Linear measurement error models with restricted sampling

Biometrics

Volumn 63, Issue 1, 2007, Pages 137-142

  Gorfine, Malka ^a,b   Lipshtat, Nurit ^b   Freedman, Laurence S ^b   Prentice, Ross L ^c  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b BAR ILAN UNIVERSITY   (Israel)

^c Fred Hutchinson Cancer Research Center   (United States)

Author keywords

Measurement error; Missing data; Multiple imputation for longitudinal data; Nutritional epidemiology; Restricted sample

Indexed keywords

LINEAR REGRESSION; MEASUREMENT ERRORS; NUTRIENTS; NUTRITION; RISK ASSESSMENT;

DIETARY INTAKES; LINEAR MEASUREMENTS; LONGITUDINAL DATA; MEASUREMENT ERROR MODELS; MISSING DATA; MULTIPLE IMPUTATION; MULTIPLE IMPUTATION FOR LONGITUDINAL DATA; NUTRIENT CONSUMPTION; NUTRITIONAL EPIDEMIOLOGY; RESTRICTED SAMPLE;

SENSITIVITY ANALYSIS;

ASSESSMENT METHOD; DIET; EPIDEMIOLOGY; ERROR ANALYSIS; FOOD INTAKE; HEALTH RISK; MEASUREMENT METHOD; MODELING; NUTRIENT UPTAKE; REGRESSION ANALYSIS; SAMPLING; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS;

ARTICLE; CALORIC INTAKE; DIETARY INTAKE; ERROR; LINEAR REGRESSION ANALYSIS; LONGITUDINAL STUDY; METHODOLOGY; MONTE CARLO METHOD; NUTRIENT; QUESTIONNAIRE; SENSITIVITY ANALYSIS; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; SYSTEMATIC ERROR; TECHNIQUE;

EID: 34247260985     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2006.00624.x     Document Type: Article

References (15)

1. Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian Journal of Statistics 12, 171-178.
2. Carroll, R. J., Ruppert, D., and Stefanski, L. A. (1995). Measurement Error in Nonlinear Models. London: Chapman & Hall.
3. Carroll, R. J., Freedman, L., and Pee, D. (1997). Design aspects of calibration studies in nutrition, with analysis of missing data in linear measurement error models. Biometrics 53, 1440-1457.
4. Collins, L. M., Schafer, J. L., and Kam, C. M. (2001). A comparison of inclusive and restrictive strategies in modern missing-data procedures. Psychological Methods 6, 330-351.
5. Freedman, L. S., Schatzkin, A., and Wax, Y. (1991). The impact of dietary measurement error on planning size required in a cohort. American Journal of Epidemiology 134, 1472-1473.
6. Kipnis, V., Carroll, R. J., Freedman, L. S., and Li, L. (1999). Implications of a new dietary measurement error model for estimation of relative risk: Application to four calibration studies. American Journal of Epidemiology 150, 642-651.
7. Kipnis, V., Subar, F., Midthune, D., Freedman, L. S., Ballard-Barbash, R., Troiano, P., Bingham, S., Schoeller, D. A., Schatzkin, A., and Carroll, R. J. (2003). Structure of dietary measurement error: Results of the OPEN biomarker study. American Journal of Epidemiology 158, 14-21.
8. Meng, X. L. (1994). Multiple-imputation inferences with uncongenial sources of input (with discussion). Statistical Science 9, 538-558.
9. Prentice, R. L. (1996). Measurement error and results from analytic epidemiology: Dietary fat and breast cancer. Journal of the National Cancer Institute 88, 1738-1747.
10. Prentice, R. L., Sugar, E., Wang, C. Y., Neuhouser, M., and Patterson, R. (2002). Research strategies and the use of nutrient biomarkers in studies of diet and chronic disease. Public Health Nutrition 5, 977-984.
11. Rubin, D. R. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association 91, 473-489.
12. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.
13. Schafer, J. L. and Yucel, R. C. (2002). Computational strategies for multivariate linear mixed models with missing values. Journal of Computational and Graphical Statistics 11, 437-457.
14. Wang, C. Y., Anderson, G. L., and Prentice, R. L. (1999). Estimation of the correlation between nutrient intake measures under restricted sampling. Biometrics 55, 711-717.
15. Women's Health Initiative Study Group. (1998). Design of the Women's Health Initiative clinical trial and observational study. Controlled Clinical Trials 19, 61-109.