Skip to main content

Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis

Buy Article:

$43.00 plus tax (Refund Policy)

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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Design efficiency; Logistic regression; Nonparametric maximum likelihood; Stratified sampling

Document Type: Original Article

Affiliations: University of Washington, Seattle, USA

Publication date: 1999-01-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more