Lindsay A. Renfro

We describe a Bayesian approach for sample size estimation for multi-arm randomized controlled trials utilizing response adaptive randomization (RAR) for continuous outcomes. Assuming normally distributed treatment effects and unknown but common treatment variance, this design incorporates outcome data to estimate posterior distributions of treatment parameters, modifies allocation proportions to favor more effective treatments, and re-estimates the number of participants needed. The sample size should be sufficient to show that at least one group difference is greater than 0 (success), or that all effect sizes are smaller than a desired threshold (futility) at prespecified probabilities. Using simulations for a 4-arm trial, we compare sample sizes based on hypothesis testing to those using a Bayesian approach: [1] without interim analysis; [2] with interim analyses and non-adaptive randomization; [3] with interim analyses and RAR. We demonstrate that two interim analyses, conducted when outcomes are available among 25% and 50% of participants, could result in fewer enrolled participants. Especially for distinctly large treatment effects, RAR-based sample size estimates increase due to imbalanced allocation of participants. Application of the approach to two completed randomized controlled trials confirms these observations. We conclude that early and frequent interim analyses may reduce the number of participants needed for a conclusive trial where participant allocation does not change throughout the trial. For trial designs incorporating RAR, the within-trial patient benefits of allocating more patients to favorable arms with larger sample size requirements should be considered against the efficiency of equal group allocation.

PURPOSE: Children with favorable histology Wilms tumor (FHWT) with pulmonary metastases have inferior outcomes compared with those with localized disease. We evaluated the impact of pulmonary tumor burden within subgroups of similarly treated children with stage IV FHWT. METHODS: Children with FHWT with pulmonary-only metastasis enrolled in AREN0533 were included. Lung nodule response assessment, by chest computed tomography after two cycles of vincristine/dactinomycin/doxorubicin (Regimen DD-4A) chemotherapy, identified rapid complete responses (RCRs) and slow incomplete pulmonary nodule responses (SIRs). Event-free survival (EFS) and overall survival (OS) were compared by number and size of pulmonary metastases within two cohorts: (1) RCR treated with additional DD-4A without lung radiation therapy (RT) and (2) SIR treated with the original three drugs plus cyclophosphamide/etoposide (Regimen M) with lung RT. The multivariable Cox proportional hazards model for EFS and OS stratified by treatment assessed the impact of the number and size of pulmonary metastases adjusted for tumor 1q gain. RESULTS: AREN0533 enrolled 288 children with stage IV pulmonary-only metastases, of whom 251 met inclusion criteria for outcome analyses. In the RCR cohort (n = 105), EFS and OS were not significantly different based on the number of lung metastases, whereas size of pulmonary metastases was significantly associated with EFS ( P = .022), but not OS. In the SIR cohort (n = 146), EFS and OS did not differ by the number or size of lung metastases. In multivariable models, neither number nor size of lung metastases was significantly associated with EFS or OS although 1q gain was significant (EFS P = .0015; OS P = .039) after adjustment for these factors. CONCLUSION: 1q gain is a superior prognostic indicator to pulmonary tumor burden in patients with FHWT with pulmonary-only metastasis.

With the advent of precision medicine and targeted therapies in cancer, new challenges in the statistical design of clinical trials have naturally emerged. Most randomized clinical trial designs incorporating predictive biomarkers (those associated with treatment efficacy) assume biomarkers are dichotomous, or dichotomize naturally continuous biomarkers upfront, or find cut points mid‐way through the trial to classify patients as biomarker‐positive or biomarker‐negative. However, these practices ignore or discard information about continuous and possible nonlinear or non‐monotone prognostic or predictive effects. In this article, we propose a novel adaptive enrichment trial design to handle continuous biomarkers with any effect shape, including Bayesian marker‐adaptive randomization. We demonstrate that this design can correctly make marker‐specific trial decisions with high efficiency, resulting in improved performance and patient‐centered decisions compared to adaptive cut‐point selection approaches without adaptive randomization that further ignore or oversimplify true underlying marker relationships.

