Since the publication of the Framingham algorithm for heart disease, tools that predict disease risk have been increasingly integrated into standards of practice. The utility of algorithms at the population level can serve several purposes in health care decision-making and planning. A population-based risk prediction tool for Diabetes Mellitus (DM) can be particularly valuable for public health given the significant burden of diabetes and its projected increase in the coming years. This thesis addresses various aspects related to diabetes risk in addition to incorporating methodologies that advance the practice of epidemiology. The goal of this thesis is to demonstrate and inform the methods of population-based diabetes risk prediction. This is studied in three components: (I) development and validation of a diabetes population risk tool, (II) measurement and (III) obesity risk. Analytic methods used include prediction survival modeling, simulation, and multilevel growth modeling. Several types of data were analyzed including population healthy survey, health administrative, simulation and longitudinal data. The results from this thesis reveal several important findings relevant to diabetes, obesity, population-based risk prediction, and measurement in the population setting. In this thesis a model (Diabetes Population Risk Tool or DPoRT) to predict 10-year risk for diabetes, which can be applied using commonly-collected national survey data was developed and validated. Conclusions drawn from the measurement analysis can inform research on the influence of measurement properties (error and type) on modeling and statistical prediction. Furthermore, the use of new modeling strategies to model change of body mass index (BMI) over time both enhance our understanding of obesity and diabetes risk and demonstrate an important methodology for future epidemiological studies. Epidemiologists are in need of innovative and accessible tools to assess population risk making these types of risk algorithms an important scientific advance. Population-based prediction models can be used to improve health planning, explore the impact of prevention strategies, and enhance our understanding of the distribution of diabetes in the population. This work can be extended to future studies which develop tools for disease planning at the population level in Canada and to enrich the epidemiologic literature on modeling strategies.

Since the publication of the Framingham algorithm for heart disease, tools that predict disease risk have been increasingly integrated into standards of practice. The utility of algorithms at the population level can serve several purposes in health care decision-making and planning. A population-based risk prediction tool for Diabetes Mellitus (DM) can be particularly valuable for public health given the significant burden of diabetes and its projected increase in the coming years. This thesis addresses various aspects related to diabetes risk in addition to incorporating methodologies that advance the practice of epidemiology. The goal of this thesis is to demonstrate and inform the methods of population-based diabetes risk prediction. This is studied in three components: (I) development and validation of a diabetes population risk tool, (II) measurement and (III) obesity risk. Analytic methods used include prediction survival modeling, simulation, and multilevel growth modeling. Several types of data were analyzed including population healthy survey, health administrative, simulation and longitudinal data. The results from this thesis reveal several important findings relevant to diabetes, obesity, population-based risk prediction, and measurement in the population setting. In this thesis a model (Diabetes Population Risk Tool or DPoRT) to predict 10-year risk for diabetes, which can be applied using commonly-collected national survey data was developed and validated. Conclusions drawn from the measurement analysis can inform research on the influence of measurement properties (error and type) on modeling and statistical prediction. Furthermore, the use of new modeling strategies to model change of body mass index (BMI) over time both enhance our understanding of obesity and diabetes risk and demonstrate an important methodology for future epidemiological studies. Epidemiologists are in need of innovative and accessible tools to assess population risk making these types of risk algorithms an important scientific advance. Population-based prediction models can be used to improve health planning, explore the impact of prevention strategies, and enhance our understanding of the distribution of diabetes in the population. This work can be extended to future studies which develop tools for disease planning at the population level in Canada and to enrich the epidemiologic literature on modeling strategies.

No description available.

Public health practice and quality of medical care rely heavily on the accuracy, precision, and robustness of risk prediction models. Health care providers use risk prediction models to assess a patient’s risk of developing an event during a specified time frame given the patient’s specific characteristics, and subsequently recommend a course of treatment or preventative action. In public health research, risk prediction models are often constructed with common statistical modeling techniques, such as logistic regression for binary outcomes or Cox proportional hazard regression for time-to-event outcomes, and the performance of the model is assessed through internal or external validation, or some combination. Model validation requires statistical and clinical significance and satisfactory baseline or improvement in model calibration and discrimination: calibration quantifies how close predictions are to observed outcomes while discrimination quantifies the model’s ability to distinguish correctly between events and nonevents. Measures for evaluating these qualities include (but are not limited to) Brier score, calibration-in-the-large, proportion of variation (R2), sensitivity and specificity, area under the receiver operating characteristic curve (AUC), discrimination slope, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision theory analytic measures such as net benefit and relative utility. Among these measures exist several interrelationships under certain assumptions, and their estimation and interpretation is an active area of research. The first two parts of this thesis focus on studying the empirical distributions and improving confidence interval (CI) estimation of ∆AUC, NRI, and IDI for both binary event data and time-to-event data. Through data simulation and the comparison of several CI types derived with bootstrapping techniques, we make recommendations for proper estimation in future work and apply our recommendations to real-life Framingham Heart Study data. The third part of this thesis summarizes the many interrelationships and possible redundancies among the measures listed, extends theoretical formulas assuming normal variables for ∆AUC, NRI, and IDI from nested models to non-nested models and to Brier score, and explores the impact of varying discrimination and calibration assumptions on Yates’ and Sanders’ decomposed versions of Brier score through simulation. Lastly, overall conclusions and future directions are presented at the end.

