Methods for Clustered Competing Risks Data and Causal Inference using Instrumental Variables for Censored Time-to-event Data

In this dissertation, we propose new methods for analysis of clustered competing risks data (Chapters 1 and 2) and for instrumental variable (IV) analysis of univariate censored time-to-event data and competing risks data (Chapters 3 and 4). In Chapter 1, we propose estimating center effects through cause-specific proportional hazards frailty models that allow correlation among a center’s cause-specific effects. To evaluate center performance, we propose a directly standardized excess cumulative incidence (ECI) measure. We apply our methods to evaluate Organ Procurement Organizations with respect to (i) receipt of a kidney transplant and (ii) death on the wait-list. In Chapter 2, we propose to model the effects of cluster and individual-level covariates directly on the cumulative incidence functions of each risk through a semiparametric mixture component model with cluster-specific random effects. Our model permits joint inference on all competing events and provides estimates of the effects of clustering. We apply our method to multicenter competing risks data. In Chapter 3, we turn our focus to causal inference in the censored time-to-event setting in the presence of unmeasured confounders. We develop weighted IV estimators of the complier average causal effect on the restricted mean survival time. Our method accommodates instrument-outcome confounding and covariate dependent censoring. We establish the asymptotic properties, derive easily implementable variance estimators, and apply our method to compare modalities for end stage renal disease (ESRD) patients using national registry data. In Chapter 4, we develop IV analysis methods for competing risks data. Our method permits simultaneous inference of exposure effects on the absolute risk of all competing events and accommodates exposure dependent censoring. We apply the methods to compare dialytic modalities for ESRD patients with respect to risk of death from (i) cardiovascular diseases and (ii) other causes.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144110/1/shdharma_1.pd

Case-Mix Adjustment of Adherence Based Pharmacy Quality Indicator Scores

Medication adherence has been shown to be influenced by demographics, health status and socio-economic status of the patient. Thus, adherence-based measures of pharmacy quality may be influenced by patient-related risk factors outside of the healthcare provider\u27s control. This study examines the performance of a classical logistic regression model containing only patient characteristics and a random-effect model including patient characteristics and a pharmacy-specific effect in predicting medication adherence. These models were used to compute three different risk-adjusted scores on adherence-based pharmacy quality indicators: based on the classical logistic regression model (Method 1), the random effects model (Method 2) and the shrinkage estimators of the random-effects model (Method 3). Finally, we compared the classification as low, medium or high quality pharmacies based on unadjusted and adjusted scores. This retrospective cohort study used the 2007 Mississippi Medicare administrative claims dataset. Patient medication adherence was measured using the proportion of days covered (PDC) measure for seven therapeutic classes of medications. Pharmacy Quality scores on adherence-based measures were computed for all pharmacies serving Medicare beneficiaries in the state. The logistic regression model and the random-effect model displayed good predictive ability (c-statistic\u3e0.7) for all therapeutic classes. The residual intra class correlation coefficient ranged from 0.008 to 0.012 indicating that although pharmacy level factors may have a significant impact, they may not be as important as patient level factors in determining adherence. Higher levels of agreement was observed between pharmacy classification based on unadjusted scores and risk-adjusted scores obtained from Methods 1 and 2 (0.

Evaluating center performance in the competing risks setting: Application to outcomes of wait‐listed end‐stage renal disease patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142886/1/biom12739_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142886/2/biom12739.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142886/3/biom12739-sup-0001-SuppData.pd

Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794-0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006-0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods

Design and development of ((4-methoxyphenyl)carbamoyl) (5-(5-nitrothiophen-2-yl)-1,3,4-thiadiazol-2-yl)amide analogues as Mycobacterium tuberculosis ketol-acid reductoisomerase inhibitors

Based on our previous finding that the titled compound possesses anti-tuberculosis activity, a series of novel ((4-methoxyphenyl)carbamoyl) (5-(5-nitrothiophen-2-yl)-1,3,4-thiadiazol-2-yl)amide analogues have been synthesized. Amongst the 22 compounds synthesized and tested, 5b, 5c and 6c showed potent inhibitory activity with K values of 2.02, 5.48 and 4.72 μM for their target, Mycobacterium tuberculosis (Mt) ketol-acid reductoisomerase (KARI). In addition, these compounds have excellent in vitro activity against Mt H37Rv with MIC values as low as 1 μM. The mode of binding for these compounds to Mt KARI was investigated through molecular docking and dynamics simulations. Furthermore, these compounds were evaluated for their activity in Mt infected macrophages, and showed inhibitory activities with up to a 1.9-fold reduction in growth (at 10 μM concentration). They also inhibited Mt growth in a nutrient starved model by up to 2.5-fold. In addition, these compounds exhibited low toxicity against HEK 293T cell lines. Thus, these compounds are promising Mt KARI inhibitors that can be further optimized into anti-tuberculosis agents

State-Level Awareness of Chronic Kidney Disease in the U.S.

IntroductionThis study examined state-level variation in chronic kidney disease (CKD) awareness using national estimates of disease awareness among adults in the U.S. with CKD.MethodsData on U.S. adults were obtained from two national, population-based surveys: (1) the Behavioral Risk Factor Surveillance System (BRFSS 2011; n=506,467), a state-level phone survey containing information on self-reported kidney disease; and (2) the National Health and Nutrition Examination Survey (NHANES 2005-2012; n=20,831), containing physical health examination, surveys containing data on self-reported kidney disease, risk factors, and laboratory values. CKD was defined as an estimated glomerular filtration rate of 15-59 mL/minute/1.73 m2 or urinary albumin-to-creatinine ratio >30 mg/g. As BRFSS does not include laboratory data, CKD status for each person was imputed (multiple) based on a logistic regression model predicting NHANES CKD status. CKD awareness in each state was estimated as the weighted proportion of BRFSS participants with imputed CKD who reported having kidney disease.ResultsOverall, estimated CKD awareness was 9.0% (95% CI=8.0%, 10.0%), ranging from 5.8% (95% CI=4.8%, 6.8%) in Iowa to 11.7% (95% CI=9.7%, 13.7%) in Arizona. Awareness was greater among adults with hypertension (12.0%) and diabetes (15.3%) than among adults without those conditions, and lower in Hispanics (6.0%) than in non-Hispanic whites (8.8%), non-Hispanic blacks (9.9%), and other racial/ethnic groups (12.7%).ConclusionsAmong individuals with CKD, awareness of their condition was very low and varied approximately twofold among states. This is the first study to estimate awareness of kidney disease by state for the U.S. adult population