Bohdana Ratitch’s research while affiliated with University of Quebec in Montreal and other places

In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al ( Stat Med 2017;36: 136‐196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation.

Background: Assessment of reliability is one of the key components of the validation process designed to demonstrate that a novel clinical measure assessed by a digital health technology tool is fit-for-purpose in clinical research, care, and decision-making. Reliability assessment contributes to characterization of the signal-to-noise ratio and measurement error and is the first indicator of potential usefulness of the proposed clinical measure. Summary: Methodologies for reliability analyses are scattered across literature on validation of PROs, wet biomarkers, etc., yet are equally useful for digital clinical measures. We review a general modeling framework and statistical metrics typically used for reliability assessments as part of the clinical validation. We also present methods for the assessment of agreement and measurement error, alongside modified approaches for categorical measures. We illustrate the discussed techniques using physical activity data from a wearable device with an accelerometer sensor collected in clinical trial participants. Key messages: This paper provides statisticians and data scientists, involved in development and validation of novel digital clinical measures, an overview of the statistical methodologies and analytical tools for reliability assessment.

There has been much interest in the evaluation of heterogeneous treatment effects (HTE) and multiple statistical methods have emerged under the heading of personalized/precision medicine combining ideas from hypothesis testing, causal inference, and machine learning over the past 10-15 years. We discuss new ideas and approaches for evaluating HTE in randomized clinical trials and observational studies using the features introduced earlier by Lipkovich, Dmitrienko, and D'Agostino that distinguish principled methods from simplistic approaches to data-driven subgroup identification and estimating individual treatment effects and use a case study to illustrate these approaches. We identified and provided a high-level overview of several classes of modern statistical approaches for personalized/precision medicine, elucidated the underlying principles and challenges, and compared findings for a case study across different methods. Different approaches to evaluating HTEs may produce (and actually produced) highly disparate results when applied to a specific data set. Evaluating HTE with machine learning methods presents special challenges since most of machine learning algorithms are optimized for prediction rather than for estimating causal effects. An additional challenge is in that the output of machine learning methods is typically a "black box" that needs to be transformed into interpretable personalized solutions in order to gain acceptance and usability.

Background: The proliferation and increasing maturity of biometric monitoring technologies allow clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of traditional clinical settings. This includes capturing meaningful aspects of health in daily living and a more granular and objective manner compared to traditional tools in clinical settings. Summary: Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures. They are called upon to provide input into trial planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. Key messages: This paper discusses key considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that investigators may encounter while dealing with data from biometric monitoring technologies.

This article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the US FDA Oncology Center of Excellence and LUNGevity Foundation on January 14, 2021, and February 8, 2021. Diverse stakeholders including oncologists, patient advocates, experts from international regulatory agencies, academicians, and representatives of the pharmaceutical industry engaged in a discussion on how best to incorporate lessons learned during the COVID-19 pandemic into the design of future oncology trials. While recognizing that decentralized or hybrid cancer trials may increase variability associated with measurement error and potentially increase bias in treatment effect estimation, panel discussions highlighted the importance of flexibility for decreasing patient burden, which has the potential to increase access to and retention in cancer clinical trials and may broaden the representation of real-world patients in the trial setting.

The ICH E9 (R1) Addendum on “Estimands and Sensitivity Analysis in Clinical Trials (Step 4)” was finalized in November 2019 and subsequently implemented by many regulatory agencies, including FDA (May 2021). This article is based on a session organized to cover experience implementing the estimand framework, including its use, impact on drug/biologic development, common challenges and ways to address them, as well as keys to productive interdisciplinary collaboration.

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in Supplemental Materials.

UNSTRUCTURED The proliferation and increasing maturity of biometric monitoring technologies allows clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of clinical settings. This includes capturing meaningful aspects of health in daily living and in a more granular and objective manner compared to traditional tools in clinical settings. Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures and are called upon to provide input into study planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. This paper discusses statistical considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that statisticians and data scientists may encounter while dealing with data from biometric monitoring technologies.

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in supplemental materials.