Survival trial design and monitoring using historical controls

Role of peer support in a hepatitis C elimination programme

Many people with chronic hepatitis C infection don't engage in treatment. To eliminate hepatitis C and avoid health inequalities therapy must be provided to everyone. In other diseases peers with lived experience of the condition have improved care but, for hepatitis C, studies have not shown unequivocal benefit. We completed a retrospective analysis of the English National Health Service treatment registry comparing treatment networks with and without peers using Bayesian Poisson (for count outcomes) or Bayesian Binomial (for proportion outcomes) mixed effects models with time fixed effects. For each outcome, we estimated relative ratio (RR-Poisson model) or odds ratio (Odds Ratio (OR)-Binomial model) between peer and non-peer networks. We analysed 30,729 patients within 20 operational delivery networks. In networks with peers there was an increase in the number of people initiating therapy (RR 1.12 95%, credible interval 1.02-1.21) and an increase in the proportion completing therapy (OR 2.45 95%, credible interval 1.49-3.84). However, we saw no change in proportions of people using drugs who initiated therapy nor any significant change in virological response (OR 1.14 95% credible interval 0.979-1.36). We repeated the analysis looking at the impact of peers two months after they had been introduced, when they had established networks of contacts, and saw an increase in the proportion of people treated in addiction services. In treating patients with chronic hepatitis C infection the inclusion of peer supporters may increase the number of people who initiate and complete antiviral therapy.

A two-stage drop-the-losers design for time-to-event outcome using a historical control arm

Phase II immuno-oncology clinical trials screen for efficacy an increasing number of treatments. In rare cancers, using historical control data is a pragmatic approach for speeding up clinical trials. The drop-the-losers design allows dropping off ineffective arms at interim analyses. We extended the original drop-the-losers design for a time-to-event outcome using a historical control through the one-sample log-rank statistic. Simulated trials featured three arms at the first stage, one at the second stage, nine scenarios, eight sample sizes with 5%- and 10%- nominal family-wise error rate (FWER). A numerical algorithm is provided to solve power calculations at the design stage. Our design was compared with a group of three independent single-arm trials (fixed design) with and without correction for multiplicity. Our design allowed strict control of the FWER at nominal levels while the misspecification of survival distribution and fixed design inflated the FWER up to three times the nominal level. The empirical power of our design increased with the sample size, the treatment effect and the number of effective treatments and dropped when more patients were recruited at the second stage. The fixed design with correction showed comparable power, while our design advantageously included more patients to the most promising arm. Recommendations for future applications are given. By taking advantage of the use of historical control data and a time-to-event outcome, the drop-the-losers design is a promising tool to meet the challenge of improving phase II clinical trials in immuno-oncology.

Group sequential design for historical control trials using error spending functions

Group sequential designs using Lan-DeMets error spending functions are proposed for historical control trials with time-to-event endpoints. Both O'Brien-Fleming and Gamma family types of sequential decision boundaries are derived based on sequential log-rank tests, which follow a Brownian motion in a transformed information time. Simulation results show that the proposed group sequential designs using historical controls preserve the overall type I error and power.

Trial Design Challenges and Approaches for Precision Oncology in Rare Tumors: Experiences of the Children's Oncology Group

In the United States, cancer remains the leading cause of disease-related death in children. Although survival from any pediatric cancer has improved dramatically during past decades, a number of cancers continue to yield dismal prognoses, which has motivated the continued study of novel therapeutic strategies. Furthermore, even patients cured of pediatric cancer often experience severe adverse effects of treatment and other long-term health implications, such as cardiotoxicity or loss of fertility. For these patients, improved risk stratification to identify those who could safely receive alternate or less-intensive therapy without affecting prognosis is a key objective. Fortunately, pediatric cancers are rare overall, but even among patients with the same narrow cancer type, there is often broad heterogeneity in terms of prognosis, molecular features or pathology, current treatment strategies, and scientific objectives. As a result, the design of clinical trials in the pediatric cancer setting is challenged by a number of practical issues that must be addressed to ensure trial feasibility for this vulnerable group of patients. In this review, we discuss some of the unique trial design considerations often encountered in any rare tumor setting through the lens of our experiences as faculty statisticians for the Children's Oncology Group, the largest organization in the world dedicated exclusively to pediatric cancer research and clinical trials. These topics include risk stratification within individual trials, relaxation of trial operating characteristics and parameters, use of historical controls, and address of noninferiority-type objectives in small cohorts. We review each in terms of practical motivation, present challenges, and potential solutions described in the literature and implemented in selected example trials from the Children's Oncology Group.