The New England Journal of Statistics in Data Science

We consider the problem of developing flexible and parsimonious biomarker combinations for cancer early detection in the presence of variable missingness at random. Motivated by the need to develop biomarker panels in a cross-institute pancreatic cyst biomarker validation study, we propose logic-regression based methods for feature selection and construction of logic rules under a multiple imputation framework. We generate ensemble trees for classification decision, and further select a single decision tree for simplicity and interpretability. We demonstrate superior performance of the proposed methods compared to alternative methods based on complete-case data or single imputation. The methods are applied to the pancreatic cyst data to estimate biomarker panels for pancreatic cysts subtype classification and malignant potential prediction.

The continuation-ratio (CR) model is frequently used in dose response studies to model a three-category outcome as the dose levels vary. Design issues for a CR model defined on an unrestricted dose interval have been discussed for estimating model parameters or a selected function of the model parameters. This paper uses metaheuristics to address design issues for a CR model defined on any compact dose interval when there are one or more objectives in the study and some are more important than others. Specifically, we use an exemplary nature-inspired metaheuristic algorithm called particle swarm optimization (PSO) to find locally optimal designs for estimating a few interesting functions of the model parameters, such as the most effective dose ($MED$), the maximum tolerated dose ($MTD$) and for estimating all parameters in a CR model. We demonstrate that PSO can efficiently find locally multiple-objective optimal designs for a CR model on various dose intervals and a small simulation study shows it tends to outperform the popular deterministic cocktail algorithm (CA) and another competitive metaheuristic algorithm called differential evolutionary (DE). We also discuss hybrid algorithms and their flexible applications to design early Phase 2 trials or tackle biomedical problems, such as different strategies for handling the recent pandemic.

Traditionally, research in nutritional epidemiology has focused on specific foods/food groups or single nutrients in their relation with disease outcomes, including cancer. Dietary pattern analysis have been introduced to examine potential cumulative and interactive effects of individual dietary components of the overall diet, in which foods are consumed in combination. Dietary patterns can be identified by using evidence-based investigator-defined approaches or by using data-driven approaches, which rely on either response independent (also named “a posteriori” dietary patterns) or response dependent (also named “mixed-type” dietary patterns) multivariate statistical methods. Within the open methodological challenges related to study design, dietary assessment, identification of dietary patterns, confounding phenomena, and cancer risk assessment, the current paper provides an updated landscape review of novel methodological developments in the statistical analysis of a posteriori/mixed-type dietary patterns and cancer risk. The review starts from standard a posteriori dietary patterns from principal component, factor, and cluster analyses, including mixture models, and examines mixed-type dietary patterns from reduced rank regression, partial least squares, classification and regression tree analysis, and least absolute shrinkage and selection operator. Novel statistical approaches reviewed include Bayesian factor analysis with modeling of sparsity through shrinkage and sparse priors and frequentist focused principal component analysis. Most novelties relate to the reproducibility of dietary patterns across studies where potentialities of the Bayesian approach to factor and cluster analysis work at best.

Basket trials have captured much attention in oncology research in recent years, as advances in health technology have opened up the possibility of classification of patients at the genomic level. Bayesian methods are particularly prevalent in basket trials as the hierarchical structure is adapted to basket trials to allow for information borrowing. In this article, we extend the Bayesian methods to basket trials with treatment and control arms for continuous endpoints, which are often the cases in clinical trials for rare diseases. To account for the imbalance in the covariates which are potentially strong predictors but not stratified in a randomized trial, our models make adjustments for these covariates, and allow different coefficients across baskets. In addition, comparisons are drawn between two-stage design and one-stage design for the four Bayesian methods. Extensive simulation studies are conducted to examine the empirical performance of all models under consideration. A real data analysis is carried out to further demonstrate the usefulness of the Bayesian methods.

Phase I trials investigate the toxicity profile of a new treatment and identify the maximum tolerated dose for further evaluation. Most phase I trials use a binary dose-limiting toxicity endpoint to summarize the toxicity profile of a dose. In reality, reported toxicity information is much more abundant, including various types and grades of adverse events. Building upon the i3+3 design (Liu et al., 2020), we propose the Ti3+3 design, in which the letter “T” represents “total” toxicity. The proposed design takes into account multiple toxicity types and grades by computing the toxicity burden at each dose. The Ti3+3 design aims to achieve desirable operating characteristics using a simple statistics framework that utilizes“toxicity burden interval” (TBI). Simulation results show that Ti3+3 demonstrates comparable performance with existing more complex designs.

