Modeling tumor measurement data to predict overall survival (OS) in cancer clinical trials.

Introduction: Longitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST.

Principles of Good Clinical Trial Design.

Clinical trials are a fundamental component of medical research and serve as the main route to obtain evidence of the safety and efficacy of treatment before its approval. A trial's ability to provide the intended evidence hinges on appropriate design, background knowledge, trial rationale to sample size, and interim monitoring rules. In this article, we present some general design principles for investigators and their research teams to consider when planning to conduct a trial.

Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials.

Purpose: Missing data commonly occur in cancer clinical trials (CCT) and may hinder the search for alternative trial endpoints. We consider reasons for missing tumor measurement (TM) data in CCT and how missing TM data are typically handled. We explore the potential impact of missing TM data on predictive ability of a set of TM-based endpoints.

Discussion of Trial Designs for Biomarker Identification and Validation Through the Use of Case Studies.

With the launch of the National Cancer Institute's Precision Medicine Initiative in 2015, there has been a shift to trial designs that tailor health care solutions to individual patients by using a screening platform and by moving away from the one-trial/one-biomarker-at-a-time approach. To make precision medicine a reality, it is critical to identify and validate potential biomarkers to help select patients who will truly benefit from a targeted therapy. In this article, we discuss five trial designs: enrichment, umbrella, basket, subgroup, and window of opportunity.

Precision oncology: A new era of cancer clinical trials.

Traditionally, site of disease and anatomic staging have been used to define patient populations to be studied in individual cancer clinical trials. In the past decade, however, oncology has become increasingly understood on a cellular and molecular level, with many cancer subtypes being described as a function of biomarkers or tumor genetic mutations. With these changes in the science of oncology have come changes to the way we design and perform clinical trials.

Clinical trial designs incorporating predictive biomarkers.

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.

Clinical Utility of Metrics Based on Tumor Measurements in Phase II Trials to Predict Overall Survival Outcomes in Phase III Trials by Using Resampling Methods.

Purpose: Phase II clinical trials inform go/no-go decisions for proceeding to phase III trials, and appropriate end points in phase II trials are critical for facilitating this decision. Phase II solid tumor trials have traditionally used end points such as tumor response defined by Response Evaluation Criteria for Solid Tumors (RECIST). We previously reported that absolute and relative changes in tumor measurements demonstrated potential, but not convincing, improvement over RECIST to predict overall survival (OS).

Evaluating Continuous Tumor Measurement-Based Metrics as Phase II Endpoints for Predicting Overall Survival.

Background: We sought to develop and validate clinically relevant, early assessment continuous tumor measurement-based metrics for predicting overall survival (OS) using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 data warehouse.

Methods: Data from 13 trials representing 2096 patients with breast cancer, non-small cell lung cancer (NSCLC), or colorectal cancer were used in a complete case analysis.

Test on existence of histology subtype-specific prognostic signatures among early stage lung adenocarcinoma and squamous cell carcinoma patients using a Cox-model based filter.

Background: Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model.

The direct assignment option as a modular design component: an example for the setting of two predefined subgroups.

Background: A phase II design with an option for direct assignment (stop randomization and assign all patients to experimental treatment based on interim analysis, IA) for a predefined subgroup was previously proposed. Here, we illustrate the modularity of the direct assignment option by applying it to the setting of two predefined subgroups and testing for separate subgroup main effects.

Methods: We power the 2-subgroup direct assignment option design with 1 IA (DAD-1) to test for separate subgroup main effects, with assessment of power to detect an interaction in a post-hoc test.

Exploring the statistical and clinical impact of two interim analyses on the Phase II design with option for direct assignment.

Purpose: The primary goal of Phase II clinical trials is to understand better a treatment's safety and efficacy to inform a Phase III go/no-go decision. Many Phase II designs have been proposed, incorporating randomization, interim analyses, adaptation, and patient selection. The Phase II design with an option for direct assignment (i.

Evaluation of alternate categorical tumor metrics and cut points for response categorization using the RECIST 1.1 data warehouse.

