Sargent, Daniel J

Phase I oncology trials are designed to identify a safe dose with an acceptable toxicity profile. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle, although patients continue to receive treatment for multiple cycles. In addition, the toxicity data from multiple types and grades are typically summarized into a single binary outcome of dose‐limiting toxicity. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades. In this work, we propose to account for longitudinal repeated measures of total toxicity profile over multiple treatment cycles, accounting for cumulative toxicity during dosing‐finding. A linear mixed model was utilized in the Bayesian framework, with addition of Bayesian risk functions for decision‐making in dose assignment. The performance of this design is evaluated using simulation studies and compared with the previously proposed quasi‐likelihood continual reassessment method (QLCRM) design. Twelve clinical scenarios incorporating four different locations of maximum tolerated dose and three different time trends (decreasing, increasing, and no effect) were investigated. The proposed repeated measures design was comparable with the QLCRM when only cycle 1 data were utilized in dose‐finding; however, it demonstrated an improvement over the QLCRM when data from multiple cycles were used across all scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

Progression‐free survival is an increasingly popular end point in oncology clinical trials. A complete blinded independent central review (BICR) is often required by regulators in an attempt to reduce the bias in progression‐free survival (PFS) assessment. In this paper, we propose a new methodology that uses a sample‐based BICR as an audit tool to decide whether a complete BICR is needed. More specifically, we propose a new index, the differential risk, to measure the reading discordance pattern, and develop a corresponding hypothesis testing procedure to decide whether the bias in local evaluation is acceptable. Simulation results demonstrate that our new index is sensitive to the change of discordance pattern; type I error is well controlled in the hypothesis testing procedure, and the calculated sample size provides the desired power. Copyright © 2016 John Wiley & Sons, Ltd.

Large sample sizes are required in randomized clinical trials designed to meet typical one‐sided 2.5% α‐level and 80% power. This may not be achievable when the disease is rare. We simulated a series of two‐arm superiority trials over a 15‐year period. The design parameters examined were the α‐level and the number of trials conducted over the 15‐year period (thus, trial sample size). Different disease severities and accrual rates were considered. The future treatment effect was characterized by its associated hazard rate; different hypotheses of how treatments improve over time were considered. We defined the total survival benefit as the relative difference of the hazard rates at year 15 versus year 0. The optimal design was defined by maximizing the expected total survival benefit, provided that the risk of selecting at year 15 a treatment inferior to the initial control treatment remains below 1%. Compared with two larger trials with typical one‐sided 2.5% α‐level, performing a series of small trials with relaxed α‐levels leads on average to larger survival benefits over a 15‐year research horizon, but also to higher risk of selecting a worse treatment at the end of the research period. Under reasonably optimistic assumptions regarding the future treatment effects, optimal designs outperform traditional ones when the disease is severe (baseline median survival ≤ 1 year) and the accrual is ≥100 patients per year, whereas no major improvement is observed in diseases with better prognosis. Trial designs aiming to maximize survival gain over a long research horizon across a series of trials are worth discussing in the context of rare diseases. Copyright © 2016 John Wiley & Sons, Ltd.

AbstractPurpose: A let-7 microRNA-complementary site (LCS6) polymorphism in the 3′ untranslated region of the KRAS gene has been shown to disrupt let-7 binding and upregulate KRAS expression. We evaluated the LCS6 genotype and its association with KRAS mutation status, clinicopathologic features, and disease-free survival (DFS) in patients with stage III colon cancer who enrolled in a phase III clinical trial (NCCTG N0147).Experimental Design: The LCS6 genotype was assayed by real-time PCR in DNA extracted from whole blood (n = 2,834) and compared with paired tumor tissue (n = 977). χ2 and two-sample t tests were used to compare baseline factors and KRAS mutation status between patients defined by LCS6 variant status. Log-rank tests and multivariate Cox models assessed associations between LCS6 status and DFS, respectively.Results: We identified 432 (15.2%) blood samples and 143 (14.6%) tumor samples heterozygous or homozygous for the LCS6 G-allele, and 2,402 of 2,834 (84.8%) blood samples and 834 of 977 (85.4%) tumor samples homozygous for the LCS6 T-allele. Genotype results were highly concordant (99.8%) in cases with paired blood and tumor tissue (n = 977). G-allele carriers were significantly more frequent in Caucasians versus other races (χ2 test, P < 0.0001). The LCS6 genotype was not associated with KRAS mutation status, clinicopathologic features (all P > 0.2), or DFS (log-rank P = 0.49; HR, 0.929; 95% confidence interval, 0.76–1.14), even after combining LCS6 genotype with KRAS mutation status.Conclusions: In the largest association study investigating the LCS6 polymorphism in colon cancers, the germline LCS6 genotype was not associated with KRAS mutation status or with clinical outcome in patients with stage III tumors. Clin Cancer Res; 20(12); 3319–27. ©2014 AACR.

