About Liang Li
I am currently an Associate Professor at Department of Biostatistics, University of Texas MD Anderson Cancer Center.
|2013 - Present||Associate Professor, The University of Texas MD Anderson Cancer Center ‐ Biostatistics|
|2003 - 2013||Assistant/Associate Staff, Cleveland Clinic ‐ Quantitative Health Sciences|
Biostatistics, Joint Modeling of Longitudinal and Survival Data, Dynamic Prediction Models, Propensity Score Analysis, Clinical Trials, Medical Informatics, Machine Learning, Observational Studies, Electronic Health Records, Cancer Research, Chronic Kidney Disease, Health Services Research, and Population Health
Honors and Awards
- Cleveland Clinic Innovator Award, 2007
- Exceptional Reviewer of Medical Care (top 5% of all peer-reviewers), 2008
- Exceptional Reviewer of Medical Care (top 5% of all peer-reviewers), 2009
- Atlantic Causal Inference Conference Thomas R. Ten Have Citation, 2011
- Faculty Recognition Award, MD Anderson Cancer Center, 2016
- Categorical Data Analysis (Rice University Statistics Department STAT 545, Fall, 2015)
- Longitudinal Data Analysis (GSBS 1153, MD Anderson Cancer Center, Spring 2014)
|1998 - 2003||PhD, University of Wisconsin-Madison ‐ Statistics|
|1994 - 1998||BS, Peking University ‐ Cell Biology and Genetics|
Statistical Methodology (31)
Semiparametric Estimation of Longitudinal Medical Cost Trajectory Journal of the American Statistical Association (2017)
Estimating the average monthly medical costs from disease diagnosis to a terminal event such as death for an incident cohort of patients is a topic of immense interest to researchers in health policy and health ...
Boosted multivariate trees for longitudinal data Machine Learning (2017)
Machine learning methods provide a powerful approach for analyzing longitudinal data in which repeated measurements are observed for a subject over time. We boost multivariate trees to fit a novel flexible semi-nonparametric marginal model for ...
Estimating the prevalence of atrial fibrillation from a three-class mixture model for repeated diagnoses Biometrical Journal (2017)
Atrial fibrillation (AF) is an abnormal heart rhythm characterized by rapid and irregular heartbeat, with or without perceivable symptoms. In clinical practice, the electrocardiogram (ECG) is often used for diagnosis of AF. Since the AF ...
Estimating treatment effects in observational studies with both prevalent and incident cohorts The Canadian Journal of Statistics (2017)
Registry databases are increasingly being used for comparative effectiveness research in cancer. Such databases reflect the real-world patient population and physician practice, and thus are natural sources for comparing multiple treatment scenarios and the associated ...
A simple method to estimate the time-dependent receiver operating characteristic curve and the area under the curve with right censored data Statistical Methods in Medical Research (2016)
The time-dependent receiver operating characteristic curve is often used to study the diagnostic accuracy of a single continuous biomarker, measured at baseline, on the onset of a disease condition when the disease onset may occur ...
Dynamic prediction of renal failure using longitudinal biomarkers in a cohort study of chronic renal disease Statistics in Biosciences (2016)
In longitudinal studies, prognostic biomarkers are often measured longitudinally. It is of both scientific and clinical interest to predict the risk of clinical events, such as disease progression or death, using these longitudinal biomarkers as ...
Joint multiple imputation for longitudinal outcomes and clinical events that truncate longitudinal follow-up Statistics in Medicine (2016)
Longitudinal cohort studies often collect both repeated measurements of longitudinal outcomes and times to clinical events whose occurrence precludes further longitudinal measurements. Although joint modeling of the clinical events and the longitudinal data can be ...
Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model Statistical Methods in Medical Research (2016)
Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of ...
A Simple Method to Estimate the Time-dependent ROC Curve Under Right Censoring COBRA Preprint Series (2015)
The time-dependent Receiver Operating Characteristic (ROC) curve is often used to study the diagnostic accuracy of a single continuous biomarker, measured at baseline, on the onset of a disease condition when the disease onset may ...
Using the landmark method for creating prediction models in large datasets derived from electronic health records Health Care Management Science (2015)
With the integration of electronic health records (EHRs), health data has become easily accessible and abounded. The EHR has the potential to provide important healthcare information to researchers by creating study cohorts. However, accessing this ...
A Class of Permutation Tests for the Equality of Two Marginal Survival Functions Using Paired Censored Data Communications in Statistics-Simulation and Computation (2014)
We studied several test statistics for testing the equality of marginal survival functions of paired censored data. The null distribution of the test statistics was approximated by permutation. These tests do not require explicit modeling ...
A Within-Patient Analysis for Time-Varying Risk Factors of CKD Progression Journal of the American Society of Nephrology (2014)
Recent data suggest that nonlinear GFR trajectories are common among patients with CKD, but the modifiable risk factors underlying these changes in CKD progression rate are unknown. Analyses relating baseline risk factors to subsequent GFR ...
Modeling potential time to event data with competing risks Lifetime Data Analysis (2014)
Patients receiving radical prostatectomy are at risk of metastasis or prostate cancer related death, and often need repeated clinical evaluations to determine whether additional adjuvant or salvage therapies are needed. Since the prostate cancer is ...
A Weighting Analogue to Pair Matching in Propensity Score Analysis International Journal of Biostatistics (2013)
Propensity score (PS) matching is widely used for studying treatment effects in observational studies. This article proposes the method of matching weights (MWs) as an analog to one-to-one pair matching without replacement on the PS ...
The balanced survivor average causal effect International Journal of Biostatistics (2013)
Statistical analysis of longitudinal outcomes is often complicated by the absence of observable values in patients who die prior to their scheduled measurement. In such cases, the longitudinal data are said to be "truncated by ...
Longitudinal Progression Trajectory of GFR Among Patients With CKD American Journal of Kidney Diseases (2012)
Background: The traditional paradigm of glomerular filtration rate (GFR) progression in patients with chronic kidney disease (CKD) is a steady nearly linear decline over time. We describe individual GFR progression trajectories over 12 years of ...
Nonparametric multistate representations of survival and longitudinal data with measurement error Statistics in Medicine (2012)
This paper proposes a nonparametric procedure to describe the progression of longitudinal cohorts over time from a population averaged perspective, leading to multistate probability curves with the states defined jointly by survival and longitudinal outcomes ...
Propensity Score Analysis with Matching Weights COBRA Preprint Series (2011)
The propensity score analysis is one of the most widely used methods for studying the causal treatment effect in observational studies. This paper studies treatment effect estimation with the method of matching weights. This method ...
Comment: Analyzing propensity score matched count data International Journal of Biostatistics (2010)
We offer an explanation to the simulation result of Austin (2009) regarding rate ratios, and argue that unmatched analysis of propensity score matched count data results in conservative statistical inferences on the rate ratios.
A semiparametric joint model for longitudinal and survival data with application to hemodialysis study Biometrics (2009)
In many longitudinal clinical studies, the level and progression rate of repeatedly measured biomarkers on each subject quantify the severity of the disease and that subject's susceptibility to progression of the disease. It is of ...