Priors, parameters, and probability: A Bayesian perspective on sensitivity analysis

Epidemiology

Volumn 25, Issue 6, 2014, Pages 910-912

  Gustafson, Paul ^a   McCandless, Lawrence ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAYES THEOREM; CONFIDENCE INTERVAL; DIABETES MELLITUS; GREENLAND; HUMAN; MEASUREMENT ERROR; NORMAL DISTRIBUTION; NOTE; OBESITY; PARAMETERS; PREVALENCE; PRIORITY JOURNAL; PROBABILITY; RANDOM ERROR; RISK; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; SIMULATION; SYSTEMATIC ERROR; DIABETES MELLITUS, TYPE 2; EPIDEMIOLOGY; STATISTICAL BIAS;

BIAS (EPIDEMIOLOGY); CAUSALITY; DIABETES MELLITUS, TYPE 2; HUMANS; OBESITY;

EID: 84927724882     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0000000000000179     Document Type: Note

References (15)

1. Johnson CY, Flanders WD, Strickland MJ, Honein MA, Howards PP. Potential sensitivity of bias analysis results to incorrect assumptions of nondifferential or differential binary exposure misclassification. Epidemiology. 2014.
2. Greenland S, Gustafson P. Accounting for independent nondifferential misclassification does not increase certainty that an observed association is in the correct direction. Am J Epidemiol. 2006;164:63-68.
3. Lash TL, Fox MP, Fink AK. Applying Quantitative Bias Analysis to Epidemiologic Data. New York: Springer; 2011.
4. Gustafson P, Greenland S. Interval estimation for messy observational data. Stat Sci. 2009;24:328-342.
5. Der Simonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177-188.
6. Johnson WD, Bouchard C, Newton Jr RL, Ryan DH, Katzmarzyk PT. Ethnic differences in self-reported and measured obesity. Obesity. 2009;17:571-577.
7. Brunner Huber LR. Validity of self-reported height and weight in women of reproductive age. Matem Child Health J. 2007;11:137-144.
8. Hussain S, Taylor M, Waltermaurer E, et al. Computer-administered screening of reproductive-aged women for diabetes risk in primary care settings, feasibility and acceptability of such screening, and validity of risk assessments based on self-reported weight. Prev Chronic Dis. 2007;4:A54.
9. Nieto-Garcia FJ, Bush TL, Keyl PM. Body mass definitions of obesity: sensitivity and specificity using self-reported weight and height. Epidem iology. 1990;1:146-152.
10. Greenland S. Multiple-bias modelling for analysis of observational data. JR Stat Soc Ser A. 2005;168:267-306.
11. Greenland S. Relaxation penalties and priors for plausible modeling of nonidentified bias sources. Stat Sci. 2009;24:195-210.
12. Chu R, Gustafson P, Le N. Bayesian adjustment for exposure misclassification in case-control studies. Stat Med. 2010;29:994-1003.
13. Krul AJ, Daanen HA, Choi H. Self-reported and measured weight, height and body mass index (BMI) in Italy, the Netherlands and North America. Eur J Public Health. 2011;21:414-419.
14. Gustafson P, Le ND, Saskin R. Case-control analysis with partial knowledge of exposure misclassification probabilities. Biometrics. 2001;57:598-609.
15. MacLehose RF, Gustafson P. Is probabilistic bias analysis approximately Bayesian? Epidem iology. 2012;23:151-158.