Su-Chun Cheng

Semiparametric Mixed-Effects Models for Clustered Failure Time Data

The Cox proportional hazards model with a random effect has been proposed for the analysis of data which consist of a large number of small clusters of correlated failure time observations. The class of linear transformation models provides many useful alternatives to the Cox model for analyzing univariate failure time data. In this article, we generalize these models by incorporating random effects, which generate the dependence among the failure times within the cluster, to handle correlated data. Inference and prediction procedures for such random effects models are proposed. They are relatively simple compared with the methods based on the nonparametric maximum likelihood estimators for the Cox frailty model in the literature. Our proposals are illustrated with a data set from a well-known eye study. Extensive numerical studies are conducted to evaluate various robustness properties of the new procedures.

Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records

Objective To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information

In recent years, a wide range of markers have become available as potential tools to predict risk or progression of disease. In addition to such biological and genetic markers, short-term outcome information may be useful in predicting long-term disease outcomes. When such information is available, it would be desirable to combine this along with predictive markers to improve the prediction of long-term survival. Most existing methods for incorporating censored short-term event information in predicting long-term survival focus on modeling the disease process and are derived under restrictive parametric models in a multistate survival setting. When such model assumptions fail to hold, the resulting prediction of long-term outcomes may be invalid or inaccurate. When there is only a single discrete baseline covariate, a fully nonparametric estimation procedure to incorporate short-term event time information has been previously proposed. However, such an approach is not feasible for settings with one or more continuous covariates due to the curse of dimensionality. In this article, we propose to incorporate short-term event time information along with multiple covariates collected up to a landmark point via a flexible varying-coefficient model. To evaluate and compare the prediction performance of the resulting landmark prediction rule, we use robust nonparametric procedures that do not require the correct specification of the proposed varying-coefficient model. Simulation studies suggest that the proposed procedures perform well in finite samples. We illustrate them here using a dataset of postdialysis patients with end-stage renal disease.

Incorporating short-term outcome information to predict long-term survival with discrete markers.

In disease screening and prognosis studies, an important task is to determine useful markers for identifying high-risk subgroups. Once such markers are established, they can be incorporated into public health practice to provide appropriate strategies for treatment or disease monitoring based on each individuals predicted risk. In the recent years, genetic and biological markers have been examined extensively for their potential to signal progression or risk of disease. In addition to these markers, it has often been argued that short-term outcomes may be helpful in making a better prediction of disease outcomes in clinical practice. In this paper we propose model-free non-parametric procedures to incorporate short-term event information to improve the prediction of a long-term terminal event. We include the optional availability of a single discrete marker measurement and assess the additional information gained by including the short-term outcome. We focus on the semi-competing risk setting where the short-term event is an intermediate event that may be censored by the terminal event while the terminal event is only subject to administrative censoring. Simulation studies suggest that the proposed procedures perform well in finite samples. Our procedures are illustrated using a data set of post-dialysis patients with end-stage renal disease.

NATURAL LANGUAGE PROCESSING IMPROVES PHENOTYPIC ACCURACY IN AN ELECTRONIC MEDICAL RECORD COHORT OF TYPE 2 DIABETES AND CARDIOVASCULAR DISEASE

Electronic Medical Records (EMR) use clinical data to enable large-scale clinical studies. We created an EMR cohort of type 2 diabetes (T2D) patients from a large academic hospital system, to enable risk stratification of T2D patients at population scale. We hypothesize that natural language

Lurcher, Cell Death and the Cell Cycle

The mouse neurologic mutant Lurcher carries a semidominant genetic lesion that results in severe neurologic dysfunction (Philips 1960). Classical studies have established that the Lurcher mutation results in cell autonomous death of cerebellar Purkinje cells beginning in the second postnatal week (Caddy and Biscoe 1975). We have examined the expression of terminal markers for Purkinje cell differentiation, including the Kv3.3b potassium channel (Goldman-Wohl et al. 1994), and demonstrated that they are expressed prior to cell death in Lurcher animals. Detailed genetic studies have allowed identification of Lc/Lc homozygotes prior to their death in the first postnatal day, and histologic studies of these animals indicate that Lurcher homozygotes are missing large neurons in several hindbrain nuclei. These studies establish that the Lurcher gene causes dose-dependent cell death of specific neuronal populations following their differentiation in the cerebellum and hindbrain.