Conditional independence models for epidemiological studies with covariate measurement error

Statistics in Medicine

Volumn 12, Issue 18, 1993, Pages 1703-1722

  Richardson, Sylvia ^a   Gilks, Walter R ^b  

^a INSERM   (France)

^b MRC BIOSTATISTICS UNIT   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; BAYES THEOREM; EPIDEMIOLOGY; ERROR; MEASUREMENT; SAMPLING; STATISTICAL MODEL;

ANALYSIS OF VARIANCE; BREAST NEOPLASMS; COHORT STUDIES; DATA INTERPRETATION, STATISTICAL; DISEASE; EPIDEMIOLOGIC METHODS; FEMALE; FOOD HABITS; HUMAN; MODELS, STATISTICAL; PROSPECTIVE STUDIES; RISK FACTORS;

EID: 0027317883     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4780121806     Document Type: Article

References (46)

1. An overview of issues related to the correction of non‐differential exposure measurement error in epidemiologic studies
2. A bibliography and comments on the use of statistical models in epidemiology in the 1980s
3. The effect of misclassification in the presence of covariates
4. Adjustment for non‐differential misclassification error in the generalized linear model
5. Semiparametric estimation in logistic measurement error models
6. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
7. Monte‐Carlo sampling methods using Markov chains and their applications
8. Bayesian analysis of generalised linear models with covariate measurement error
9. Models for the analysis of cohort and case‐control studies with inaccurately measured exposures
10. The use of dual or multiple reports in epidemiologic studies
11. Measurement error in the generalized linear model
12. Covariate measurement error in genealised linear models
13. Correction of logistic regression relative risk estimates and confidence intervals for systematic within‐person measurement error
14. Covariate measurement error in logistic regression
15. Approximations for regression with covariate measurement error
16. The effect of measurement error
17. Approximation quasi‐likelihood estimation in models with surrogate predictors
18. A general approach to analysing epidemiological data that contain misclassification error
19. A review of methods for misclassified categorical data in epidemiology
20. External validation, repeat determination and precision of risk estimation in misclassified exposure data in epidemiology
21. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariate measured with error
22. Independence properties of directed Markov fields
23. Sampling based approaches to calculating marginal densities
24. Illustration of Bayesian inference in normal data models using Gibbs sampling
25. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
26. Modelling complexity: applications of Bibbs sampling in medicine
27. Spatial statistics and Bayesian computation
28. Adaptive rejection samplng for Gibbs sampling
29. Derivative‐free adaptive rejection sampling for Gibbs sampling
30. Efficient generation of random variates via the ratio‐of‐uniforms method
31. Exploring posterior distributions using Markov chains
32. Equations of state calculations by fast computing machine
33. How many iterations of the Gibbs sampler?
34. Evaluating the accuracy of sampling based approaches to the calculation of posterior moments
35. Inferences from Iterative Simulation using multiple sequences
36. Practical Markov Chain Monte‐Carlo
37. Covariance analysis in generalized linear measurement error models
38. A measurement‐error model for binary and ordinal regression
39. Analysis of disease risks using ancillary risk factors, with application to job‐exposure matrices
40. The effects of measurement errors on relative risk regression
41. Difficulties and ambiguities in the definition of a likelihood function
42. BUGS: a program to perform Bayesian inference using Gibbs sampling