Background: Despite several decades of research, the relevance of body fat and body fat distribution to the risk of cardiovascular disease remains unclear. This thesis aims to investigate associations of body-mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) with risk of first-onset cardiovascular disease under a range of different circumstances. Methods: This thesis used individual records from the Emerging Risk Factors Collaboration to calculate risk ratios, and measures of discrimination and reclassification. 118 prospective studies, involving 1,064,541 participants without known history of cardiovascular disease, had information on BMI at baseline examination. 58 of these studies, involving 221,934 participants, had additional information on waist and hip circumference at baseline examination. Serial measurements made in 42,300 participants from 12 studies with concomitant information on these adiposity measures enabled quantification of within-person variability in BMI, WC and WHR. Results: Cross-sectional analyses demonstrated that although the correlations of adiposity measures differed with one another, BMI, WC and WHR were similarly and importantly associated with mediating cardiovascular risk factors, such as blood pressure, fasting glucose and lipids. Within-person variability was lower in BMI (regression dilution ratio: 0.96) than in WC (0.88) and WHR (0.66). The variability of adiposity measures was not materially influenced by several characteristics, although the variability of WHR varied somewhat by sex, diabetes status and baseline WHR values. 1,064,541 individuals with information on BMI recorded 161,903 deaths or non-fatal cardiovascular outcomes during 15.0 million person-years of follow-up. In analyses adjusted for age, sex and smoking status, BMI had positive and nearly loge-linear associations with coronary heart disease and ischaemic stroke (except at BMI values below 20 kg/m2), which were largely explained by the intermediate risk factors noted above. The association between BMI and non-vascular mortality was curvilinear. Data on 221,934 individuals with complete information on weight, height, and waist and hip circumference (14,297 incident cardiovascular outcomes; 1.87 million person-years of follow-up) demonstrated that BMI, WC and WHR were substantially and similarly related to risk of coronary heart disease and ischaemic stroke. For cardiovascular risk prediction, additional information on BMI, WC or WHR to a prediction model containing conventional risk factors did not importantly improve risk discrimination, nor classification of participants to risk categories of predicted 10-year risk. Conclusions: BMI, WC and WHR are similarly associated with risk of cardiovascular disease, with much of the risk explained by intermediate risk factors. These clinical measures of adiposity do not importantly improve cardiovascular risk prediction when additional information is available on blood pressures, history of diabetes and lipids.

No description available.

Gestational diabetes mellitus (GDM) is the most common metabolic complication in pregnancy and is associated with substantial maternal and neonatal morbidity. The standard of care for GDM in most developed countries is universal mid- to late- pregnancy (24-28 weeks gestation) glucose testing. While earlier diagnosis and treatment could improve pregnancy outcomes, tools for early identification of risk for GDM are not commonly used in practice. Existing models for predicting GDM risk within the first trimester of pregnancy based on maternal risk factors perform only modestly in the clinical setting. Heavy reliance on history of GDM to predict GDM development in the current pregnancy prevents these tools from being applicable to nulliparous women (i.e., women who have never given birth). In order to offer timely preventive intervention and enhanced antenatal care to nulliparous women, we need to be able to accurately identify those at high risk for GDM early in pregnancy. Data from the California Office of Statewide Health Planning and Development Linked Birth File was used to address three aims: 1) improve early pregnancy prediction of GDM risk in nulliparous women through development of a risk factor-based model, 2) conduct a systematic review and meta-analysis assessing the relationship between first trimester prenatal screening biomarker levels and development of GDM, and 3) determine if the addition of first and second trimester prenatal screening biomarkers to risk factor-based models will improve early prediction of GDM in nulliparous women. We developed a clinical prediction model including five well-established risk factors for GDM (race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension). Our model had moderate predictive performance among all nulliparous women, and performed particularly well among Hispanic and Black women when assessed within specific racial/ethnic groups. Our risk prediction model also showed superior performance over the commonly used American College of Obstetricians and Gynecologists (ACOG) screening guidelines, encouraging the prompt incorporation of this tool into preconception and prenatal care. Biomarkers commonly assessed in prenatal screening have been associated with a number of adverse perinatal and birth outcomes. However, reports on the relationship between first trimester measurements of prenatal screening biomarkers and GDM development are inconsistent. Our meta-analysis demonstrated that women who are diagnosed with GDM have lower first trimester multiple of the median (MoM) levels of both pregnancy associated plasma protein-A (PAPP-A) and free β-human chorionic gonadotropin (free β-hCG) than women who remain normoglycemic throughout pregnancy. Findings from our meta-analysis suggested that incorporation of prenatal screening biomarkers in clinical risk prediction models could aid in earlier identification of women at risk of developing GDM. Upon linkage of California Office of Statewide Health Planning and Development Linked Birth File and California Prenatal Screening Program records, we found that decreased levels of first trimester PAPP-A, increased second trimester unconjugated estriol, and increased second trimester dimeric inhibin A were associated with GDM development in nulliparous women. However, the addition of these biomarkers in clinical models did not offer improvements to the clinical utility (i.e., risk stratification) of models including maternal risk factors alone. Our findings demonstrate that incorporation of maternal risk factors in a clinical risk prediction model can more accurately identify nulliparous women at high risk for GDM early in pregnancy compared to current standard practice. The maternal characteristics model we developed is based on clinical history and demographic variables that are already routinely collected by clinicians in the United States so that it may be easily adapted into existing prenatal care practice and screening programs. Future work should focus on evaluating the clinical impact of model implementation on maternal and infant outcomes as well as financial costs to the health care system.

