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Interest in causal inference as a statistical field has grown tremendously over the past decade. While this is partially motivated by the growing availability of non-randomized observational data, it has also been realized that ideas from the field of causal inference provide a useful framework for inference in randomized studies. For example the ICH E9 addendum on estimands uses the counterfactual viewpoint from causal inference to define treatment effects in clinical trials.
In this training we will outline the basic ideas of causal inference (causal effect, potential outcomes, standardization and inverse probability weighting). We will also illustrate how it relates to questions and concepts encountered in randomized clinical trials (intent-to-treat, per-protocol analyses, covariate adjustments in regression analyses, ...) and the strategies defined in the ICH E9 addendum on estimands. While the causal inference framework is in many aspects aligned with pharmaceutical statistics traditions, there are also areas where the framework sheds new light on established traditions, which we will outline in this training.
Outline:
1) Introduction to causal inference and potential outcomes (~45min)
2) Causal inference in randomized clinical trials and drug development (~45min)
3) Estimation methods for causal inference: Standardization and inverse probability weighting, including connection to treatment effect parameters in regression models (~60min)
4) Application example: Treatment switching (~30min optional)
Instructor(s) background: All three instructors have extensive experience with implementation of the ICH E9 addendum over the past few years (including contribution to health authority interactions), and how causal inference ideas and techniques can be utilized to define estimands as well as estimation techniques reflecting clinical questions in randomized clinical trials. The instructors have presented trainings on this topic successfully on multiple occasions.
References
Akacha, M., Bretz, F., Ohlssen, D., Rosenkranz, G., and Schmidli, H. (2017). Estimands and Their Role in Clinical Trials, Statistics in Biopharmaceutical Research, 9, 268-271.
Degtyarev, E., ..., Bornkamp, B., et al. (2019) Estimands and the Patient Journey: Addressing the Right Question in Oncology Clinical Trials. JCO Precision Oncology 3, 1-10.
Magnusson, B., Schmidli, H., Rouyrre, N., Scharfstein D. (2019) Bayesian inference for a principal stratum estimand to assess the treatment effect in a subgroup characterized by postrandomization event occurrence, Statistics in Medicine, 38, 4761-4771.
Bornkamp, B., Bermann G. (2019) Estimating the treatment effect in a subgroup defined by an early post-baseline biomarker measurement in randomized clinical trials with time-to-event endpoint, Statistics in Biopharmaceutical Statistics, to appear, https://doi.org/10.1080/19466315.2019.1575280
Artificial intelligence (AI) or machine learning (ML) has been used in drug discovery in biopharmaceutical companies for nearly 20 years. More recently AI has also been used for the disease diagnosis and prognosis in healthcare. In analysis of clinical trial data, predicted individual patient outcomes for precision medicine, similarity based machine learning (SBML) has recently been proposed for clinical trials for oncology and rare disease without the requirement of big data. The course will focus on supervised learning, including similarity-based learning and deep learning neural networks. We will also introduce unsupervised, reinforcement, and evolutionary learning methods. In addition, initiatives and innovative thinking of AI addressing key challenges in pharmaceutical industry will be discussed. The short course aims at conceptual clarity and mathematical simplicity. R code will be provided with examples for implementation. The course materials are based on instructor’s upcoming book in Feb, 2020: Artificial Intelligence in Drug Development, Precision Medicine, and Healthcare.
The course will cover:
(1) Introduction to AI: Classic Statistics versus AI; Past, Current, and Future of AI in Drug Development, Medicine, and HealthCare
(2) Deep Learning Neural Networks: Convolutional Neural Network (CNN); Recurrent Neural Network (RNN); Long Short-term Memory Networks (LSTMs); Deep Belief Network (DBN)
(3) Similarity Based Method: Similarity-Based Machine Learning; Kernel Method; Nearest-Neighbors Method; Support Vector Machine
(4) Overview of unsupervised, Reinforcement, Collective Intelligence, and Evolutionary Learning Methods
Goals: attendees will learn common AI methods in drug development and medical research, and will be able to use the AI methods with R to analyze clinical trial and other data, and interpret the results.
This one-day short course will cover a variety of sequentially adaptive phase I-II clinical trial designs that use both efficacy and toxicity to optimize dose, the dose pair of a two-agent combination, two doses given in sequence, or dose and schedule. The course will begin with an explanation of fundamental flaws with the conventional paradigm that separates phase I and phase II, followed by an overview of phase I-II designs. The remainder of the course will cover specific designs, with each illustrated by a practical application, including basic design structure, establishing numerical values of design parameters and prior parameters, and computer simulations to establish operating characteristics. Examples will include designs based on either efficacy-toxicity probability trade-offs, elicited joint utilities of efficacy and toxicity, methods for dealing with late onset outcomes, personalized (precision) dose-finding, optimizing molecularly targeted agents, two-agent combination trials, and methods for dealing with drop-outs.
