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Analysis of longitudinal data with irregular, outcome‐dependent follow‐up

Author

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  • Haiqun Lin
  • Daniel O. Scharfstein
  • Robert A. Rosenheck

Abstract

Summary. A frequent problem in longitudinal studies is that subjects may miss scheduled visits or be assessed at self‐selected points in time. As a result, observed outcome data may be highly unbalanced and the availability of the data may be directly related to the outcome measure and/or some auxiliary factors that are associated with the outcome. If the follow‐up visit and outcome processes are correlated, then marginal regression analyses will produce biased estimates. Building on the work of Robins, Rotnitzky and Zhao, we propose a class of inverse intensity‐of‐visit process‐weighted estimators in marginal regression models for longitudinal responses that may be observed in continuous time. This allows us to handle arbitrary patterns of missing data as embedded in a subject's visit process. We derive the large sample distribution for our inverse visit‐intensity‐weighted estimators and investigate their finite sample behaviour by simulation. Our approach is illustrated with a data set from a health services research study in which homeless people with mental illness were randomized to three different treatments and measures of homelessness (as percentage days homeless in the past 3 months) and other auxiliary factors were recorded at follow‐up times that are not fixed by design.

Suggested Citation

  • Haiqun Lin & Daniel O. Scharfstein & Robert A. Rosenheck, 2004. "Analysis of longitudinal data with irregular, outcome‐dependent follow‐up," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 791-813, August.
  • Handle: RePEc:bla:jorssb:v:66:y:2004:i:3:p:791-813
    DOI: 10.1111/j.1467-9868.2004.b5543.x
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    File URL: https://doi.org/10.1111/j.1467-9868.2004.b5543.x
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    Citations

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    Cited by:

    1. Lianqiang Qu & Liuquan Sun & Xinyuan Song, 2018. "A Joint Modeling Approach for Longitudinal Data with Informative Observation Times and a Terminal Event," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 609-633, December.
    2. Yingye Zheng & Patrick J. Heagerty, 2007. "Prospective Accuracy for Longitudinal Markers," Biometrics, The International Biometric Society, vol. 63(2), pages 332-341, June.
    3. Cox, D.R. & Kartsonaki, Christiana & Keogh, Ruth H., 2018. "Big data: Some statistical issues," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 111-115.
    4. Benjamin French & Patrick J. Heagerty, 2009. "Marginal Mark Regression Analysis of Recurrent Marked Point Process Data," Biometrics, The International Biometric Society, vol. 65(2), pages 415-422, June.
    5. Sun, Liuquan & Tong, Xingwei, 2009. "Analyzing longitudinal data with informative observation times under biased sampling," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1162-1168, May.
    6. Yu Liang & Wenbin Lu & Zhiliang Ying, 2009. "Joint Modeling and Analysis of Longitudinal Data with Informative Observation Times," Biometrics, The International Biometric Society, vol. 65(2), pages 377-384, June.
    7. Qing Cai & Mei‐Cheng Wang & Kwun Chuen Gary Chan, 2017. "Joint modeling of longitudinal, recurrent events and failure time data for survivor's population," Biometrics, The International Biometric Society, vol. 73(4), pages 1150-1160, December.
    8. Peter McCullagh, 2008. "Sampling bias and logistic models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 643-677, September.
    9. Na Cai & Wenbin Lu & Hao Helen Zhang, 2012. "Time-Varying Latent Effect Model for Longitudinal Data with Informative Observation Times," Biometrics, The International Biometric Society, vol. 68(4), pages 1093-1102, December.
    10. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
    11. Shaun R. Seaman & Daniel Farewell & Ian R. White, 2016. "Linear Increments with Non-monotone Missing Data and Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 996-1018, December.

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