The generalized linear models (GLMs) are widely used in statistical analysis and the related design issues are undoubtedly challenging. The state‐of‐the‐art works mostly apply to design criteria on the estimates of regression coefficients. The prediction accuracy is usually critical in modern decision‐making and artificial intelligence applications. It is of importance to study optimal designs from the prediction aspects for GLMs. In this work, we consider Elastic I‐optimality as a prediction‐oriented design criterion for GLMs, and develop an efficient algorithm for such EI‐optimal designs. By investigating theoretical properties for the optimal weights of any set of design points and extending the general equivalence theorem to the EI‐optimality for GLMs, the proposed efficient algorithm adequately combines the Fedorov–Wynn algorithm and the multiplicative algorithm. It achieves great computational efficiency with guaranteed convergence. Numerical examples are conducted to evaluate the feasibility and computational efficiency of the proposed algorithm.
This content will become publicly available on March 7, 2025
This well-studied polymer system does not conform to existing design criteria for imparting mixed conduction. The reasons for this anomalous behaviour are unravelled and used to outline more robust design criteria for new organic semiconductors.
more » « less- Award ID(s):
- 1751308
- PAR ID:
- 10548141
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Journal of Materials Chemistry C
- Volume:
- 12
- Issue:
- 10
- ISSN:
- 2050-7526
- Page Range / eLocation ID:
- 3686 to 3697
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background/Aims: Design of clinical trials requires careful decision-making across several dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial simulation can be an informative tool in trial design, providing empirical evidence by which to evaluate and compare the results of hypothetical trials with varying designs. We introduce a novel simulation-based approach using observational data to inform the design of a future pragmatic trial.
Methods: We utilize propensity score-adjusted models to simulate hypothetical trials under alternative endpoints and enrollment criteria. We apply our approach to the design of pragmatic trials in psoriatic arthritis, using observational data embedded within the Tight Control of Inflammation in Early Psoriatic Arthritis study to simulate hypothetical open-label trials comparing treatment with tumor necrosis factor-α inhibitors to methotrexate. We first validate our simulations of a trial with traditional enrollment criteria and endpoints against a recently published trial. Next, we compare simulated treatment effects in patient populations defined by traditional and broadened enrollment criteria, where the latter is consistent with a future pragmatic trial. In each trial, we also consider five candidate primary endpoints.
Results: Our results highlight how changes in the enrolled population and primary endpoints may qualitatively alter study findings and the ability to detect heterogeneous treatment effects between clinical subgroups. For treatments of interest in the study of psoriatic arthritis, broadened enrollment criteria led to diluted estimated treatment effects. Endpoints with greater responsiveness to treatment compared with a traditionally used endpoint were identified. These considerations, among others, are important for designing a future pragmatic trial aimed at having high external validity with relevance for real-world clinical practice.
Conclusion: Observational data may be leveraged to inform design decisions in pragmatic trials. Our approach may be generalized to the study of other conditions where existing trial data are limited or do not generalize well to real-world clinical practice, but where observational data are available.
