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Design and analysis of two‐phase studies with binary outcome applied to Wilms tumour prognosis
Author(s) -
Breslow N. E.,
Chatterjee N.
Publication year - 1999
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00165
Subject(s) - covariate , statistics , stratified sampling , outcome (game theory) , sampling design , logistic regression , nonparametric statistics , sampling (signal processing) , computer science , data mining , mathematics , econometrics , medicine , population , mathematical economics , environmental health , filter (signal processing) , computer vision
Two‐phase stratified sampling is used to select subjects for the collection of additional data, e.g. validation data in measurement error problems. Stratification jointly by outcome and covariates, with sampling fractions chosen to achieve approximately equal numbers per stratum at the second phase of sampling, enhances efficiency compared with stratification based on the outcome or covariates alone. Nonparametric maximum likelihood may result in substantially more efficient estimates of logistic regression coefficients than weighted or pseudolikelihood procedures. Software to implement all three procedures is available. We demonstrate the practical importance of these design and analysis principles by an analysis of, and simulations based on, data from the US National Wilms Tumor Study.