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Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information


  • Layla Parast
  • Su-Chun Cheng
  • Tianxi Cai


In recent years, a wide range of markers have become available as potential tools to predict risk or progression of disease. In addition to such biological and genetic markers, short-term outcome information may be useful in predicting long-term disease outcomes. When such information is available, it would be desirable to combine this along with predictive markers to improve the prediction of long-term survival. Most existing methods for incorporating censored short-term event information in predicting long-term survival focus on modeling the disease process and are derived under restrictive parametric models in a multistate survival setting. When such model assumptions fail to hold, the resulting prediction of long-term outcomes may be invalid or inaccurate. When there is only a single discrete baseline covariate, a fully nonparametric estimation procedure to incorporate short-term event time information has been previously proposed. However, such an approach is not feasible for settings with one or more continuous covariates due to the curse of dimensionality. In this article, we propose to incorporate short-term event time information along with multiple covariates collected up to a landmark point via a flexible varying-coefficient model. To evaluate and compare the prediction performance of the resulting landmark prediction rule, we use robust nonparametric procedures that do not require the correct specification of the proposed varying-coefficient model. Simulation studies suggest that the proposed procedures perform well in finite samples. We illustrate them here using a dataset of postdialysis patients with end-stage renal disease.

Suggested Citation

  • Layla Parast & Su-Chun Cheng & Tianxi Cai, 2012. "Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1492-1501, December.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1492-1501 DOI: 10.1080/01621459.2012.721281

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    References listed on IDEAS

    1. Maindonald, John, 2006. "Generalized Additive Models: An Introduction with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(b03).
    2. Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
    3. Jianqing Fan, 2000. "Simultaneous Confidence Bands and Hypothesis Testing in Varying-coefficient Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 715-731.
    4. H rdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 2004. "Bootstrap Inference In Semiparametric Generalized Additive Models," Econometric Theory, Cambridge University Press, vol. 20(02), pages 265-300, April.
    5. Jing Wang & Lijian Yang, 2009. "Efficient and fast spline-backfitted kernel smoothing of additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 663-690, September.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, March.
    7. Osmani, R.S., 1990. "Food Deprivation and Undernutrition in Rural Bangladesh," Research Paper 82, World Institute for Development Economics Research.
    8. Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
    9. Haerdle,Wolfgang & Bowman,Adrian, 1986. "Bootstrapping in nonparametric regression: Local adaptive smoothing and confidence bands," Discussion Paper Serie A 71, University of Bonn, Germany.
    10. M. Ruth & K. Donaghy & P. Kirshen, 2006. "Introduction," Chapters,in: Regional Climate Change and Variability, chapter 1 Edward Elgar Publishing.
    11. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, March.
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    Cited by:

    1. Liang Li & Sheng Luo & Bo Hu & Tom Greene, 0. "Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-22.
    2. repec:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9183-7 is not listed on IDEAS

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