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Novel statistical approaches for practical problems in individualized medicine

Talisa, Victor (2021) Novel statistical approaches for practical problems in individualized medicine. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Conditions such as sepsis are clinically and biologically heterogeneous, often deadly, and yet lack demonstrably effective treatments. In these cases, learning patient-level conditional average treatment effects (CATEs) using methodological and computational advancements could improve trial execution and delivery of care. In this dissertation, I develop methodologies addressing two practical challenges in individualized medicine. The first stems from the existing regulatory procedure for drug development programs. Regulatory agencies usually require two successful demonstrations of efficacy trials before granting approval. One way to improve the likelihood of follow-up success is to identify a subpopulation associated with a treatment benefit, and enroll future studies from this subpopulation. In part 1 of this dissertation, I define Confirmable Responder Class (CRC) learning, where the objective is to find a subpopulation in which the predicted conditional average treatment effect (CATE) suggests benefit, with high power of being confirmed empirically in an independent sample, and strong type 1 error control. I define a set of CRC learners and study their performance across simulated trial datasets. Simulations show that performance depends on population characteristics, and that confirmation rates can be improved by increasing the use of all data in relatively simple ways. A method based on cross-validation is a possible avenue for further development of model selection procedures. In part 2 I address the challenge of estimating CATEs when some covariate information is missing. I design and study 6 new algorithms for estimation and inference of the CATEs in this setting. I describe novel approaches that respond adaptively to missingness in the covariate vector, which could contain elements with different marginal missingness mechanisms. I use simulated datasets to show that the best-performing algorithm is a Bayesian Causal Forest (BCF) model expanded to include a prior on missing data elements, in combination with the Missingness Incorporated in Attributes (MIA) decision tree splitting approach.
Public Health Significance: I develop tools that could be used for effective subgroup identification in the context of missing covariate data. Identifying treatment responders could in turn advance development of tailored treatment approaches for a variety of public health and medical conditions without effective treatments.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Talisa, Victorvit13@pitt.eduVIT13
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorChang, Chung-Chou H.changj@pitt.edu
Committee MemberWahed, Abduswahed@pitt.edu
Committee MemberKang, Chae Ryoncrkang@pitt.edu
Committee MemberYende, Sachinyendes@upmc.edu
Committee MemberDing, Yingyingding@pitt.edu
Date: 19 January 2021
Date Type: Publication
Defense Date: 20 November 2020
Approval Date: 19 January 2021
Submission Date: 10 December 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 102
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Individualized medicine, subgroup identification, conditional average treatment effect, missing data, biostatistics
Date Deposited: 19 Jan 2021 20:01
Last Modified: 19 Jan 2021 20:01
URI: http://d-scholarship.pitt.edu/id/eprint/40063

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