Sheng Luo is an Associate Professor of Biostatistics at Duke University Medical Center. He has received his Ph.D. in Biostatistics from The Johns Hopkins University. He is a leading biostatistician specializing in functional data analysis, statistical methods of longitudinal and survival data, clinical trial design and analysis, and neurological sciences. He is PI of an ongoing NIH/NINDS grant R01NS091307 “Statistical methods for clinical trials with multivariate longitudinal outcomes,” which deals with statistical methodology development for complex longitudinal data in clinical trials. He was PI of an NIH/NINDS grant 5U01NS043127 “Parkinson’s disease clinical trial: Statistical center”, which provides design and analysis to Phase II and III trials in the NET-PD network. He was PI of multiple projects regarding rating scale development, validation, and translation.
1. Luo S, Crainiceanu CM, Louis TA, Chatterjee N. (2008). Analysis of smoking cessation patterns using a stochastic mixed-effects model with a latent cured state. Journal of The American Statistical Association, 103(483), 1002-13, PMID: 19305513, PMCID: 2658598.
2. Luo S, Crainiceanu CM, Louis TA, Chatterjee N. (2009). Bayesian inference for smoking cessation with a latent cure state. Biometrics, 65(3), 970-8, PMID: 19173701, PMCID: 3856570.
3. Crainiceanu CM, Caffo B, Luo S, Zippunikov V, Punjabi NM. (2011). Population value decomposition, a framework for the analysis of image populations. Journal of the American Statistical Association, discussion paper, 106(495), 775-90, PMID: 24415813, PMCID: 3886284.
4. Wang J, Luo S, Li L. (2017). Dynamic prediction for multiple repeated measures and event time data: an application to Parkinson's disease, Annals of Applied Statistics, 11(3), 1787-809, PMCID: 5656296.
5. Li K, O'Brien R, Lutz M, Luo S, (2018). A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data, Alzheimer's \& Dementia: The Journal of the Alzheimer's Association, published online on January 4, 2018, NIHMSID: 923182.