Modeling sensitivity and specificity with a time-varying reference standard within a longitudinal setting

被引：2
|
作者：
Yu, Qin [1 ]
Tang, Wan [1 ,2 ]
Marcus, Sue [3 ]
Ma, Yan [4 ]
Zhang, Hui [1 ]
Tu, Xin [1 ,2 ]
机构：
[1] Univ Rochester, Dept Biostat & Computat Biol, Rochester, NY 14623 USA
[2] Univ Rochester, Dept Psychiat, Rochester, NY 14623 USA
[3] Mt Sinai Sch Med, Dept Psychiat, New York, NY 10029 USA
[4] Cornell Univ, Weill Med Coll, Dept Publ Hlth, New York, NY 10021 USA
来源：
JOURNAL OF APPLIED STATISTICS | 2010年 / 37卷 / 07期
关键词：
augmented inverse probability weighted (AIPW) estimate; bivariate monotone missing data pattern (BMMDP); diagnostic test; double robust estimate; inverse probability weighted (IPW) estimate; missing data; COLLABORATIVE-COCAINE-TREATMENT; BAYESIAN-INFERENCE; NATIONAL-INSTITUTE; SELF-REPORTS; ACCURACY; PERFORMANCE; PREVALENCE; SCORE;
D O I：
10.1080/02664760902998444
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Diagnostic tests are used in a wide range of behavioral, medical, psychosocial, and healthcare-related research. Test sensitivity and specificity are the most popular measures of accuracy for diagnostic tests. Available methods for analyzing longitudinal study designs assume fixed gold or reference standards and as such do not apply to studies with dynamically changing reference standards, which are especially popular in psychosocial research. In this article, we develop a novel approach to address missing data and other related issues for modeling sensitivity and specificity within such a time-varying reference standard setting. The approach is illustrated with real as well as simulated data.
引用
收藏
页码：1213 / 1230
页数：18
相关论文
共 36 条
  • [1] Time-Varying Jamming Modeling and Classification
    Zhang, Lei
    Ren, Jian
    Li, Tongtong
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2012, 60 (07) : 3902 - 3907
  • [2] Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates
    Chen, Qingxia
    May, Ryan C.
    Ibrahim, Joseph G.
    Chu, Haitao
    Cole, Stephen R.
    STATISTICS IN MEDICINE, 2014, 33 (26) : 4560 - 4576
  • [3] The time-varying nature of social media sentiments in modeling stock returns
    Ho, Chi-San
    Damien, Paul
    Gu, Bin
    Konana, Prabhudev
    DECISION SUPPORT SYSTEMS, 2017, 101 : 69 - 81
  • [4] On the time-varying predictive performance of longitudinal biomarkers: Measure and estimation
    Zhang, Jing
    Ning, Jing
    Huang, Xuelin
    Li, Ruosha
    STATISTICS IN MEDICINE, 2021, 40 (23) : 5065 - 5077
  • [5] Time-Varying Aeroelastic Modeling and Analysis for a Morphing Wing
    Yu, Shijie
    Zhou, Xinghua
    Huang, Rui
    AIAA JOURNAL, 2024, 62 (10) : 3825 - 3840
  • [6] Increasing parameter identifiability through clustered time-varying sensitivity analysis
    Wang, Lu
    Xu, Yue-Ping
    Xu, Jiliang
    Gu, Haiting
    Bai, Zhixu
    Zhou, Peng
    Yu, Hongjie
    Guo, Yuxue
    ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 181
  • [7] L1 adaptive controller for nonlinear time-varying reference systems
    Wang, Xiaofeng
    Hovakimyan, Naira
    SYSTEMS & CONTROL LETTERS, 2012, 61 (04) : 455 - 463
  • [8] Time-varying MIMO channels: Parametric statistical Modeling and experimental results
    Wang, Shuangquan
    Abdi, Ali
    Salo, Jari
    El-Sallabi, Hassan M.
    Wallace, Jon W.
    Vainikainen, Pertti
    Jensen, Michael A.
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2007, 56 (04) : 1949 - 1963
  • [9] Modeling and quadratic stabilization of a class of linear uncertain time-varying systems
    Jetto, L.
    Orsini, V.
    Romagnoli, R.
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2017, 27 (10) : 1810 - 1825
  • [10] Physical Function and Survival in Older Adults: A longitudinal study accounting for time-varying effects
    Karunananthan, Sathya
    Moodie, Erica E. M.
    Bergman, Howard
    Payette, Helene
    Diehr, Paula H.
    Wolfson, Christina
    ARCHIVES OF GERONTOLOGY AND GERIATRICS, 2021, 96
1234