[논문]Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness

Kyoung, Yujung; Lee, Keunbaik

doi:10.5351/csam.2015.22.6.589

[국내논문] Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness 원문보기

Communications for statistical applications and methods = 한국통계학회논문집, v.22 no.6, 2015년, pp.589 - 598

Kyoung, Yujung (Department of Statistics, Sungkyunkwan University) , Lee, Keunbaik (Department of Statistics, Sungkyunkwan University)

Abstract ▼ AI-Helper

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized ...

Keyword

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

이론/모형

This paper also develops Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. We also use sensitivity analyses under the MNAR assumption.
The analysis of metabolic syndrome data indicated that most of participants dropped out after the second visit. We fit two presented models using Bayesian methodology. Both identifiable and nonidentifiable parameters are estimated and sensitivity analysis uses series of point mass priors, starting with MAR and the number of sensitivity parameters are reduced based on assumption.

참고문헌 (20)

Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models, Journal of the American Statistical Association, 88, 125-134.
Daniels, M. and Hogan, J. (2008). Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis, TEST, 18, 51-58.
Demirtas, H. and Schafer, J. L. (2003). On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out, Statistics in Medicine, 22, 1113-1121.

상세보기
Diggle, J. and Kenward, M. (1994). Informative dropout in longitudinal data analysis, Journal of the Royal Statistical Society, 43, 49-93.
Gamerman, D. (1997). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Chapman & Hall, London.
Gelfand, A. and Ghosh, S. (1998). Model choice: A minimum posterior predictive loss approach, Biometrika, 53, 1527-1537.
Gelman, A., Carlin, J., Stern, H. and Rubin, D. B. (2004). Bayesian Data Analysis, 2nd Edition, Chapman & Hall, London, UK, 1995.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov Chains and their applications. Biometrika, 57, 97-109.

상세보기
Heckman, J. J. (1974). Shadow wages, market wages and labor supply, Econometrica, 42, 679-693.

상세보기
Hogan, J. and Laird, N. (1997). Model-based approaches to analysing incomplete longitudinal and failure time data, Statistics in Medicine, 16, 259-272.

상세보기
Kenward, M. and Molenberghs, G. (1999). Parametric models for incomplete continuous and categorical longitudinal data, Statistical Methods in Medical Research, 8, 51.

상세보기
Kim, J., Kim, E., Yi, H., Joo, S., Shin, K., Kim, J., Kimm, K. and Shin, C. (2006). Short-term incidence rate of hypertension in Korea middle-aged adults, Journal of Hypertension, 24, 2177-2182.

상세보기
Lee, K., Daniels, M. and Joo, Y. (2013). Flexible marginalized models for bivariate longitudinal ordinal data, Biostatistics, 14, 462-476.

상세보기
Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies, Journal of the American Statistical Association, 90, 1112-1121.

상세보기
Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd edition, John Wiley, New York.
Molenberghs, G. and Kenward, M. (2007). Missing Data in Clinical Studies, Wiley.
Rubin, D. B. (1976). Inference and missing data, Biometrika, 63, 581-590.

상세보기
Rubin, D. B. (1977). Formalizing subjective notions about the effect of nonrespondents in sample surveys, Journal of the American Statistical Association, 72, 538-543.

상세보기
Scharfstein, D. O., Rotnitzky, A. and Robins, J. M. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models, Journal of the American Statistical Association, 94, 448.
Spiegelhalter, D. J., Best, N., Carlin, B. and van der Linde, A. (2002). Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society, Series B, 64, 583-640, 651-653, 655, 658, 661, 671.

상세보기

저자의 다른 논문 :

LOADING...

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증