Understanding the relationship between genomic markers and complex disease could have a profound impact on medicine, but the large number of potential markers can make it hard to differentiate true biological signal from noise and false positive associations. A standard approach for relating genetic markers to complex disease is to test each marker for its association with disease outcome by comparing disease cases to healthy controls. It would be cost-effective to use control groups across studies of many different diseases; however, this can be problematic when the controls are genotyped on a platform different from the one used for cases. Since different platforms genotype different SNPs, imputation is needed to provide full genomic coverage, but introduces differential measurement error. In Chapter 1, we consider the effects of this differential error on association tests. We quantify the inflation in Type I Error by comparing two healthy control groups drawn from the same cohort study but genotyped on different platforms, and assess several methods for mitigating this error. Analyzing genomic data one marker at a time can effectively identify associations, but the resulting lists of significant SNPs or differentially expressed genes can be hard to interpret. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways reduces the dimensionality of the problem and could improve models by making them more biologically grounded and interpretable. The kernel machine framework has been proposed to model pathway effects because it allows nonlinear associations between the genes in a pathway and disease risk. In Chapter 2, we propose kernel machine regression under the accelerated failure time model. We derive a pseudo-score statistic for testing and a risk score for prediction using genes in a single pathway. We propose omnibus procedures that alleviate the need to prespecify the kernel and allow the data to drive the complexity of the resulting model. In Chapter 3, we extend methods for risk prediction using a single pathway to methods for risk prediction model using multiple pathways using a multiple kernel learning approach to select important pathways and efficiently combine information across pathways.

The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings the onset time of an observable intermediate event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long term survival. Throughout this dissertation, we examine an approach to incorporate intermediate event information for the prediction of long term survival: the landmark model. In Chapter 1, we use the landmark modeling framework to develop procedures to assess how a patient’s long term survival trajectory may change over time given good intermediate outcome indications along with prognosis based on baseline markers. We propose time-varying accuracy measures to quantify the predictive performance of landmark prediction rules for residual life and provide resampling-based procedures to make inference about such accuracy measures. We illustrate our proposed procedures using a breast cancer dataset. In Chapter 2, we aim to incorporate intermediate event time information for the prediction of survival. We propose a fully non-parametric procedure to incorporate intermediate event information when only a single baseline discrete covariate is available for prediction. When a continuous covariate or multiple covariates are available, we propose to incorporate intermediate event time information using a flexible varying coefficient model. To evaluate the performance of the resulting landmark prediction rule and quantify the information gained by using the intermediate event, we use robust non-parametric procedures. We illustrate these procedures using a dataset of post-dialysis patients with end-stage renal disease. In Chapter 3, we consider improving efficiency by incorporating intermediate event information in a randomized clinical trial setting. We propose a semi-nonparametric two-stage procedure to estimate survival by incorporating intermediate event information observed before the landmark time. In addition, we present a testing procedure using these resulting estimates to test for a difference in survival between two treatment groups. We illustrate these proposed procedures using an AIDS dataset.

Assessment of risk has been a key element in efforts to identify factors associated with disease, to assess potential targets of therapy and enhance disease prevention and treatment. Considerable work has been done to develop methods to identify markers, construct risk prediction models and evaluate such models. This dissertation aims to develop robust approaches for these tasks. In Chapter 1, we present a robust, flexible yet powerful approach to identify genetic variants that are associated with disease risk in genome-wide association studies when some subjects are related. In Chapter 2, we focus on identifying important genes predictive of survival outcome when the number of covariates greatly exceeds the number of observations via a nonparametric transformation model. We propose a rank-based estimator that poses minimal assumptions and develop an efficient