Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

Point estimation following two-stage adaptive threshold enrichment clinical trials

Recently, several study designs incorporating treatment effect assessment in biomarker‐based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two‐stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial

Duration of adjuvant chemotherapy for stage III colon cancer

BACKGROUND Since 2004, a regimen of 6 months of treatment with oxaliplatin plus a fluoropyrimidine has been standard adjuvant therapy in patients with stage III colon cancer. However, since oxaliplatin is associated with cumulative neurotoxicity, a shorter duration of therapy could spare toxic effects and health expenditures. METHODS We performed a prospective, preplanned, pooled analysis of six randomized, phase 3 trials that were conducted concurrently to evaluate the noninferiority of adjuvant therapy with either FOLFOX (fluorouracil, leucovorin, and oxaliplatin) or CAPOX (capecitabine and oxaliplatin) administered for 3 months, as compared with 6 months. The primary end point was the rate of disease-free survival at 3 years. Noninferiority of 3 months versus 6 months of therapy could be claimed if the upper limit of the two-sided 95% confidence interval of the hazard ratio did not exceed 1.12. RESULTS After 3263 events of disease recurrence or death had been reported in 12,834 patients, the noninferiority of 3 months of treatment versus 6 months was not confirmed in the overall study population (hazard ratio, 1.07; 95% confidence interval [CI], 1.00 to 1.15). Noninferiority of the shorter regimen was seen for CAPOX (hazard ratio, 0.95; 95% CI, 0.85 to 1.06) but not for FOLFOX (hazard ratio, 1.16; 95% CI, 1.06 to 1.26). In an exploratory analysis of the combined regimens, among the patients with T1, T2, or T3 and N1 cancers, 3 months of therapy was noninferior to 6 months, with a 3-year rate of disease-free survival of 83.1% and 83.3%, respectively (hazard ratio, 1.01; 95% CI, 0.90 to 1.12). Among patients with cancers that were classified as T4, N2, or both, the disease-free survival rate for a 6-month duration of therapy was superior to that for a 3-month duration (64.4% vs. 62.7%) for the combined treatments (hazard ratio, 1.12; 95% CI, 1.03 to 1.23; P=0.01 for superiority). CONCLUSIONS Among patients with stage III colon cancer receiving adjuvant therapy with FOLFOX or CAPOX, noninferiority of 3 months of therapy, as compared with 6 months, was not confirmed in the overall population. However, in patients treated with CAPOX, 3 months of therapy was as effective as 6 months, particularly in the lower-risk subgroup. (Funded by the National Cancer Institute and others.

Germline Variation in Colorectal Risk Loci Does Not Influence Treatment Effect or Survival in Metastatic Colorectal Cancer

BackgroundColorectal cancer (CRC) risk is partly conferred by common, low-penetrance single nucleotide polymorphisms (SNPs). We hypothesized that these SNPs are associated with outcomes in metastatic CRC.MethodsSix candidate SNPs from 8q24, 10p14, 15q13, 18q21 were investigated for their association with response rate (RR), time to progression (TTP) and overall survival (OS) among 524 patients treated on a phase III clinical trial of first-line chemotherapy for metastatic CRC.Resultsrs10795668 was weakly associated with TTP (p = 0.02), but not RR or OS. No other SNPs carried statistically significant HRs for any of the primary outcomes (RR, TTP or OS).ConclusionCommon low-penetrance CRC risk SNPs were not associated with outcomes among patients with metastatic CRC

Clinical Calculator for Early Mortality in Metastatic Colorectal Cancer: An Analysis of Patients From 28 Clinical Trials in the Aide et Recherche en Cancérologie Digestive Database

Purpose: Factors contributing to early mortality after initiation of treatment of metastatic colorectal cancer are poorly understood. Materials and Methods: Data from 22,654 patients enrolled in 28 randomized phase III trials contained in the ARCAD (Aide et Recherche en Cancérologie Digestive) database were pooled. Multivariable logistic regression models for 30-, 60-, and 90-day mortality were constructed, including clinically and statistically significant patient and disease factors and interaction terms. A calculator (nomogram) for 90-day mortality was developed and validated internally using bootstrapping methods and externally using a 10% random holdout sample from each trial. The impact of early progression on the likelihood of survival to 90 days was examined with time-dependent Cox proportional hazards models. Results: Mortality rates were 1.4% at 30 days, 3.4% at 60 days, and 5.5% at 90 days. Among baseline factors, advanced age, lower body mass index, poorer performance status, increased number of metastatic sites, BRAF mutant status, and several laboratory parameters were associated with increased likelihood of early mortality. A multivariable model for 90-day mortality showed strong internal discrimination (C-index, 0.77) and good calibration across risk groups as well as accurate predictions in the external validation set, both overall and within patient subgroups. Conclusion: A validated clinical nomogram has been developed to quantify the risk of early death for individual patients during initial treatment of metastatic colorectal cancer. This tool may be used for patient eligibility assessment or risk stratification in future clinical trials and to identify patients requiring more or less aggressive therapy and additional supportive measures during and after treatment

Bayesian evaluation and adaptive trial design for surrogate time-to-event endpoints in clinical trials.

Surrogate endpoints are desirable in clinical trials when primary endpoints are costly to obtain, difficult to measure, or require lengthy follow-up to observe. Despite legitimate concerns, evaluation of potentially beneficial treatments in some settings remains impossible or implausible without the use of surrogates. Furthermore, strong evidence based on a collection of trials, rather than a relationship observed within a single trial, is required to validate a surrogate endpoint for future primary use. We present a Bayesian approach to evaluating surrogacy using patient data from multiple trials with time-to-event endpoints that accounts for estimation error of treatment effects and offers greater computational stability than existing methods. Once a surrogate endpoint has been deemed valid for use in a future trial, a healthy skepticism should remain regarding its ability to reflect the true treatment effect that would have been observed on the primary endpoint. Despite the surrogate's intended role, few (if any) efforts have been made to formalize existing knowledge and uncertainty in the design of such a trial. We propose a Bayesian adaptive design that uses the validated surrogate as the primary endpoint, while acknowledging that this endpoint is really a surrogate, and perhaps only a recently- validated one. At prospectively-defined checkpoints, we assess the performance of the surrogate and decide whether to continue its use or switch consideration to the primary endpoint. Furthermore, our design incorporates other favorable aspects of Bayesian adaptive trials, including the ability to stop a trial early for treatment efficacy, inferiority, or trial futility. Flowgraphs are useful for modeling diseases that are well-described by multi- state models, but for which Markov assumptions are inadequate and returns to previous states are possible. Furthermore, censoring and covariates may influence the distribution of waiting times between any two states, and to a differing degree for separate transitions within the same system. We discuss the construction and advantages of flowgraph models when used to describe cancer progression within two clinical trials, where our goal is improved modeling of treatment effects and prediction of patient outcomes for the purpose of more realistic surrogacy evaluation.by Lindsay A. Renfro.Ph.D

Comparative assessment of trial-level surrogacy measures for candidate time-to-event surrogate endpoints in clinical trials

Various meta-analytical approaches have been applied to evaluate putative surrogate endpoints (S) of primary clinical endpoints (T), however a systematic assessment of their performance is lacking. Existing methods in the meta-analytic framework can be grouped into two types--conventional and model-based trial-level surrogacy (TLS) measures. Both conventional and model-based TLS measures assess the ability to predict the treatment effect on T based on an observed treatment effect on putative S. Conventional TLS measures include correlation coefficients and R-square measures from weighted linear regression. Model-based TLS includes Copula R2 proposed by Burzykowski et al. (2001). We examined and compared the estimation performance of these frequently used surrogacy measures in a large-scale simulation study. The impact of several key factors on the estimation performance was assessed, including the strength of the true surrogacy, the amount of effective information provided by available data, and the range of within-trial treatment effect on S and T. The TLS can be estimated accurately and precisely by both types of surrogacy measures when the true surrogacy is strong, number of trials is large, and the range of within-trial treatment effects is wide. When one or more factors deviate from the "best" scenarios, both types of TLS measures tend to underestimate the true surrogacy with increased variability. The estimation performance of conventional measures is similar to model-based measures, but with higher computational efficiency. The findings are applied to a large individual patient data pooled analysis in colon cancer.Clinical trials Meta-analysis Survival analysis Trial-level surrogacy

Projecting Event-Based Analysis Dates in Clinical Trials: An Illustration Based on the International Duration Evaluation of Adjuvant Chemotherapy (IDEA) Collaboration. Projecting Analysis Dates for the IDEA Collaboration

Purpose: Clinical trials are expensive and lengthy, where success of a given trial depends on observing a prospectively defined number of patient events required to answer the clinical question. The point at which this analysis time occurs depends on both patient accrual and primary event rates, which typically vary throughout the trial's duration. We demonstrate real-time analysis date projections using data from a collection of six clinical trials that are part of the IDEA collaboration, an international preplanned pooling of data from six trials testing the duration of adjuvant chemotherapy in stage III colon cancer, and we additionally consider the hypothetical impact of one trial's early termination of follow-up

Characteristics of Study Population.

<p>Characteristics of Study Population.</p