Nonparametric covariate-adjusted response-adaptive design based on a functional urn model

Andrea Ghiglietti, Giacomo Aletti, William F. Rosenberger

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

3 Citazioni (Scopus)

Abstract

In this paper we propose a general class of covariate-adjusted response-adaptive (CARA) designs based on a new functional urn model. We prove strong consistency concerning the functional urn proportion and the proportion of subjects assigned to the treatment groups, in the whole study and for each covariate profile, allowing the distribution of the responses conditioned on covariates to be estimated nonparametrically. In addition, we establish joint central limit theorems for the above quantities and the sufficient statistics of features of interest, which allow to construct procedures to make inference on the conditional response distributions. These results are then applied to typical situations concerning Gaussian and binary responses.
Lingua originaleEnglish
pagine (da-a)1-28
Numero di pagine28
RivistaAnnals of Statistics
DOI
Stato di pubblicazionePubblicato - 2017
Pubblicato esternamente

Keywords

  • Clinical trials
  • covariate-adjusted analysis
  • inference
  • large sample theory
  • personalized medicine
  • randomization

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