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Jane W. Liang, PhD

jane.w.liang@kp.org

Liang Jane

Jane W. Liang, PhD, is a research scientist and biostatistician at the Kaiser Permanente Northern California Division of Research. Her research interests focus on developing predictive models and tools for clinical risk assessment. As a collaborative researcher, she also provides expertise in study design and statistical methods to the multidisciplinary teams at the Division of Research. Her recent projects include characterizing heterogeneous treatment effects and risk stratifying patients for applications in nephrology and hepatology, and developing multi-gene, multi-cancer models to predict carriers of pathogenic variants in cancer susceptibility genes. She holds a PhD in biostatistics from Harvard University.

Current Positions

  • Research Scientist/Biostatistician, Division of Research, Kaiser Permanente Northern California

Section Affiliations

Primary Research Interests

  • Prediction modeling
  • Clinical risk assessment tools
  • Precision medicine
  • Statistical software

Publications

Gender and Authorship in Annals of Surgery: A nineteen-year review including the pandemic.

Authors: Liang, Jane W;Chang, Marcello;Stein, Sharon L;Salles, Arghavan

Ann Surg Open. 2024 Sep 26;5(4):e491. doi: 10.1097/AS9.0000000000000491. eCollection 2024 Dec.

PubMed abstract

Model for Urgency for Liver Transplantation in Hepatocellular Carcinoma: A Practical Model to Prioritize Patients With Hepatocellular Carcinoma on the Liver Transplant Waiting List.

Authors: Norman, Joshua S;Mehta, Neil;Kim, W Ray;Liang, Jane W;Biggins, Scott W;Asrani, Sumeet K;Heimbach, Julie;Charu, Vivek;Kwong, Allison J

Gastroenterology. 2024 Nov 30:pii: S0016-5085(24)05754-8. doi: 10.1053/j.gastro.2024.11.015..

PubMed abstract

Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD.

Authors: Charu, Vivek;Liang, Jane W;Mannalithara, Ajitha;Kwong, Allison;Tian, Lu;Kim, W Ray

Hepatology. 2024 Nov 01;80(5):1184-1195. doi: 10.1097/HEP.0000000000000908. Epub 2024 Apr 30.

PubMed abstract

Nirmatrelvir-Ritonavir and Symptoms in Adults With Postacute Sequelae of SARS-CoV-2 Infection: The STOP-PASC Randomized Clinical Trial.

Authors: Geng, Linda N;Liang, Jane W;Singh, Upinder;et al.

JAMA Intern Med. 2024 Sep 01;184(9):1024-1034. doi: 10.1001/jamainternmed.2024.2007..

PubMed abstract

Evaluating the utility of multi-gene, multi-disease population-based panel testing accounting for uncertainty in penetrance estimates.

Authors: Liang, Jane W;Christensen, Kurt D;Green, Robert C;Kraft, Peter

NPJ Genom Med. 2024 May 17;9(1):30. doi: 10.1038/s41525-024-00414-y..

PubMed abstract

Heterogeneous Treatment Effects of Intensive Glycemic Control on Kidney Microvascular Outcomes and Mortality in ACCORD.

Authors: Charu, Vivek;Liang, Jane W;Chertow, Glenn M;Li, June;Montez-Rath, Maria E;Geldsetzer, Pascal;de Boer, Ian H;Tian, Lu;Tamura, Manjula Kurella

J Am Soc Nephrol. 2024 Feb 01;35(2):216-228. doi: 10.1681/ASN.0000000000000272. Epub 2023 Dec 11.

PubMed abstract

Barriers to Family Building Among Physicians and Medical Students.

Authors: King, Zoe;Liang, Jane W;Salles, Arghavan;et al.

JAMA Netw Open. 2023 Dec 01;6(12):e2349937. doi: 10.1001/jamanetworkopen.2023.49937..

PubMed abstract

Statistical methods for Mendelian models with multiple genes and cancers.

Authors: Liang, Jane W;Idos, Gregory E;Hong, Christine;Gruber, Stephen B;Parmigiani, Giovanni;Braun, Danielle

Genet Epidemiol. 2022 Oct;46(7):395-414. doi: 10.1002/gepi.22460. Epub 2022 May 18.

PubMed abstract

Sparse matrix linear models for structured high-throughput data

Authors: Liang, Jane W;Sen, Śaunak

Ann. Appl. Stat. 16(1): 169-192 (March 2022). DOI: 10.1214/21-AOAS1444

Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO.

Authors: Lee, Gavin;Liang, Jane W;Zhang, Qing;Huang, Theodore;Choirat, Christine;Parmigiani, Giovanni;Braun, Danielle

Elife. 2021 Aug 18;10:pii: e68699. doi: 10.7554/eLife.68699..

PubMed abstract

Matrix Linear Models for High-Throughput Chemical Genetic Screens.

Authors: Liang, Jane W;Nichols, Robert J;Sen, Śaunak

Genetics. 2019 Aug;212(4):1063-1073. doi: 10.1534/genetics.119.302299. Epub 2019 Jun 26.

PubMed abstract

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