서브메뉴
검색
How to Apply Directed Acyclic Graphs to Descriptive, Predictive, and Causal Inference Aims in Epidemiology- [electronic resource]
How to Apply Directed Acyclic Graphs to Descriptive, Predictive, and Causal Inference Aims in Epidemiology- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101217
- ISBN
- 9798379649098
- DDC
- 614.4
- 서명/저자
- How to Apply Directed Acyclic Graphs to Descriptive, Predictive, and Causal Inference Aims in Epidemiology - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Los Angeles., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(138 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Arah, Onyebuchi A.;Nianogo, Roch A.K.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Applied epidemiologists are required to not only address causal aims but descriptive and predictive aims as well. There is a lack of guidance on how to approach aims that are not obviously causal with the causal tools and methods that epidemiologists are often trained in. Directed Acyclic Graphs (DAGs) are used in epidemiology and clinical research to clarify assumptions and illustrate causal questions to inform study design and statistical analysis. However, there is little guidance on the use of DAGs outside of causal inference. This dissertation aims to address this gap by walking through the use of DAGs while navigating and adapting previously developed frameworks. In chapter 1, we provide the background and general approach of the dissertation. In chapters 2-4, we adapt an existing framework to provide guidance on the use of DAGs to address descriptive, predictive, and causal aims, respectively. We demonstrate the application of DAGs by working through an example aim using data from the National Health and Nutrition Examination Survey I (NHANES-I) Epidemiologic Follow-up Study (NHEFS) as used in Causal Inference: What If. Lastly, chapter 5 provides a brief discussion of the similarities and differences in addressing these types of aims. We found that the importance of the target population is prevalent in any type of study. Similarly, selection bias, information bias, and missing data issues can arise in any study whereas confounding may not be as much of a concern in descriptive and some predictive studies. DAGs are useful to communicate and address these uncertainties.
- 일반주제명
- Epidemiology.
- 일반주제명
- Computer science.
- 키워드
- Causal inference
- 키워드
- Descriptive
- 키워드
- Predictive
- 키워드
- NHANES-I
- 기타저자
- University of California, Los Angeles Epidemiology 0357
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016933204
■00520240214101217
■006m o d
■007cr#unu||||||||
■020 ▼a9798379649098
■035 ▼a(MiAaPQ)AAI30526075
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a614.4
■1001 ▼aWickramasekaran, Ranjana Nisha.
■24510▼aHow to Apply Directed Acyclic Graphs to Descriptive, Predictive, and Causal Inference Aims in Epidemiology▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Los Angeles. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(138 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Arah, Onyebuchi A.;Nianogo, Roch A.K.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aApplied epidemiologists are required to not only address causal aims but descriptive and predictive aims as well. There is a lack of guidance on how to approach aims that are not obviously causal with the causal tools and methods that epidemiologists are often trained in. Directed Acyclic Graphs (DAGs) are used in epidemiology and clinical research to clarify assumptions and illustrate causal questions to inform study design and statistical analysis. However, there is little guidance on the use of DAGs outside of causal inference. This dissertation aims to address this gap by walking through the use of DAGs while navigating and adapting previously developed frameworks. In chapter 1, we provide the background and general approach of the dissertation. In chapters 2-4, we adapt an existing framework to provide guidance on the use of DAGs to address descriptive, predictive, and causal aims, respectively. We demonstrate the application of DAGs by working through an example aim using data from the National Health and Nutrition Examination Survey I (NHANES-I) Epidemiologic Follow-up Study (NHEFS) as used in Causal Inference: What If. Lastly, chapter 5 provides a brief discussion of the similarities and differences in addressing these types of aims. We found that the importance of the target population is prevalent in any type of study. Similarly, selection bias, information bias, and missing data issues can arise in any study whereas confounding may not be as much of a concern in descriptive and some predictive studies. DAGs are useful to communicate and address these uncertainties.
■590 ▼aSchool code: 0031.
■650 4▼aEpidemiology.
■650 4▼aComputer science.
■653 ▼aCausal inference
■653 ▼aDescriptive
■653 ▼aDirected acyclic graphs
■653 ▼aPredictive
■653 ▼aNHANES-I
■690 ▼a0766
■690 ▼a0984
■71020▼aUniversity of California, Los Angeles▼bEpidemiology 0357.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933204▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
