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Towards development of best practice methods of causal inferenceto assess treatment selection biomarkers from non-randomizeddata

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  1. Tez No: 720626
  2. Yazar: HULYA KOCYİGİT
  3. Danışmanlar: DR. KEVİN K. DOBBİN
  4. Tez Türü: Doktora
  5. Konular: Biyoistatistik, Biostatistics
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2022
  8. Dil: İngilizce
  9. Üniversite: The University of Georgia
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 159

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Özet (Çeviri)

In this dissertation, we present three novel contributions, providing a new methodology, examining the proposed method's performances, and extensive the study in literature. The first paper of this dissertation focuses on statistical methods for developing biomarkers that provide integration of reliable indicators of effectiveness for guiding adjuvant chemotherapy treatment selection for cases utilizing the tumor's biological makeup. When we directly attempt to evaluate a biomarker's performance without considering the influence of covariates on treatment assignment, the result can lead to inaccurate evaluation of biomarker performance. To minimize the influence of covariates on treatment, outcome, or both, that can produce bias, we have employed various causal inference methods in a lung cancer dataset. Chapter 3 aims to present the general framework for the treatment selection process in literature, consisting of the intersection of machine learning, causal inference, and biomarkers. We use parametric, and machine learning techniques to estimate propensity scores and then apply pair matching techniques that rely on these scores to adjust the existence of extraneous factors. Different associations between treatment or outcome and covariates are studied and assessed in terms of results in outcome models. After that, we use the results of parametric and machine learning methods to evaluate biomarkers that may be used to identify patients who will benefit from a specific treatment from observational data. In chapter 4, the positivity assumption, which states that the propensity score must be constrained away from 0 and 1, is a crucial criterion for inverse probability weighting estimation. However, when the positivity assumption is violated in propensity score distributions between treatment groups, some weights can be approximately 0 and 1. These weights led to uncertainty, bias and large variance in estimators. We study various techniques to eliminate poor overlap. We propose different levels of nonoverlap scenarios to examine the performance of balance weighting family and generalized propensity score matching across true propensity model and misspecified propensity score models in multiple treatment cases. We present results of different methods of variance estimation when estimating the causal effect. INDEX WORDS: Treatment Selection, Biomarker, Machine Learning, Balance Weighting, Lack of Overlap

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