Background: Health-related quality of life (HRQoL) was recently demonstrated to improve throughout therapy for high-risk pediatric Hodgkin lymphoma (HL); however, average scores may not reflect individual differences. This study aimed to identify subgroups of patients with similar HRQoL trajectories from pre- to post-therapy. Methods: AHOD1331 trial participants aged 11-20 (n = 268; mean [SD] age = 15.6 [1.9]; 48% male) completed the Child Health Ratings Inventories–Global scale (HRQoL) prior to treatment, after cycle 2, after cycle 5, and the end of treatment. Group-based trajectory models (GBTMs) identified latent clusters of individuals with similar HRQoL patterns over time. Multivariable multinomial logistic regression estimated the association between a priori defined characteristics and membership in trajectory-based groups. Log-rank tests examined differences in post-T4 progression-free survival (PFS) by trajectory groups. Results: GBTM identified 3 HRQoL groups: Group 1 (consistently unfavorable [25.7%]), Group 2 (moderate-and-increasing [44.8%]), and Group 3 (consistently favorable [29.5%]). Older age (odds ratio = 1.24, 95% confidence interval = 1.03 to 1.50; P = .022), female sex (2.48, 1.23 to 4.99; P = .011), and Hispanic ethnicity (2.31, 0.97 to 5.50; P = .059) were associated with increased odds of membership in Group 1 vs Group 3. Older age (1.18, 1.00 to 1.39; P = .038) and B-symptoms (2.18, 1.09 to 4.33; P = .027) were associated with increased odds of Group 2 membership vs Group 3. Group membership was not associated with post-T4 PFS. Conclusions: A subgroup of high-risk pediatric HL patients experience persistently poor HRQoL, starting at diagnosis and continuing through therapy. Age, female sex, Hispanic ethnicity, and B-symptoms were linked to worse HRQoL. These findings can help identify patients at higher risk for poor HRQoL and guide intervention. ClinicalTrials.gov: NCT02166463.

Objective: To evaluate age, TNW, or tumor diameter (TD) as continuous prognostic variables for outcomes in early stage FHWT after accounting for biology and treatment. Summary of Background Data: Patient age (< 2 vs. ≥ 2 years) and tumor nephrectomy weight (TNW; < 550g vs. ≥ 550 grams) have been used to risk stratify children with stage I favorable histology Wilms tumor (FHWT) on Children’s Oncology Group (COG) studies and select patients for omission of chemotherapy. Methods: Included patients had stage I or II FHWT per central review and were treated with nephrectomy only, EE4A, or DD4A on COG trials. Restricted cubic splines models were used to estimate the stage-specific effects of age, TNW, and TD on event-free survival (EFS) and overall survival (OS), accounting for treatment and biology. Results: In pooled analyses of 775 stage I and 936 stage II patients, age was not significantly associated with EFS or OS for stage I or II patients after accounting for adverse biology that is more prevalent with older age. Greater TNW and larger TD were associated with increased risk of relapse in stage I and increased risk of death in stage II, but not when restricted to patients less than 4 years old. Conclusions: Age, TNW, and TD are each prognostic for EFS or OS in some cohorts of patients with stage I or II FHWT. However, after accounting for adverse biology that becomes more prevalent at older ages, these factors are no longer independently prognostic. The next COG FHWT study will implement and validate these findings.

Understanding the patient experience of treatment toxicities and their impact on health-related quality of life from cancer treatments requires asking patients using patient-reported outcomes. Over the past 20 years, the National Institutes of Health has sponsored several tools—namely, Patient-Reported Outcomes Measurement Information System measures and the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events—for precisely this purpose: to ensure valid, reliable tools to collect and detect patient-reported toxicities or adverse events and their impact on health-related quality of life. These patient-reported outcomes measures have been widely incorporated in clinical trials for adults with cancer. Yet, despite considerable work developing and validating developmentally appropriate versions of these measures for pediatric and adolescent self-report, patient-reported outcomes inclusion in pediatric and adolescent and young adult clinical trials has lagged. Here, we discuss optimal strategies to integrate validated patient-reported outcomes tools and sound analytic methodologies in clinical trials for children and adolescent and young adults with cancer, highlighting lessons learned from recent successes and ongoing experiences developing and opening cross-network trials for children and adolescent and young adults through the Children’s Oncology Group for patients with classic Hodgkin lymphoma, osteosarcoma, and acute lymphoblastic leukemia.

A better understanding of positron emission tomography (PET) response in nodular lymphocyte‐predominant Hodgkin lymphoma (nLPHL) is critical for incorporating PET into prospective trials. PET scans from Children's Oncology Group study AHOD03P1 for patients less than 22 years with low‐risk nLPHL, treated with three cycles of doxorubicin, vincristine, prednisone, and cyclophosphamide chemotherapy, were retrospectively reviewed and assigned Deauville 5‐point scale (5PS) scores. Five‐year post‐PET event‐free survival was 90.1% (80% CI: 85.2%–93.4%) for PET‐negative (5PS 1–3) and 66.7% (80% CI: 36.4%–85.0%) for PET‐positive (5PS 4–5) patients. PET response after three cycles of low‐dose chemotherapy is predictive of relapse risk for low‐risk nLPHL.

As cancer has become better understood on the molecular level with the evolution of gene sequencing techniques, considerations for individualized therapy using predictive biomarkers (those associated with a treatment’s effect) have shifted to a new level. In the last decade or so, randomized “adaptive enrichment” clinical trials have become increasingly utilized to strike a balance between enrolling all patients with a given tumor type, versus enrolling only a subpopulation whose tumors are defined by a potential predictive biomarker related to the mechanism of action of the experimental therapy. In this review article, we review recent innovative design extensions and adaptations to adaptive enrichment designs proposed during the last few years in the clinical trial methodology literature, both from Bayesian and frequentist perspectives.

Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.