The use of a quantitative treatment selection marker to choose between two treatment options requires the estimate of an optimal threshold above which one of these two treatments is preferred. Herein, the optimal threshold expression is based on the definition of a utility function which aims to quantify the expected utility of the population (e.g. life expectancy, quality of life) by taking into account both efficacy (success or failure) and toxicity of each treatment option. Therefore, the optimal threshold is the marker value that maximizes the expected utility of the population. A method modelling the marker distribution in patient subgroups defined by the received treatment and the outcome is proposed to calculate the parameters of the utility function so as to estimate the optimal threshold and its 95% credible interval using the Bayesian inference. The simulation study found that the method had low bias and coverage probability close to 95% in multiple settings, but also the need of large sample size to estimate the optimal threshold in some settings. The method is then applied to the PETACC-8 trial that compares the efficacy of chemotherapy with a combined chemotherapy + anti-epidermal growth factor receptor in stage III colorectal cancer.

1. Ballman, K . Biomarker: predictive or prognostic?. J Clin Oncol 2015; 33: 39683971.
Google Scholar | Crossref | Medline | ISI
2. Italiano, A . Prognostic or predictive? It’s time to get back to definitions!. J Clin Oncol 2011; 29: 4718.
Google Scholar | Crossref | Medline | ISI
3. Lièvre, A, Bachet, J, Boige, V, et al. KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab. J Clin Oncol 2008; 26: 374379.
Google Scholar | Crossref | Medline | ISI
4. Byar, D . Assessing apparent treatment-covariate interactions in randomized clinical trials. Stat Med 1985; 4: 255263.
Google Scholar | Crossref | Medline | ISI
5. Vickers, A, Kattan, M, Sargent, D. Method for evaluating prediction models that apply the results of randomized trials to individual patients. Trials 2007; 8: 14, .
Google Scholar | Crossref | Medline | ISI
6. Janes, H, Pepe, M, Bossuyt, P, et al. Measuring the performance of markers for guiding treatment decisions. Ann Intern Med 2011; 154: 253259.
Google Scholar | Crossref | Medline | ISI
7. Janes, H, Brown, M, Huang, Y, et al. An approach to evaluating and comparing biomarkers for patient treatment selection. Int J Biostat 2014; 10: 99121.
Google Scholar | Crossref | Medline | ISI
8. Huang, Y, Gilbert, P, Janes, H. Assessing treatment-selection markers using a potential outcomes framework. Biometrics 2012; 68: 687696.
Google Scholar | Crossref | Medline | ISI
9. Zhang, Z, Nie, L, Soon, G, et al. The use of covariates and random effects in evaluating predictive biomarkers under a potential outcome framework. Ann Appl Stat 2014; 8: 23362355.
Google Scholar | Crossref | Medline
10. Sox, H, Higgins, M, Owens, D. Medical decision making, 2nd ed. Chichester: John Wiley & Sons, 2013.
Google Scholar | Crossref
11. Janes, H, Pepe, M, Huang, Y. A framework for evaluating markers used to select patient treatment. Med Decis Making 2014; 34: 159167.
Google Scholar | SAGE Journals | ISI
12. Collins, G, Moons, K. Comparing risk prediction models. BMJ 2012; 344: e3186.
Google Scholar | Crossref | Medline
13. Van Calster, B, Vickers, A. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Making 2015; 35: 162169.
Google Scholar | SAGE Journals | ISI
14. Cullen, A, Frey, H. Probabilistic techniques in exposure assessment, New York: Plenum Press, 1999.
Google Scholar
15. Forbes, C, Evans, M, Hastings, N, et al. Statistical distributions, 4th ed. Hoboken: John Wiley & Sons, 2000.
Google Scholar
16. Delignette-Muller, ML, Dutang, C. fitdistrplus: an R package for fitting distributions. J Stat Softw 2015; 64: 134.
Google Scholar | Crossref
17. Vickers, AJ, Elkin, EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26: 56574.
Google Scholar | SAGE Journals | ISI
18. Huang, Y, Laber, E, Janes, H. Characterizing expected benefits of biomarkers in treatment selection. Biostatistics 2015; 16: 383399.
Google Scholar | Crossref | Medline
19. Baker, SG, Cook, NR, Vickers, A, et al. Using relative utility curves to evaluate risk prediction. J R Stat Soc Ser A Stat Soc 2009; 172: 729748.
Google Scholar | Crossref | Medline | ISI
20. Efron, B, Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1986; 1: 5475.
Google Scholar | Crossref
21. Schisterman, E, Perkins, N. Confidence intervals of the Youden index and corresponding optimal cut-point. Commun Stat Simul Comput 2007; 36: 549563.
Google Scholar | Crossref | ISI
22. Subtil, F, Rabilloud, M. A Bayesian method to estimate the optimal threshold of a longitudinal biomarker. Biom J 2010; 52: 333347.
Google Scholar | Crossref | Medline
23. Subtil, F, Rabilloud, M. Estimating the optimal threshold for a diagnostic biomarker in case of complex biomarker distributions. BMC Med Inform Decis Making 2014; 14: 53.
Google Scholar | Crossref | Medline
24. Gelman, A, Carlin, J, Stern, H, et al. Bayesian data analysis, 3rd ed. Boca Raton: CRC Press, 2014.
Google Scholar
25. R Core Team . R: a language and environment for statistical computing, Vienna, Austria: R Foundation for Statistical Computing, 2017.
Google Scholar
26. Taieb, J, Tabernero, J, Mini, E, et al. Oxaliplatin, fluorouracil, and leucovorin with or without cetuximab in patients with resected stage III colon cancer (PETACC-8): an open-label, randomised phase 3 trial. Lancet Oncol 2014; 15: 862873.
Google Scholar | Crossref | Medline
27. Dmitrienko, A, Molenberghs, G, Chuang-Stein, C, et al. Analysis of clinical trials using SAS: a practical guide, Cary: SAS Institute, 2005.
Google Scholar
28. Janes, H, Brown, M, Pepe, M. Designing a study to evaluate the benefit of a biomarker for selecting patient treatment. Stat Med 2015; 34: 35033515.
Google Scholar | Crossref | Medline
29. Escobar, M . Estimating normal means with a Dirichlet process prior. J Am Stat Assoc 1994; 89: 268277.
Google Scholar | Crossref | ISI
30. Ohlssen, D, Sharples, L, Spiegelhalter, D. Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons. Stat Med 2007; 26: 20882112.
Google Scholar | Crossref | Medline | ISI
31. Blanche, P, Dartigues, JF, Jacqmin-Gadda, H. Review and comparison of ROC curve estimators for a time-dependent outcome with marker-dependent censoring. Biom J 2013; 55: 687704.
Google Scholar | Crossref | Medline | ISI
Access Options

My Account

Welcome
You do not have access to this content.



Chinese Institutions / 中国用户

Click the button below for the full-text content

请点击以下获取该全文

Institutional Access

does not have access to this content.

Purchase Content

24 hours online access to download content

Research off-campus without worrying about access issues. Find out about Lean Library here

Your Access Options


Purchase

SMM-article-ppv for $41.50
Single Issue 24 hour E-access for $543.66

Cookies Notification

This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies. Find out more.
Top