Sargent, Daniel J

Purpose Prior studies have suggested that patients with stage II/III colon cancer receive similar benefit from intravenous (IV) fluoropyrimidine adjuvant therapy regardless of age. Combination regimens and oral fluorouracil (FU) therapy are now standard. We examined the impact of age on colon cancer recurrence and mortality after adjuvant therapy with these newer options. Patients and Methods We analyzed 11,953 patients age < 70 and 2,575 age ≥ 70 years from seven adjuvant therapy trials comparing IV FU with oral fluoropyrimidines (capecitabine, uracil, or tegafur) or combinations of fluoropyrimidines with oxaliplatin or irinotecan in stage II/III colon cancer. End points were disease-free survival (DFS), overall survival (OS), and time to recurrence (TTR). Results In three studies comparing oxaliplatin-based chemotherapy with IV FU, statistically significant interactions were not observed between treatment arm and age (P interaction = .09 for DFS, .05 for OS, and .36 for TTR), although the stratified point estimates suggested limited benefit from the addition of oxaliplatin in elderly patients (DFS hazard ratio [HR], 0.94; 95% CI, 0.78 to 1.13; OS HR, 1.04; 95% CI, 0.85 to 1.27). No significant interactions by age were detected with oral fluoropyrimidine therapy compared with IV FU; noninferiority was supported in both age populations. Conclusion Patients age ≥ 70 years seemed to experience reduced benefit from adding oxaliplatin to fluoropyrimidines in the adjuvant setting, although statistically, there was not a significant effect modification by age, whereas oral fluoropyrimidines retained their efficacy.

Background Prospective trial design often occurs in the presence of ‘acceptable’ historical control data. Typically, these data are only utilized for treatment comparison in a posteriori retrospective analysis to estimate population-averaged effects in a random-effects meta-analysis. Purpose We propose and investigate an adaptive trial design in the context of an actual randomized controlled colorectal cancer trial. This trial, originally reported by Goldberg et al., succeeded a similar trial reported by Saltz et al., and used a control therapy identical to that tested (and found beneficial) in the Saltz trial. Methods The proposed trial implements an adaptive randomization procedure for allocating patients aimed at balancing total information (concurrent and historical) among the study arms. This is accomplished by assigning more patients to receive the novel therapy in the absence of strong evidence for heterogeneity among the concurrent and historical controls. Allocation probabilities adapt as a function of the effective historical sample size (EHSS), characterizing relative informativeness defined in the context of a piecewise exponential model for evaluating time to disease progression. Commensurate priors are utilized to assess historical and concurrent heterogeneity at interim analyses and to borrow strength from the historical data in the final analysis. The adaptive trial’s frequentist properties are simulated using the actual patient-level historical control data from the Saltz trial and the actual enrollment dates for patients enrolled into the Goldberg trial. Results Assessing concurrent and historical heterogeneity at interim analyses and balancing total information with the adaptive randomization procedure lead to trials that on average assign more new patients to the novel treatment when the historical controls are unbiased or slightly biased compared to the concurrent controls. Large magnitudes of bias lead to approximately equal allocation of patients among the treatment arms. Using the proposed commensurate prior model to borrow strength from the historical data, after balancing total information with the adaptive randomization procedure, provides admissible estimators of the novel treatment effect with desirable bias-variance trade-offs. Limitations Adaptive randomization methods in general are sensitive to population drift and more suitable for trials that initiate with gradual enrollment. Balancing information among study arms in time-to-event analyses is difficult in the presence of informative right-censoring. Conclusions The proposed design could prove important in trials that follow recent evaluations of a control therapy. Efficient use of the historical controls is especially important in contexts where reliance on preexisting information is unavoidable because the control therapy is exceptionally hazardous, expensive, or the disease is rare.

Purpose By using data from North Central Cancer Treatment Group Phase III Trial N0147, a randomized adjuvant trial of patients with stage III colon cancer, we assessed the relationship between smoking and cancer outcomes, disease-free survival (DFS), and time to recurrence (TTR), accounting for heterogeneity by patient and tumor characteristics. Patients and Methods Before random assignment to infusional fluorouracil, leucovorin, and oxaliplatin (FOLFOX) or FOLFOX plus cetuximab, 1,968 participants completed a questionnaire on smoking history and other risk factors. Cox models assessed the association between smoking history and the primary trial outcome of DFS (ie, time to recurrence or death), as well as TTR, adjusting for other clinical and patient factors. The median follow-up was 3.5 years among patients who did not experience events. Results Compared with never-smokers, ever smokers experienced significantly shorter DFS (3-year DFS proportion: 70% v 74%; hazard ratio [HR], 1.21; 95% CI, 1.02 to 1.42). This association persisted after multivariate adjustment (HR, 1.23; 95% CI, 1.02 to 1.49). There was significant interaction in this association by BRAF mutation status (P = .03): smoking was associated with shorter DFS in patients with BRAF wild-type (HR, 1.36; 95% CI, 1.11 to 1.66) but not BRAF mutated (HR, 0.80; 95% CI, 0.50 to 1.29) colon cancer. Smoking was more strongly associated with poorer DFS in those with KRAS mutated versus KRAS wild-type colon cancer (HR, 1.50 [95% CI, 1.12 to 2.00] v HR, 1.09 [95% CI, 0.85 to 1.39]), although interaction by KRAS mutation status was not statistically significant (P = .07). Associations were comparable in analyses of TTR. Conclusion Overall, smoking was significantly associated with shorter DFS and TTR in patients with colon cancer. These adverse relationships were most evident in patients with BRAF wild-type or KRAS mutated colon cancer.