Abstract Purpose: We examined the prognostic impact of specific KRAS mutations in patients with stage III colon adenocarcinoma receiving adjuvant FOLFOX alone or combined with cetuximab in a phase III trial (N0147). Analysis was restricted to BRAF–wild-type tumors, because BRAF mutation was associated with poor prognosis, and BRAF and KRAS mutations are mutually exclusive. Experimental Design: The seven most common KRAS mutations in codon 12 and codon 13 were examined in 2,478 BRAF–wild-type tumors. Because KRAS mutations in codon 12 (n = 779) or 13 (n = 220) were not predictive of adjuvant cetuximab benefit, study arms were pooled for analysis. Disease-free survival (DFS) was evaluated by HRs using Cox models. Results: KRAS mutations in codon 12 (multivariate HR, 1.52; 95% confidence interval, CI, 1.28–1.80; P < 0.0001) or codon 13 (multivariate HR, 1.36; 95% CI, 1.04–1.77; P = 0.0248) were significantly associated with shorter DFS compared with patients with wild-type KRAS/BRAF tumors, independent of covariates. KRAS codon 12 mutations were independently associated with proficient mismatch repair (P < 0.0001), proximal tumor site (P < 0.0001), low grade, age, and sex, whereas codon 13 mutations were associated with proximal site (P < 0.0001). Conclusion: KRAS mutations in either codon 12 or 13 are associated with inferior survival in patients with resected stage III colon cancer. These data highlight the importance of accurate molecular characterization and the significant role of KRAS mutations in both codons in the progression of this malignancy in the adjuvant setting. Clin Cancer Res; 20(11); 3033–43. ©2014 AACR.

Purpose We sought to test and validate the predictive utility of trichotomous tumor response (TriTR; complete response [CR] or partial response [PR] v stable disease [SD] v progressive disease [PD]), disease control rate (DCR; CR/PR/SD v PD), and dichotomous tumor response (DiTR; CR/PR v others) metrics using alternate cut points for PR and PD. The data warehouse assembled to guide the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 was used. Methods Data from 13 trials (5,480 patients with metastatic breast cancer, non–small-cell lung cancer, or colorectal cancer) were randomly split (60:40) into training and validation data sets. In all, 27 pairs of cut points for PR and PD were considered: PR (10% to 50% decrease by 5% increments) and PD (10% to 20% increase by 5% increments), for which 30% and 20% correspond to the RECIST categorization. Cox proportional hazards models with landmark analyses at 12 and 24 weeks stratified by study and number of lesions (fewer than three v three or more) and adjusted for average baseline tumor size were used to assess the impact of each metric on overall survival (OS). Model discrimination was assessed by using the concordance index (c-index). Results Standard RECIST cut points demonstrated predictive ability similar to the alternate PR and PD cut points. Regardless of tumor type, the TriTR, DiTR, and DCR metrics had similar predictive performance. The 24-week metrics (albeit with higher c-index point estimate) were not meaningfully better than the 12-week metrics. None of the metrics did particularly well for breast cancer. Conclusion Alternative cut points to RECIST standards provided no meaningful improvement in OS prediction. Metrics assessed at 12 weeks have good predictive performance.

AbstractIn cancer clinical trials, patients often experience a recurrence of disease prior to the outcome of interest, overall survival. Additionally, for many cancers, there is a cured fraction of the population who will never experience a recurrence. There is often interest in how different covariates affect the probability of being cured of disease and the time to recurrence, time to death, and time to death after recurrence. We propose a multi‐state Markov model with an incorporated cured fraction to jointly model recurrence and death in colon cancer. A Bayesian estimation strategy is used to obtain parameter estimates. The model can be used to assess how individual covariates affect the probability of being cured and each of the transition rates. Checks for the adequacy of the model fit and for the functional forms of covariates are explored. The methods are applied to data from 12 randomized trials in colon cancer, where we show common effects of specific covariates across the trials. Copyright © 2013 John Wiley & Sons, Ltd.

Background Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially. Purpose The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate. Results A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method. Conclusions Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward.