Preserving Patient Privacy in Modeling Multi-Category Outcomes Across Real-World Data Sources / Kenneth Thomas Locke Jr.

Format:

Book

Thesis/Dissertation

Author/Creator:

Locke, Kenneth Thomas, Jr., author.

Contributor:

University of Pennsylvania. Epidemiology and Biostatistics, degree granting institution.

Language:

English

Subjects (All):

Biostatistics.

Bioinformatics.

Information science.

Epidemiology and Biostatistics--Penn dissertations.

Penn dissertations--Epidemiology and Biostatistics.

Local Subjects:

Biostatistics.

Bioinformatics.

Information science.

Epidemiology and Biostatistics--Penn dissertations.

Penn dissertations--Epidemiology and Biostatistics.

Physical Description:

1 online resource (140 pages)

Distribution:

Ann Arbor : ProQuest Dissertations & Theses, 2023

Contained In:

Dissertations Abstracts International 84-12A.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2022.

Language Note:

English

Summary:

Multi-site studies involving data from real-world data sources, such as those from Electronic Health Records (EHR), have been increasingly common in recent years in the study of rare or complex diseases. One challenge of such studies is how data are managed and analyzed in the privacy-preserving setting where individual patient data (IPD) must remain within respective sites. As most statistical analyses require that data be centralized into one site for analysis, this may not be possible when working with sensitive patient information. Distributed algorithms can overcome this obstacle by utilizing only summary level information to model the outcome of interest and obtain results similar to the pooled data analysis. However, some of these algorithms are limited in real-world applications as they either require iterative rounds of communications and/or ignore different levels of heterogeneity between sites. Multi-category outcomes, such as improvement scales and diagnosis codes, are commonly encountered in clinical studies. To date, no communication-efficient distributed algorithms have been developed for modeling ordered and unordered categorical outcomes. In this dissertation, we developed two robust privacy-preserving distributed algorithms for modeling multi-category outcomes in the ordered and unordered category settings that require only two rounds of communication between sites. For our first algorithm, we developed methods under the assumption of homogeneity of data distribution between sites. Our second algorithm concerns the setting where the data distribution is heterogeneous across sites. Through simulations and applied data examples, we demonstrate that both algorithms in ordered and unordered categorical outcome settings are accurate relative to the gold standard analysis conducted over the combined data. Finally, we apply our distributed algorithms to a pediatric multi-site clinical research network (PEDSnet) by modeling clinical outcomes that occur in the two-year postoperative period of Tympanostomy Tube Insertion surgery.

Notes:

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Advisors: Chen, Yong; Landis, J. Richard; Committee members: Yang, Wei Peter; Farrar, John T.; Dedhia, Kavita.

Department: Epidemiology and Biostatistics.

Ph.D. University of Pennsylvania 2023.

Local Notes:

School code: 0175

ISBN:

9798379751661

Access Restriction:

Restricted for use by site license.