 1 to 5). Forty-two percent of patients did not participate in a trial. In multivariable regression (C-Index 0.85), trial-specific factors strongly associated with not participating included intensive trial-related testing (relative risk [RR]: 1.89; 95% confidence interval [CI]: 1.63 to 2.20) and anticipated trial participation >6 months (RR: 4.10; 95% CI: 2.30 to 7.29). Patient-specific factors associated with not participating included older age (RR: 1.23; 95% CI: 1.11 to 1.36, per 10-year increase if age ≥65 years), out-of-state residence (RR: 1.26; 95% CI: 1.04 to 1.54), and female sex (RR: 1.17; 95% CI: 1.01 to 1.35). Race was not associated with participation. CONCLUSIONS: While patient-specific factors were associated with not participating in cardiovascular trials, longer trial duration and intensive trial-related testing were most strongly associated with risk for patients not participating. Innovative trial designs fostering convenience may most enhance trial participation

Preparation of Cu/Sr3Ti2O7 and its photocatalytic activity of water-splitting for hydrogen evolution

Sr3Ti2O7 photocatalyst with perovskite-layered structure was synthesized by polymerized complex method (PCM). Cu ion as an effective dopant was loaded onto Sr3Ti2O7 catalyst. Cu/Sr3Ti2O7 catalyst was applied in the mixture of water and methanol, methanol was used as a sacrificial agent under ultra-violet irradiation, and the catalyst was characterized by XPS, XRD, and UV-Vis DRS. The results showed that Cu existed in several kinds of valence and the photocatalytic activity of Cu/Sr3Ti2O7 was superior to that of pure Sr3Ti2O7, Cu+ and adsorbed oxygen can accelerate the interfacial electron transfer. When the amount of Cu was 1.5%(w), the best catalytic effect was obtained and the stable average hydrogen evolution rate was about 550-600 mu mol.h(-1). The Cu/Sr3Ti2O7 after reduction attained the highest hydrogen evolution rate that was close to 1140.8 mu mol.h(-1)

Impact of a Rapid Microarray-Based Assay for Identification of Positive Blood Cultures for Treatment Optimization for Patients with Streptococcal and Enterococcal Bacteremia

Implementation of the Verigene Gram-positive blood culture test led to reductions in time to acceptable antibiotic overall (1.9 versus 13.2 h, respectively; P = 0.04) and time to appropriate antibiotic for patients with vancomycin-resistant Enterococcus (4.2 versus 43.7 h; P = 0.006) and viridans group Streptococcus (0.2 versus 7.1 h; P = 0.02)