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract Biomarkers are critical to targeted therapies, as they may identify patients more likely to benefit from a treatment. Several prospective designs for biomarker-directed therapy have been previously proposed, differing primarily in the study population, randomization scheme, or both. Recognizing the need for randomization, yet acknowledging the possibility of promising but inconclusive results after a stage I cohort of randomized patients, we propose a 2-stage phase II design on marker-positive patients that allows for direct assignment in a stage II cohort. In stage I, marker-positive patients are equally randomized to receive experimental treatment or control. Stage II has the option to adopt “direct assignment” whereby all patients receive experimental treatment. Through simulation, we studied the power and type I error rate of our design compared with a balanced randomized two-stage design, and conducted sensitivity analyses to study the effect of timing of stage I analysis, population shift effects, and unbalanced randomization. Our proposed design has minimal loss in power (&lt;1.8%) and increased type I error rate (&lt;2.1%) compared with a balanced randomized design. The maximum increase in type I error rate in the presence of a population shift was between 3.1% and 5%, and the loss in power across possible timings of stage I analysis was less than 1.2%. Our proposed design has desirable statistical properties with potential appeal in practice. The direct assignment option, if adopted, provides for an “extended confirmation phase” as an alternative to stopping the trial early for evidence of efficacy in stage I. Clin Cancer Res; 18(16); 4225–33. ©2012 AACR.

Purpose Limited data exist regarding the outcomes of adjuvant therapy in younger patients with stage II and III colon cancer. We examined disease-free survival (DFS), overall survival (OS), recurrence-free interval (RFI), and grade 3+ adverse events (AEs) in younger patients in the 33,574 patient Adjuvant Colon Cancer Endpoints Group data set. Patients and Methods Individual patient data from 24 randomized phase III clinical trials were obtained for survival outcomes, which included 10 clinical trials for AE outcomes. Two age-based cutoff points were used to define younger patients: age younger than 40 years and younger than 50 years. Adjuvant therapy benefit analyses were limited to the nine clinical trials in which the investigational chemotherapeutic arm demonstrated benefit. Results One thousand seven hundred fifty-eight patients (5.2%) were younger than 40 years, 5,817 patients (17.3%) were younger than 50 years, and only 299 patients (0.9%) were younger than 30 years. No meaningful differences in sex or stage were noted in younger versus older patients. Younger and older patients did not differ in RFI (age, < 40 years: hazard ratio [HR], 1.0; P = .62 and age < 50 years: HR, 1.02; P = .35). Younger patients (both cutoff points), had longer OS and DFS than older patients. In trials demonstrating adjuvant therapy benefit, similar DFS benefit was observed by age. Younger patients experienced less leukopenia and stomatitis, but more frequent nausea/vomiting. Conclusion Among patients on clinical trials, younger and older patients with stage II and III colon cancer had similar RFI and adjuvant therapy benefit. Younger patients have longer OS and DFS, which is likely primarily because of fewer competing causes of death. Adjuvant therapy is beneficial for colon cancer in patients younger than 50 years who meet typical clinical trial eligibility criteria.

Background Traditional clinical trial designs strive to definitively establish the superiority of an experimental treatment, which results in risk-adverse criteria and large sample sizes. Increasingly, common cancers are recognized as consisting of small subsets with specific aberrations for targeted therapy, making large trials infeasible. Purpose To compare the performance of different trial design strategies over a long-term research horizon. Methods We simulated a series of two-treatment superiority trials over 15 years using different design parameters. Trial parameters examined included the number of positive trials to establish superiority (one-trial vs. two-trial rule), α level (2.5%–50%), and the number of trials in the 15-year period, K (thus, trial sample size). The design parameters were evaluated for different disease scenarios, accrual rates, and distributions of treatment effect. Metrics used included the overall survival gain at a 15-year horizon measured by the hazard ratio (HR), year 15 versus year 0. We also computed the expected total survival benefit and the risk of selecting as new standard of care at year 15 a treatment inferior to the initial control treatment, P(detrimental effect). Results Expected survival benefits over the 15-year horizon were maximized when more (smaller) trials were conducted than recommended under traditional criteria, using the criterion of one positive trial (vs. two), and relaxing the α value from 2.5% to 20%. Reducing the sample size and relaxing the α value also increased the likelihood of selecting an inferior treatment at the end. The impact of α and K on the survival benefit depended on the specific disease scenario and accrual rate: greater gains for relaxing α in diseases with good outcome and/or low accrual rates and greater gains for increasing K for diseases with poor outcomes. Trials with smaller sample size did not perform well when using stringent (standard) level of evidence. For each disease scenario and accrual rate studied, the optimal design, defined as the design that the maximized expected total survival benefit while constraining P(detrimental effect) < 2.5%, specified α = 20% or 10%, and a sample size considerably smaller than that recommended by the traditional designs. The results were consistent under different assumed distributions for treatment effect. Limitations The simulations assumed no toxicity issues and did not consider interim analyses. Conclusions It is worthwhile to consider a design paradigm that seeks to maximize the expected survival benefit across a series of trials, over a longer research horizon. In today’s environment of constrained, biomarker-selected populations, our results indicate that smaller sample sizes and larger α values lead to greater long-term survival gains compared to traditional large trials designed to meet stringent criteria with a low efficacy bar.