Small-sample bias and corrections for conditional maximum-likelihood odds-ratio estimators
β Scribed by Greenland S.
- Year
- 2000
- Tongue
- English
- Leaves
- 10
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
A number of small-sample corrections have been proposed for the conditional maximum-likelihood estimator of the odds ratio for matched pairs with a dichotomous exposure. I here contrast the rationale and performance of several corrections, specifically those that generalize easily to multiple conditional logistic regression. These corrections or Bayesian analyses with informative priors may serve as diagnostics for small-sample problems. Points are illustrated with a small exact performance comparison and with anexample from a study of electrical wiring and childhood leukemia. The former comparison suggests that small-sample bias may be more prevalent than commonly realized.Keywords: Bias; Case-control studies; Conditional logistic regression; Cox model; Epidemiologic methods; Likelihood analysis; Logistic models; Matching; Odds ratio; Proportional hazards; Relative risk; Risk assessment.
π SIMILAR VOLUMES
<p>This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990. In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimatio
This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. We cover both theory and practice of nonparametric estimation. The text is novel in its use of maximum penalized likelihood estimation, and the theory of conve
<span>This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven s