Inference for the overlap coefficient based on P-splines and Dirichlet process mixtures

  1. Javier E. Garrido Guillén 1
  2. Vanda Inácio
  3. María Xosé Rodríguez Álvarez
  1. 1 University of Edinburgh

    University of Edinburgh

    Edimburgo, Reino Unido


Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain
  1. Itziar Irigoien (ed. lit.)
  2. Dae-Jin Lee (ed. lit.)
  3. Joaquín Martínez-Minaya (ed. lit.)
  4. María Xosé Rodríguez- Álvarez (ed. lit.)

Editorial: Servicio Editorial = Argitalpen Zerbitzua ; Universidad del País Vasco = Euskal Herriko Unibertsitatea

ISBN: 978-84-1319-267-3

Ano de publicación: 2020

Páxinas: 91-95

Congreso: International Workshop on Statistical Modelling (35. 2020. Bilbao)

Tipo: Achega congreso


Accurate diagnosis of disease is of great importance in clinical practice and medical research. Before a diagnostic test is routinely used in practice its ability to discriminate between diseased and nondiseased states must be rigorously assessed. Further, its performance may depend on covariates (e.g., age and/or gender). This motivates us to propose the covariate-specific overlap coefficient, which will help to determine the optimal populations where to perform the tests on. We assume a location-scale regression model for the test outcomes in each group, relying on an additive formulation based on Penalised splines, while the regression error follows a Dirichlet process mixture of normal distributions. Our approach is illustrated through an application concerning diagnosis of diabetes.