Author:
Cheng Ji,Pullenayegum Eleanor,Marshall Deborah A,Marshall John K,Thabane Lehana
Abstract
Abstract
Background
A discrete choice experiment (DCE) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks. Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar. In this study, we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer (CRC) screening tests conducted in Hamilton, Ontario, Canada in 2002.
Methods
A two-stage DCE design was used to investigate the impact of six attributes on participants' preferences for CRC screening test and willingness to undertake the test. We compared six models for clustered binary outcomes (logistic and probit regressions using cluster-robust standard error (SE), random-effects and generalized estimating equation approaches) and three models for clustered nominal outcomes (multinomial logistic and probit regressions with cluster-robust SE and random-effects multinomial logistic model). We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes. The rank of relative importance between attributes and the estimates of
β
coefficient within attributes were used to assess the model robustness.
Results
In total 468 participants with each completing 10 choices were analyzed. Similar results were reported for the rank of relative importance and
β
coefficients across models for stage-one data on evaluating participants' preferences for the test. The six attributes ranked from high to low as follows: cost, specificity, process, sensitivity, preparation and pain. However, the results differed across models for stage-two data on evaluating participants' willingness to undertake the tests. Little within-patient correlation (ICC ≈ 0) was found in stage-one data, but substantial within-patient correlation existed (ICC = 0.659) in stage-two data.
Conclusions
When small clustering effect presented in DCE data, results remained robust across statistical models. However, results varied when larger clustering effect presented. Therefore, it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies.
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Epidemiology
Reference59 articles.
1. Longo MF, Cohen DR, Hood K, Edwards A, Robling M, Elwyn G, Russell IT: Involving patients in primary care consultations: assessing preferences using discrete choice experiments. Br J Gen Pract. 2006, 56 (522): 35-42.
2. Ryan M, Major K, Skatun D: Using discrete choice experiments to go beyond clinical outcomes when evaluating clinical practice. J Eval Clin Pract. 2005, 11 (4): 328-338. 10.1111/j.1365-2753.2005.00539.x.
3. Lancaster KJ: A new approach to consumer theory. J Polit Econ. 1966, 74 (2): 132-157. 10.1086/259131.
4. Montgomery DC: Design and analysis of experiments. 2000, New York: Wiley, 5
5. Louviere J, Hensher D: On the design and analysis of simulated choice or allocation experiments in travel choice modelling. Transp Res Rec. 1982, 890: 11-17.
Cited by
22 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献