Adjusted Inference for Multiple Testing Procedure in Group-Sequential Designs.

Adjustment of statistical significance levels for repeated analysis in group-sequential trials has been understood for some time. Adjustment accounting for testing multiple hypotheses is also well understood. There is limited research on simultaneously adjusting for both multiple hypothesis testing and repeated analyses of one or more hypotheses.

Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure.

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model.

A gated group sequential design for seamless Phase II/III trial with subpopulation selection.

Background: Due to the high cost and high failure rate of Phase III trials where a classical group sequential design (GSD) is usually used, seamless Phase II/III designs are more and more popular to improve trial efficiency. A potential attraction of Phase II/III design is to allow a randomized proof-of-concept stage prior to committing to the full cost of a Phase III trial. Population selection during the trial allows a trial to adapt and focus investment where it is most likely to provide patient benefit.

Electronic common technical document submission with analysis using R.

Background: In clinical trial development, it is a critical step to submit applications, amendments, supplements, and reports on medicinal products to regulatory agencies. The electronic common technical document is the standard format to enable worldwide regulatory submission. There is a growing trend of using R for clinical trial analysis and reporting as part of regulatory submissions, where R functions, analysis scripts, analysis results, and all proprietary code dependencies are required to be included.

Log-Rank Test vs MaxCombo and Difference in Restricted Mean Survival Time Tests for Comparing Survival Under Nonproportional Hazards in Immuno-oncology Trials: A Systematic Review and Meta-analysis.

Importance: The log-rank test is considered the criterion standard for comparing 2 survival curves in pivotal registrational trials. However, with novel immunotherapies that often violate the proportional hazards assumptions over time, log-rank can lose power and may fail to detect treatment benefit. The MaxCombo test, a combination of weighted log-rank tests, retains power under different types of nonproportional hazards.

A unified framework for weighted parametric group sequential design.

Group sequential design (GSD) is widely used in clinical trials in which correlated tests of multiple hypotheses are used. Multiple primary objectives resulting in tests with known correlations include evaluating (1) multiple experimental treatment arms, (2) multiple populations, (3) the combination of multiple arms and multiple populations, or (4) any asymptotically multivariate normal tests. In this paper, we focus on the first three of these and extend the framework of the weighted parametric multiple test procedure from fixed designs with a single analysis per objective to a GSD setting where different objectives may be assessed at the same or different times, each in a group sequential fashion.

Multiplicity for a group sequential trial with biomarker subpopulations.

Biomarker subpopulations have become increasingly important for drug development in targeted therapies. The use of biomarkers has the potential to facilitate more effective outcomes by guiding patient selection appropriately, thus enhancing the benefit-risk profile and improving trial power. Studying a broad population simultaneously with a more targeted one allows the trial to determine the population for which a treatment is effective and allows a goal of making approved regulatory labeling as inclusive as is appropriate.

Using artificial intelligence tools in answering important clinical questions: The KEYNOTE-183 multiple myeloma experience.

The phase III, randomized, active-controlled, multicenter, open-label KEYNOTE-183 study (NCT02576977) evaluating pomalidomide and low dose dexamethasone (standard-of-care [SOC]) with or without pembrolizumab in patients with refractory or relapsed and refractory multiple myeloma (rrMM) was placed on full clinical hold by the US FDA on July 03, 2017 due to an imbalance in the number of deaths between arms. Clinically-led subgroup analyses are typically used to shed light on clinical findings. However, this approach is not always successful.

On the empirical choice of the time window for restricted mean survival time.

The t-year mean survival or restricted mean survival time (RMST) has been used as an appealing summary of the survival distribution within a time window [0, t]. RMST is the patient's life expectancy until time t and can be estimated nonparametrically by the area under the Kaplan-Meier curve up to t. In a comparative study, the difference or ratio of two RMSTs has been utilized to quantify the between-group-difference as a clinically interpretable alternative summary to the hazard ratio.

Pembrolizumab Exposure-Response Assessments Challenged by Association of Cancer Cachexia and Catabolic Clearance.

Purpose: To investigate the relationship of pembrolizumab pharmacokinetics (PK) and overall survival (OS) in patients with advanced melanoma and non-small cell lung cancer (NSCLC).

Patients And Methods: PK dependencies in OS were evaluated across three pembrolizumab studies of either 200 mg or 2 to 10 mg/kg every 3 weeks (Q3W). Kaplan-Meier plots of OS, stratified by dose, exposure, and baseline clearance (CL), were assessed per indication and study.

Baseline Tumor Size Is an Independent Prognostic Factor for Overall Survival in Patients with Melanoma Treated with Pembrolizumab.