Outline
Morning Lectures (Professor Thall)
• Flaws with the Conventional Phase I ? Phase II Paradigm
• Phase I-II Trials: Using Both Efficacy and Toxicity
• Efficacy-Toxicity Trade-Off Based Designs
• Utility Based Designs
Afternoon Lectures (Professor Yuan)
• Model-assisted Designs
• Designs with Late Onset Outcomes
• Optimizing Molecularly Targeted and Immunotherapy Agents
• Personalized Dose Finding
Learning Objectives
The two over-arching objectives of this short course are (1) to show the attendees the many serious flaws with the conventional approach that separates phase I from phase II, so that they may avoid this approach when possible, and (2) to present practical alternative phase I-II designs and methods.
Text Book
Yuan Y, Nguyen HQ, Thall PF. Bayesian Designs for Phase I-II Clinical Trials. Chapman & Hall/CRC Biostatistics Series. 2016.
Under the 21st Century Cures Act, the FDA is directed to develop a program to evaluate how real-world evidence (RWE) can potentially be used to support approval of new indications for approved drugs or to support/satisfy post-approval study requirements. This brings new opportunities to utilize statistical innovations and advances that are critical to assess and address data quality as well as establish causal inference based on real world data (RWD) for regulatory decision-making. However, designing a valid RWD-based study and draw inferences about RWE face with numerous challenges such as confounding, treatment switching, missing information, etc. Propensity score methods offer powerful and flexible approaches to design and analyze RWE studies that can efficiently address aforementioned statistical challenges. This course will start with a general overview on causal inference and related methods including matching and inverse probability weighting (IPW). We will discuss how to design and analyze an RWD-based study using these methods under a question of interest that is coherent to ICH-E9 regulatory definition of estimand. This course will provide hands-on training based on simulated data and implementation codes in R. During the training, participants will be able to learn (1) efficient way to examine balance of covariate distributions in the target population before/after matching or weighting, how to (2) fit the different causal models, (3) deal with tails of the PS distribution, and (4) interpret study findings based on selected causal model. Then we will illustrate a case-study example that demonstrates a practical use of IPW for drug safety evaluation. A mock SAS code will be provided during the case-study illustration. This course will wrap up with introduction to some recently developed methodologies such as overlap weights that places emphasis on clinical equipoise and targeted maximum likelihood estimation that utilizes machine learning algorithms.
Immunotherapy has emerged as a promising treatment option for cancer in recent years. A major challenge in immune-oncology development is the delayed onset of treatment effects due to the mechanism of immunotherapy which violates the proportional hazard (PH) assumption. It is often referred as the non-proportional hazard (NPH) problem. In contrast to the PH assumption, NPH constitutes a broad class of alternative hypotheses. A suitable design for time to event data with potential NPH needs to be flexible enough to incorporate the uncertainty of NPH type and provide a robust inference. Different alternative design and analysis approaches for immune-oncology trials will be discussed. These include piecewise log-rank test, weighted log-rank test, combination tests and Kaplan-Meier based methods (e.g. restricted mean survival time). We will introduce a new MaxCombo test which provides robust power under different NPH scenarios. The short course will provide the analysis methodology, general design framework and strategies, sample size calculation, strategies of interim analysis, evaluation of operating characteristics and necessary steps for protocol implementation. All methodologies will be illustrated with real life examples and implementation with the available R package Simtrial. Outline: 1. Introduction 2. Alternative methods for design and analysis 3. Introduction of the R package Simtrial 4. Designing a trial with potential NPH 5. Design using Simtrial 6. Group sequential design with potential NPH 7. Summary and discussion Instructor(s): Dr. Satrajit Roychoudhury is a Senior Director and a member of Statistical Research and Innovation group in Pfizer Inc. His areas of research include the use of survival analysis and Bayesian methods. Dr. Keaven M Anderson is a Distinguished Scientist and head of Methodology Research biostatistics group at Merck. He has interest in group sequential design and survival analysis and applications of multiplicity control.
Ask a group of statisticians, “What do effective leaders do?” and you’ll hear a sweep of answers: Leaders set strategy; they demonstrate technical competence; they influence and collaborate across functions; they possess strong communication and negotiation skills. Then ask: “What should leaders do?” If the group is seasoned, you’ll likely hear one response: The leader’s singular job is to get results.
The recently endorsed Leadership- in- Practice Committee (LiPCom) of the Biopharmaceutical Section of the ASA seeks to present an interactive practical leadership workshop. The workshop will introduce real-life scenarios common to statistical practice that require leadership skills to drive results.
Most statisticians don’t enter their careers intending to become leaders and statistical leadership does not always accompany titles. A statistician becomes a leader by recognizing a problem that matters and can turn bold scientific, strategic, or organizational objectives into reality. Statisticians who can do this are increasingly valued in today’s data-driven organizations. As stated by 2012 ASA President Bob Rodriguez, “Leadership ability is a prerequisite for the growth of our field because statistics is an interdisciplinary endeavor and our success ultimately depends on getting others to understand and act on our work.”
This half-day workshop will provide a forum to explore multiple dimensions of leadership and expose ways that statisticians can leverage these leadership skills in driving results.
