BACKGROUND In patients with chronic-phase chronic myeloid leukemia (CP-CML), imatinib resistance is of increasing importance. Imatinib dose escalation was the main treatment option before dasatinib, which has 325-fold more potent inhibition than imatinib against unmutated Bcr-Abl in vitro. Data with a minimum of 2 years of follow-up were available for the current study of dasatinib and high-dose imatinib in CP-CML resistant to imatinib at daily doses from 400 mg to 600 mg. METHODS A phase 2, open-label study was initiated of 150 patients with imatinib-resistant CP-CML who were randomized (2:1) to receive either dasatinib 70 mg twice daily (n = 101) or high-dose imatinib 800 mg (400 mg twice daily; n = 49). RESULTS At a minimum follow-up of 2 years, dasatinib demonstrated higher rates of complete hematologic response (93% vs 82%; P = .034), major cytogenetic response (MCyR) (53% vs 33%; P = .017), and complete cytogenetic response (44% vs 18%; P = .0025). At 18 months, the MCyR was maintained in 90% of patients on the dasatinib arm and in 74% of patients on the high-dose imatinib arm. Major molecular response rates also were more frequent with dasatinib than with high-dose imatinib (29% vs 12%; P = .028). The estimated progression-free survival also favored dasatinib (unstratified log-rank test; P = .0012). CONCLUSIONS After 2 years of follow-up, dasatinib demonstrated durable responses and improved response and progression-free survival rates relative to high-dose imatinib.

Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comparability of the current and the historical data. The motivating data set consists of randomised controlled trials for acute myeloid leukaemia. A simulation study was used to compare the methods in terms of bias, precision, power and type I error rate. Methods that estimate parameters for the between-trial heterogeneity generally offer the best trade-off of power, precision and type I error, with the meta-analytic-predictive prior being the most promising method. The results show that it can be feasible to include historical data in the analysis of clinical trials, if an appropriate method is used to estimate the heterogeneity between trials, and the historical data satisfy criteria for comparability.

Including historical data may increase the power of the analysis of a current clinical trial and reduce the sample size of the study. Recently, several Bayesian methods for incorporating historical data have been proposed. One of the methods consists of specifying a so‐called power prior whereby the historical likelihood is downweighted with a weight parameter. When the weight parameter is also estimated from the data, the modified power prior (MPP) is needed. This method has been used primarily when a single historical trial is available. We have adapted the MPP for incorporating multiple historical control arms into a current clinical trial, each with a separate weight parameter. Three priors for the weights are considered: (1) independent, (2) dependent, and (3) robustified dependent. The latter is developed to account for the possibility of a conflict between the historical data and the current data. We analyze two real‐life data sets and perform simulation studies to compare the performance of competing Bayesian methods that allow to incorporate historical control patients in the analysis of a current trial. The dependent power prior borrows more information from comparable historical studies and thereby can improve the statistical power. Robustifying the dependent power prior seems to protect against prior‐data conflict.

Progression-related endpoints (such as time to progression or progression-free survival) and time to death are common endpoints in cancer clinical trials. It is of interest to study the link between progression-related endpoints and time to death (e.g. to evaluate the degree of surrogacy). However, current methods ignore some aspects of the definitions of progression-related endpoints. We review those definitions and investigate their impact on modeling the joint distribution. Further, we propose a multi-state model in which the association between the endpoints is modeled through a frailty term. We also argue that interval-censoring needs to be taken into account to more closely match the latent disease evolution. The joint distribution and an expression for Kendall's tau are derived. The model is applied to data from a clinical trial in advanced metastatic ovarian cancer.