The purpose of this study was to assess the association of baseline tumor size (BTS) with other baseline clinical factors and outcomes in pembrolizumab-treated patients with advanced melanoma in KEYNOTE-001 (NCT01295827). BTS was quantified by adding the sum of the longest dimensions of all measurable baseline target lesions. BTS as a dichotomous and continuous variable was evaluated with other baseline factors using logistic regression for objective response rate (ORR) and Cox regression for overall survival (OS).

A 2-in-1 adaptive phase 2/3 design for expedited oncology drug development.

We propose an adaptive design that allows us to expand an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program with fewer patients. Rather than the usual practice of increasing sample size with a less positive interim outcome, here we propose maintaining sample size with such a result and wait for fully mature data. The final Phase 2 data may be negative, may warrant a larger Phase 3 trial, or, in the extreme, could provide a definitively positive outcome.

Stepwise two-stage sample size adaptation.

Several adaptive design methods have been proposed to reestimate sample size using the observed treatment effect after an initial stage of a clinical trial while preserving the overall type I error at the time of the final analysis. One unfortunate property of the algorithms used in some methods is that they can be inverted to reveal the exact treatment effect at the interim analysis. We propose using a step function with an inverted U-shape of observed treatment difference for sample size reestimation to lessen the information on treatment effect revealed.

Information-based sample size re-estimation in group sequential design for longitudinal trials.

Group sequential design has become more popular in clinical trials because it allows for trials to stop early for futility or efficacy to save time and resources. However, this approach is less well-known for longitudinal analysis. We have observed repeated cases of studies with longitudinal data where there is an interest in early stopping for a lack of treatment effect or in adapting sample size to correct for inappropriate variance assumptions.

On efficient two-stage adaptive designs for clinical trials with sample size adjustment.

Group sequential designs are rarely used for clinical trials with substantial over running due to fast enrollment or long duration of treatment and follow-up. Traditionally, such trials rely on fixed sample size designs. Recently, various two-stage adaptive designs have been introduced to allow sample size adjustment to increase statistical power or avoid unnecessarily large trials.

The BATTLE trial: a bold step toward improving the efficiency of biomarker-based drug development.

Successful completion of the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial, reported in this issue of Cancer Discovery, is an important advance in the effort to improve clinical trial approaches to the simultaneous development of new therapeutics with matching diagnostic tests so that patients most likely to benefit from these therapies can be identified.

Viewpoints on the FDA draft adaptive designs guidance from the PhRMA working group.

The US Food and Drug Administration has recently released a draft guidance document on adaptive clinical trials. We comment on the document from the particular perspective of the authors as members of a PhRMA working group on this topic, which has interacted with FDA personnel on adaptive trial issue during recent years. We describe the activities and prior work of our working group, and use this as a basis to discuss the content of the guidance document as it relates to several issues of current relevance, such as data monitoring processes, adaptive dose finding, so-called seamless trial designs, and sample size reestimation.

Finding the right dose for cancer therapeutics--can we do better?

Unlike other diseases, dose-selection for cancer therapeutics is often based on the maximum-tolerated dose in phase 1 studies involving relatively few patients. In this issue of Clinical Cancer Research, Jain and colleagues provide evidence that lower doses may be as effective as maximum-tolerated doses in the treatment of cancer patients.

Fitting spending functions.

Group sequential monitoring is used to provide guidance on stopping a clinical trial in progress based on interim evaluation of its efficacy objectives. A trial could stop because an experimental regimen (1) is efficacious, (2) lacks any sign of efficacy, or (3) is specifically less efficacious than a control. Group sequential methods using alpha- and beta-spending functions (Biometrika 1983; 70:659-663) are often used to create stopping boundaries for test statistics for efficacy hypotheses computed at interim analyses.

Detection bias due to the effect of finasteride on prostate volume: a modeling approach for analysis of the Prostate Cancer Prevention Trial.

Background: The Prostate Cancer Prevention Trial (PCPT) demonstrated a 24.8% reduction in the 7-year prevalence of prostate cancer among patients treated with finasteride (5 mg daily) compared with that among patients treated with placebo; however, a 25.5% increase in the prevalence of high-Gleason grade tumors was observed, the clinical significance of which is unknown.

Optimal spending functions for asymmetric group sequential designs.

We present optimized group sequential designs where testing of a single parameter theta is of interest. We require specification of a loss function and of a prior distribution for theta. For the examples presented, we pre-specify Type I and II error rates and minimize the expected sample size over the prior distribution for theta.