Platform trials are multiarm clinical studies that allow the addition of new experimental arms after the activation of the trial. Statistical issues concerning “adding new arms”, however, have not been thoroughly discussed. This work was motivated by a “two-period” pediatric osteosarcoma study, starting with two experimental arms and one control arm and later adding two more pre-planned experimental arms. The common control arm will be shared among experimental arms across the trial. In this paper, we provide a principled approach, including how to modify the critical boundaries to control the family-wise error rate as new arms are added, how to re-estimate the sample sizes and provide the optimal control-to-experimental arms allocation ratio, in terms of minimizing the total sample size to achieve a desirable marginal power level. We examined the influence of the timing of adding new arms on the design’s operating characteristics, which provides a practical guide for deciding the timing. Other various numerical evaluations have also been conducted. A method for controlling the pair-wise error rate (PWER) has also been developed. We have published an R package, PlatformDesign, on CRAN for practitioners to easily implement this platform trial approach. A detailed step-by-step tutorial is provided in Appendix A.2.

Non-inferiority (NI) clinical trials’ goal is to demonstrate that a new treatment is not worse than a standard of care by a certain amount called margin. The choice of non-inferiority margin is not straightforward as it depends on historical data, and clinical experts’ opinion. Knowing the “true”, objective clinical margin would be helpful for design and analysis of non-inferiority trials, but it is not possible in practice. We propose to treat non-inferiority margin as missing information. In order to recover an objective margin, we believe it is essential to conduct a survey among a group of representative clinical experts. We introduce a novel framework, where data obtained from a survey are combined with NI trial data, so that both an estimated clinically acceptable margin and its uncertainty are accounted for when claiming non-inferiority. Through simulations, we compare several methods for implementing this framework. We believe the proposed framework would lead to better informed decisions regarding new potentially non-inferior treatments and could help resolve current practical issues related to the choice of the margin.

In diagnostic imaging drug developments, the imaging scan read data in controlled imaging drug clinical trials includes test positive and test negative. Broadly speaking, the standard of reference data are either presence or absence of a disease or clinical condition. Together, these data are used to assess the diagnostic performance of an investigational imaging drug in a controlled imaging drug clinical trial. For those imaging scan read data that cannot be called positive/negative, the “indeterminate” category is commonly used to cover imaging results that may be considered intermediate, indeterminate, or uninterpretable. Similarly, for those standard of reference data that cannot be categorized into presence/absence including uncollected or unavailable reference standard data, the “indeterminate” category may be used. Historically, little attention has been paid to the indeterminate imaging scan read data as they are generally rare or considered irrelevant though they are related to scanned subjects and can be informative. Subjects lack the standard of reference are simply excluded as such the study only reports the analysis results in subjects with available standard of reference data, known as completer analysis, similar to evaluable subjects seen in controlled trials for drug developments.

Master protocol is a type of trial designs where multiple therapies and/or multiple disease populations can be investigated in the same trial. A shared control can be used for multiple therapies to gain operational efficiency and gain attraction to patients. To balance between controlling for false positive rate and having adequate power for detecting true signals, the impact of False Discovery Rate (FDR) is evaluated when multiple investigational drugs are studied in the master protocol. With the shared control group, the “random high” or “random low” in the control group can potentially impact all hypotheses testing that compare each of the test regimens and the control group in terms of probability of having at least one positive hypothesis outcome, or multiple positive outcomes. When regulatory agencies make the decision of approving or declining one or more regimens based on the master protocol design, this introduces a different type of error: simultaneous false-decision error. In this manuscript, we examine in detail the derivations and properties of the simultaneous false-decision error in the master protocol with shared control under the framework of FDR. The simultaneous false-decision error consists of two parts: simultaneous false-discovery rate (SFDR) and simultaneous false non-discovery rate (SFNR). Based on our analytical evaluation and simulations, the magnitude of SFDR and SFNR inflation is small. Therefore, the multiple error rate controls are generally adequate, further adjustment to a pre-specified level on SFDR or SFNR or reduce the alpha allocated to each individual treatment comparison to the shared control is deemed unnecessary.