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.

Choosing profile double-sampling designs for survival estimation with application to President's Emergency Plan for AIDS Relief evaluation.

Most studies that follow subjects over time are challenged by having some subjects who dropout. Double sampling is a design that selects and devotes resources to intensively pursue and find a subset of these dropouts, then uses data obtained from these to adjust naïve estimates, which are potentially biased by the dropout. Existing methods to estimate survival from double sampling assume a random sample.

A review of phase II trial designs for initial marker validation.

Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. In this review article, we focus on phase II designs for initial predictive biomarker validation to determine if a drug should be developed for an unselected patient population or for a biomarker-defined patient subset only. Several prospective designs for biomarker-directed therapy have been proposed, differing primarily in the study population, or randomization scheme, or both.

A 2-stage phase II design with direct assignment option in stage II for initial marker validation.

Biomarkers are critical to targeted therapies, as they may identify patients more likely to benefit from a treatment. Several prospective designs for biomarker-directed therapy have been previously proposed, differing primarily in the study population, randomization scheme, or both. Recognizing the need for randomization, yet acknowledging the possibility of promising but inconclusive results after a stage I cohort of randomized patients, we propose a 2-stage phase II design on marker-positive patients that allows for direct assignment in a stage II cohort.

Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials.

Purpose: The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival.

Experimental Design: Individual patient data from three North Central Cancer Treatment Group trials (N0026, n = 117; N9741, n = 1,109; and N9841, n = 332) were used.

A stochastic simulator of a blood product donation environment with demand spikes and supply shocks.

The availability of an adequate blood supply is a critical public health need. An influenza epidemic or another crisis affecting population mobility could create a critical donor shortage, which could profoundly impact blood availability. We developed a simulation model for the blood supply environment in the United States to assess the likely impact on blood availability of factors such as an epidemic.

Longitudinal evaluation of sleep-disordered breathing and sleep symptoms with change in quality of life: the Sleep Heart Health Study (SHHS).

Study Objectives: Findings from population studies evaluating the progression and incidence of sleep disordered breathing have shown evidence of a longitudinal increase in the severity of sleep disordered breathing. The present study evaluates the association among changes in sleep disordered breathing, sleep symptoms, and quality of life over time.

Design: Prospective cohort study.

Evaluation of school-based smoking-cessation interventions for self-described adolescent smokers.

Objective: The goal was to compare the efficacy of school-based, multisession, group smoking-cessation interventions versus a single group session in increasing quit rates among adolescent smokers.

Methods: Eight schools were assigned randomly to use 1 of 2 group smoking-cessation programs previously shown to increase quit rates among adolescents (Not on Tobacco [NOT] or Kickin' Butts). We reformatted the programs to twice-weekly 25- to 30-minute sessions delivered during lunch periods.

Evaluation of the optimal number of lesions needed for tumor evaluation using the response evaluation criteria in solid tumors: a north central cancer treatment group investigation.

PURPOSE In February 2000, the criteria for measuring tumor shrinkage as an indicator of antitumor activity were redefined by the Response Evaluation Criteria in Solid Tumors (RECIST). This resulted in simplifying bidimensional to unidimensional measurement of lesions. Under RECIST, all lesions, up to 10, must be measured.

Sampling-based approaches to improve estimation of mortality among patient dropouts: experience from a large PEPFAR-funded program in Western Kenya.

Background: Monitoring and evaluation (M&E) of HIV care and treatment programs is impacted by losses to follow-up (LTFU) in the patient population. The severity of this effect is undeniable but its extent unknown. Tracing all lost patients addresses this but census methods are not feasible in programs involving rapid scale-up of HIV treatment in the developing world.

Flexible Random Intercept Models for Binary Outcomes Using Mixtures of Normals.

Random intercept models for binary data are useful tools for addressing between-subject heterogeneity. Unlike linear models, the non-linearity of link functions used for binary data force a distinction between marginal and conditional interpretations. This distinction is blurred in probit models with a normally distributed random intercept because the resulting model implies a probit marginal link as well.