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Optimal designs for longitudinal trials with discrete-time survival endpoints

Author

Listed:
  • Yun-Juan Wang

    (Shanghai Lixin University of Accounting and Finance, School of Statistics and Mathematics)

  • Xin-Yuan Ji

    (Shanghai University of International Business and Economics, School of Statistics and Data Science)

  • Xiao-Dong Zhou

    (Shanghai University of International Business and Economics, School of Statistics and Data Science)

  • Rong-Xian Yue

    (Shanghai Normal University, College of Mathematics and Science
    Fuyao University of Science and Technology, School of Arts and Sciences)

Abstract

This paper introduces a novel methodological framework for the optimal design of longitudinal trials with discrete-time survival endpoints. We address two critical limitations of existing methodologies: (1) the problematic dependence of model complexity on the number of observation periods, and (2) the narrow focus on treatment effect estimation through D- or $$\hbox {D}_s$$ -optimality, which often neglects important predictive targets such as cumulative incidence functions. We propose a prediction-focused optimal design method based on the extended partial logistic regression model and present rigorous equivalence theorems to validate the optimality of the design. Additionally, we introduce a general $$\hbox {D}_{\varvec{A}}$$ -optimal criterion for comprehensive comparison. Through four carefully constructed case studies encompassing diverse experimental conditions, we systematically examine the characteristics of our proposed designs. The results reveal distinct support patterns and weight allocation preferences between estimation-focused ( $$\hbox {D}_{\varvec{A}}$$ -optimal) designs and prediction-focused ( $$\hbox {I}_{\varvec{ B}}$$ -optimal) designs. This work provides researchers with a more flexible and practical framework for designing discrete-time survival studies that better align with their analytical needs.

Suggested Citation

  • Yun-Juan Wang & Xin-Yuan Ji & Xiao-Dong Zhou & Rong-Xian Yue, 2026. "Optimal designs for longitudinal trials with discrete-time survival endpoints," Computational Statistics, Springer, vol. 41(3), pages 1-32, April.
    • Handle: RePEc:spr:compst:v:41:y:2026:i:3:d:10.1007_s00180-026-01718-6
    DOI: 10.1007/s00180-026-01718-6
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    References listed on IDEAS

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    1. Masoudi, Ehsan & Holling, Heinz & Wong, Weng Kee, 2017. "Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 330-345.
    2. Satya Prakash Singh & Deepak Prajapati, 2025. "Bayesian optimal design for $$2 \times 2$$ 2 × 2 binary crossover trials using copula," Computational Statistics, Springer, vol. 40(9), pages 4875-4900, December.
    3. Xiao-Dong Zhou & Yun-Juan Wang & Rong-Xian Yue, 2018. "Robust population designs for longitudinal linear regression model with a random intercept," Computational Statistics, Springer, vol. 33(2), pages 903-931, June.
    4. Xiao-Dong Zhou & Yun-Juan Wang & Rong-Xian Yue, 2021. "Optimal designs for discrete-time survival models with random effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 300-332, April.
    5. Hao Ji & Hans-Georg Müller, 2017. "Optimal designs for longitudinal and functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 859-876, June.
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