Bayesian modeling of cost-effectiveness studies with unmeasured confounding: A simulation study

Pharmaceutical Statistics

Volumn 13, Issue 1, 2014, Pages 94-100

  Stamey, James D ^a   Beavers, Daniel P ^b   Faries, Douglas ^c   Price, Karen L ^c   Seaman, John W ^a  

^a BAYLOR UNIVERSITY   (United States)

^b WAKE FOREST SCHOOL OF MEDICINE   (United States)

^c ELI LILLY AND COMPANY   (United States)

Author keywords

cost effectiveness; simulation; validation data

Indexed keywords

ARTICLE; BAYES THEOREM; COST EFFECTIVENESS ANALYSIS; KERNEL METHOD; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NORMAL DISTRIBUTION; PROBABILITY; PROCESS MODEL; REGRESSION ANALYSIS; SAMPLE SIZE; SENSITIVITY ANALYSIS; STATISTICAL BIAS; STATISTICAL PARAMETERS; UNMEASURED CONFOUNDING; VALIDATION STUDY; COMPUTER SIMULATION; COST BENEFIT ANALYSIS; COST-EFFECTIVENESS; EPIDEMIOLOGY; HUMAN; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; VALIDATION DATA;

COST-EFFECTIVENESS; SIMULATION; VALIDATION DATA;

BAYES THEOREM; COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); COST-BENEFIT ANALYSIS; DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL;

EID: 84903615997     PISSN: 15391604     EISSN: 15391612     Source Type: Journal    
DOI: 10.1002/pst.1604     Document Type: Article

References (23)

1. Obenchain RL, Robinson RL, Swindle RW,. Cost-effectiveness inferences from bootstrap quadrant confidence levels: three degrees of dominance. Journal of Biopharmaceutical Statistics 2005; 15: 419-436.
2. Willan AR, Briggs AH,. Statistical Analysis of cost-effectiveness data. Wiley: Chichester, U.K., 2006.
3. Bang H, Zhao H,. Average cost-effectiveness ratio withcensored data. Journal of Biopharmaceutical Statistics 2012; 22: 401-415.
4. O'Hagan A, Stevens JW, Montmartin J,. Bayesian cost-effectiveness analysis from clinical trial data. Statistics in Medicine 2001; 20: 733-753.
5. Bachmann MO, Fairall L, Clark A, Mugford M,. Methods for analyzing cost effectiveness data from cluster randomized trials. Cost Effectiveness and Resource Allocation 2007; 5: 12.
6. Grieve R, Nixon R, Thompson SG,. Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials. Medical Decision Making 2010; 30: 163-175.
7. Schneeweiss S,. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiology and Drug Safety 2006; 15: 291-303.
8. McCandless LC, Gustafson P, Levy AR,. Bayesian sensitivity analysis for unmeasured confounding in observational studies. Statistics in Medicine 2007; 26: 2331-2347.
9. Faries D, Peng X, Pawaskar M, Price K, Stamey JD, Seaman JW,. Evaluating the impact of unmeasured confounding with internal validation data: an example cost evaluation in type 2 diabetes. Value in Health 2013; 16: 259-266.
10. Kreif N, Grieve R, Sadique MZ,. Statistical methods for cost-effectiveness analyses that use observational data: a critical appraisal tool and review of current practice. Health Economics 2013; 4: 486-500.
11. Corrao G, Scotti L, et al., Cost-effectiveness of enhancing adherence to therapy with statins in the setting of primary cardiovascular prevention. Evidence from an empirical approach based on administrative databases. Atherosclerosis 2011; 217: 479-485.
12. Steenland K, Greenland S,. Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. American Journal of Epidemiology 2004; 160: 384-392.
13. Basu A, Manning W. G, Mullahy J,. Comparing alternative models: log vs. Cox proportional hazard. Health Economics 2004; 13: 749-765.
14. Thompson SG, Nixon RM,. How sensitive are cost-effectiveness analyses to choice of parametric distributions? Medical Decision Making 2005; 25: 416-423.
15. Rothman KJ, Greenland S, Lash TL,. Modern epidemiology. Wolters Kluwer Health: Chichester, U.K., 2008.
16. McCandless LC, Gustafson P, Levy AR,. A sensitivity analysis using information about measured confounders yielded improved uncertainty assessments for unmeasured confounding. Journal of Clinical Epidemiology 2008; 61: 247-255.
17. Fewell Z, Smith GD, Sterne JA,. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. American Journal of Epidemiology 2007; 166 (6): 646-655.
18. Greenland S,. Relaxation penalties and priors for plausible modeling of nonidentified bias sources. Statistical Science 2009; 24: 195-210.
19. Gustafson P, McCandless LC,. Probabilistic approaches to better quantifying the results of epidemiologic studies. International Journal of Environmental Research and Public Health 2010; 7: 1520-1539.
20. Prescott GJ, Garthwaite PH,. A Bayesian approach to prospective binary outcome studies with misclassification in a binary risk factor. Statistics in Medicine 2005; 24: 3463-3477.
21. Lyles RH, Tang L, Superak HM, King CC, Celentano DD, Lo Y, Sobel JD,. Validation data-based adjustments for outcome misclassification in logistic regression: an illustration. Epidemiology 2011; 22: 589-597.
22. Willan A, Kowgier M,. Cost-effectiveness analysis of multinational RCT with a binary measure of effectiveness and an interacting covariate. Health Economics 2008; 17: 777-791.
23. Christensen R, Johnson WO, Branscum A, Hanson TE,. Bayesian ideas and data analysis: an introduction for scientists and statisticians. CRC Press: Boca Raton, 2010.