Dissertation/Thesis Abstract

Identification of risk factors and likelihood of benefit from adjuvant chemotherapy for early stage lung cancer patients
by Chao, Ted, M.S., California State University, Long Beach, 2017, 72; 10264880
Abstract (Summary)

The purpose of the research is to develop a statistical decision support algorithm for patients who may benefit from Adjuvant Cisplatin/Vinorelbine (ACT) and improve their survival rates. Genome-wide microarray data will be used to identify feasible sets of genes and probe sets that constitute the gene signature. The data are available at the National Center for Biotechnology Information Gene Expression Omnibus (GSE14814). Preliminary studies have shown that high risk patients that received ACT resulted in an improved prognosis. However, low risk patients showed no benefit from ACT and the treatment was possibly detrimental to the patient. Studies using random forests models have shown that genomic markers could potentially identify a patient’s risk factor and likelihood to benefit from ACT; however, it was noted that the random forests do not provide an estimate of the strength of the treatment effect, nor is it possible to clearly identify subgroups of patients with similar responses to ACT treatment. Building on this idea, Accelerated Failure Time models are used to predict the probability of benefit from receiving chemotherapy or surgery only and provide a treatment recommendation. We showed that regardless of whether the model recommended chemotherapy or surgery only, patients that followed the treatment recommendation had significantly longer survival times than patients that did not. For new patients, the model can provide the likelihood of benefit for each treatment based on a small number of genomic biomarkers.

Indexing (document details)
Advisor: Moon, Hojin
Commitee: Kim, Sung Eun, Zhou, Tianni
School: California State University, Long Beach
Department: Mathematics and Statistics
School Location: United States -- California
Source: MAI 56/04M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Biostatistics, Statistics
Keywords:
Publication Number: 10264880
ISBN: 9781369773965
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