Targeted design for adaptive clinical trials via semiparametric model
Abstract: Precision medicine approach that assigns treatment according to an individual’s personal (including molecular) profile is revolutionizing health care. Existing statistical methods for clinical trial design typically assume a known model to estimate characteristics of treatment outcomes, which may yield biased results if the true model deviates far from the assumed one. This article aims to achieve model robustness in a phase II multi-stage adaptive clinical trial design. We propose and study a semiparametric regression mixture model in which the mixing proportions are specified according to the subjects’ profiles, and each sub-group distribution is only assumed to be unimodal for robustness. The regression parameters and the error density functions are estimated by semiparametric maximum likelihood and isotonic regression estimators. The asymptotic properties of the estimates are studied. Simulation studies are conducted to evaluate the performance of the method after a real data analysis.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
Targeted design for adaptive clinical trials via semiparametric model ; volume:17 ; number:2 ; year:2020 ; pages:177-190 ; extent:14
The international journal of biostatistics ; 17, Heft 2 (2020), 177-190 (gesamt 14)
- Urheber
-
Zhang, Hongbin
Yuan, Ao
Tan, Ming T.
- DOI
-
10.1515/ijb-2018-0100
- URN
-
urn:nbn:de:101:1-2502181234441.077424419316
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:37 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Zhang, Hongbin
- Yuan, Ao
- Tan, Ming T.
Ähnliche Objekte (12)
Efficient and adaptive estimation for semiparametric models
Locally Adaptive Fitting of Semiparametric Models to Nonstationary Time Series
Adaptive trials, efficiency, and ethics
Semiparametric models
A comparison of semiparametric approaches to evaluate composite endpoints in heart failure trials
Bayesian semiparametric regression
Semiparametric Specification Testing
Using biomarkers prospectively in adaptive clinical trials
Inference for bivariate extremes via a semi-parametric angular-radial model
Targeted V1 comodulation supports task-adaptive sensory decisions
Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project
Adaptive design methods in clinical trials – a review
Efficient and adaptive estimation for semiparametric models
Locally Adaptive Fitting of Semiparametric Models to Nonstationary Time Series
Adaptive trials, efficiency, and ethics
Semiparametric models
A comparison of semiparametric approaches to evaluate composite endpoints in heart failure trials
Bayesian semiparametric regression
Semiparametric Specification Testing
Using biomarkers prospectively in adaptive clinical trials
Inference for bivariate extremes via a semi-parametric angular-radial model
Targeted V1 comodulation supports task-adaptive sensory decisions
Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project
Adaptive design methods in clinical trials – a review
Efficient and adaptive estimation for semiparametric models
Locally Adaptive Fitting of Semiparametric Models to Nonstationary Time Series
Adaptive trials, efficiency, and ethics
Semiparametric models
A comparison of semiparametric approaches to evaluate composite endpoints in heart failure trials
Bayesian semiparametric regression
Semiparametric Specification Testing
Using biomarkers prospectively in adaptive clinical trials
Inference for bivariate extremes via a semi-parametric angular-radial model
Targeted V1 comodulation supports task-adaptive sensory decisions
Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project