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Neural network on interval‐censored data with application to the prediction of Alzheimer's disease

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  • Tao Sun
  • Ying Ding

Abstract

Alzheimer's disease (AD) is a progressive and polygenic disorder that affects millions of individuals each year. Given that there have been few effective treatments yet for AD, it is highly desirable to develop an accurate model to predict the full disease progression profile based on an individual's genetic characteristics for early prevention and clinical management. This work uses data composed of all four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, including 1740 individuals with 8 million genetic variants. We tackle several challenges in this data, characterized by large‐scale genetic data, interval‐censored outcome due to intermittent assessments, and left truncation in one study phase (ADNIGO). Specifically, we first develop a semiparametric transformation model on interval‐censored and left‐truncated data and estimate parameters through a sieve approach. Then we propose a computationally efficient generalized score test to identify variants associated with AD progression. Next, we implement a novel neural network on interval‐censored data (NN‐IC) to construct a prediction model using top variants identified from the genome‐wide test. Comprehensive simulation studies show that the NN‐IC outperforms several existing methods in terms of prediction accuracy. Finally, we apply the NN‐IC to the full ADNI data and successfully identify subgroups with differential progression risk profiles. Data used in the preparation of this article were obtained from the ADNI database.

Suggested Citation

  • Tao Sun & Ying Ding, 2023. "Neural network on interval‐censored data with application to the prediction of Alzheimer's disease," Biometrics, The International Biometric Society, vol. 79(3), pages 2677-2690, September.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2677-2690
    DOI: 10.1111/biom.13734
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    References listed on IDEAS

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    1. Shu Jiang & Yijun Xie & Graham A. Colditz, 2021. "Functional ensemble survival tree: Dynamic prediction of Alzheimer’s disease progression accommodating multiple time‐varying covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 66-79, January.
    2. Peijie Wang & Danning Li & Jianguo Sun, 2021. "A pairwise pseudo‐likelihood approach for left‐truncated and interval‐censored data under the Cox model," Biometrics, The International Biometric Society, vol. 77(4), pages 1303-1314, December.
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    4. Qingning Zhou & Tao Hu & Jianguo Sun, 2017. "A Sieve Semiparametric Maximum Likelihood Approach for Regression Analysis of Bivariate Interval-Censored Failure Time Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 664-672, April.
    5. Fei Gao & Kwun Chuen Gary Chan, 2019. "Semiparametric regression analysis of length‐biased interval‐censored data," Biometrics, The International Biometric Society, vol. 75(1), pages 121-132, March.
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