Personalized Dose Finding Using Outcome Weighted Learning

In dose-finding clinical trials, it is becoming increasingly important to account for individual-level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the expected beneficial clinical outcome for each individual. In this article, we advoc...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the American Statistical Association Jg. 111; H. 516; S. 1509 - 1521
Hauptverfasser: Chen, Guanhua, Zeng, Donglin, Kosorok, Michael R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Taylor & Francis 01.12.2016
Taylor & Francis Group,LLC
Taylor & Francis Ltd
Schlagworte:
Algorithms
Candidates
Clinical outcomes
Clinical research
Clinical trials
Cohort analysis
cohort studies
Convergence
DC algorithm
Dosage
Dose finding
Drug therapy
drugs
equations
Individual differences
Individualized dose rule
Learning
Regression analysis
Risk bound
Simulation
Statistics
Theory and Methods
Warfarin
Weighted support vector regression
Dose Finding
DC Algorithm
Individualized Dose Rule
Risk Bound
Weighted Support Vector Regression
ISSN:0162-1459, 1537-274X, 1537-274X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In dose-finding clinical trials, it is becoming increasingly important to account for individual-level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the expected beneficial clinical outcome for each individual. In this article, we advocate a randomized trial design where candidate dose levels assigned to study subjects are randomly chosen from a continuous distribution within a safe range. To estimate the optimal IDR using such data, we propose an outcome weighted learning method based on a nonconvex loss function, which can be solved efficiently using a difference of convex functions algorithm. The consistency and convergence rate for the estimated IDR are derived, and its small-sample performance is evaluated via simulation studies. We demonstrate that the proposed method outperforms competing approaches. Finally, we illustrate this method using data from a cohort study for warfarin (an anti-thrombotic drug) dosing. Supplementary materials for this article are available online.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2016.1148611