Characterizing quantile-varying covariate effects under the accelerated failure time model.
Reeder HT, Lee KH, Haneuse S.
Biostatistics. 2024 04 15. 25(2):449-467. PMID: 36610077
Faculty Affiliate in the Department of Epidemiology
Epidemiology
Harvard T.H. Chan School of Public Health
Faculty Affiliate in the Department of Biostatistics
Biostatistics
Harvard T.H. Chan School of Public Health
I am interested in developing statistical methods for multivariate and/or high-dimensional biomedical data from a wide range of applications:
1. Multivariate statistical methods for microbiome data
One primary focus of my research program is on statistical methods development for microbiome study. One research goal is to develop spatial point pattern analysis methods for understanding the spatial organization of microbes by using spectral imaging data. Another research goal is to develop comprehensive multivariate methods for microbiome sequencing count data. These methods differ from most commonly used techniques in that they involve analyzing the spatial/counts distributions of all microbial types as a joint endpoint distribution, instead of analyzing the univariate distribution of each type separately (taxon-by-taxon analysis). The overarching goal is to provide more robust and valid quantitative analysis tools to scientists in microbiology and bioinformatics.
2. Semi-competing risks framework for multivariate survival data
Semi-competing risks refers to the setting where interest lies in a nonterminal event (e.g. hospital readmission), the occurrence of which is subject to a terminal event (e.g. death). Although less known than competing risks, semi-competing risks problem arises in a broad range of public health applications. I have developed a novel hierarchical modeling framework for the analysis of clustered semi-competing risks survival data. The framework permits parametric or nonparametric specifications for a range of model components, including baseline hazard functions and distributions for key random effects, giving analysts substantial flexibility as they consider their own analyses. I am currently extending the method for various type of study designs to further expand the scope of scientific inquiry from clinical and public health science.
3. Survival analysis with high-dimensional genomic covariates
Developing a predictive model that relates the time-to-event outcome to high-dimensional genomic data is challenging because of i) high-dimensional genomic variables, the number of which often far exceeds the number of subjects, ii) structured grouping of genes, and iii) censored outcomes. I am developing statistical methods for correlated and structured high-dimensional genomic data with survival outcomes in the context of penalized regression models.
Reeder HT, Lee KH, Haneuse S.
Biostatistics. 2024 04 15. 25(2):449-467. PMID: 36610077
Hu Y, Li J, Wang B, Zhu L, Li Y, Ivey KL, Lee KH, Eliassen AH, Chan A, Huttenhower C, Hu FB, Qi Q, Rimm EB, Sun Q.
Gut. 2023 Nov 24. 72(12):2260-2271. PMID: 37739776
Wang F, Tessier AJ, Liang L, Wittenbecher C, Haslam DE, Fernández-Duval G, Heather Eliassen A, Rexrode KM, Tobias DK, Li J, Zeleznik O, Grodstein F, Martínez-González MA, Salas-Salvadó J, Clish C, Lee KH, Sun Q, Stampfer MJ, Hu FB, Guasch-Ferré M.
Nat Commun. 2023 09 16. 14(1):5744. PMID: 37717037
Aljawad H, Kang N, Lee KC.
Angle Orthod. 2023 01 01. 93(1):66-70. PMID: 35895315
Aljawad H, Lim HJ, Lee KC.
J Craniofac Surg. 2023 Jul-Aug 01. 34(5):1456-1458. PMID: 36731044
Haneuse S, Schrag D, Dominici F, Normand SL, Lee KH.
Ann Appl Stat. 2022 Sep. 16(3):1586-1607. PMID: 36483542
Li J, Li Y, Ivey KL, Wang DD, Wilkinson JE, Franke A, Lee KH, Chan A, Huttenhower C, Hu FB, Rimm EB, Sun Q.
Gut. 2022 04. 71(4):724-733. PMID: 33926968
Li Y, Seo S, Lee KH.
J Stat Comput Simul. 2021. 14(91):2937-2952.
Lee KS, Talenfeld AD, Browne WF, Holzwanger DJ, Harnain C, Kesselman A, Pua BB.
Clin Imaging. 2021 Mar. 71:143-146. PMID: 33259979
Lee KH, Coull BA, Moscicki AB, Paster BJ, Starr JR.
Biostatistics. 2020 07 01. 21(3):499-517. PMID: 30590511
People who adhere to the Planetary Health Diet may substantially lower their risk of premature death and their environmental impact, according to a new Harvard Chan School study.
A new Harvard Chan School study has identified a group of metabolites associated with risk of mortality, and another group linked with longevity.
Harvard T.H. Chan School of Public Health welcomed nine new faculty members for the fall semester.