Premium
A General Statistical Framework for Multistage Designs
Author(s) -
GRÜNEWALD MARIA,
HÖSSJER OLA
Publication year - 2012
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2011.00745.x
Subject(s) - mathematics , efficiency , measure (data warehouse) , sampling (signal processing) , square root , statistics , fisher information , construct (python library) , mathematical optimization , computer science , data mining , geometry , filter (signal processing) , estimator , computer vision , programming language
. The efficiency of observational studies may be increased by applying multistage sampling designs. It is, however, not always transparent how to construct such a design to obtain increased efficiency. We here present a general statistical framework for describing and constructing multistage designs. We also provide tools for efficiency and cost‐efficiency comparisons, to facilitate the choice of sampling scheme. The comparisons are based on Fisher information matrices and the results are presented in graphs, where either efficiency or cost‐adjusted efficiency is plotted against a normalized measure of cost. The former curve resides in the unit square and is analogous to the receiver operating characteristic curve used for testing.