Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.
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Novartis Pharma AG
Using principal stratification to address post-randomization events: A case study
In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.
Daniel Scharfstein Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Estimands and Causal Inference
Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis.
Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data
In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.
*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.
An opportunity to meet statisticians from across the pharmaceutical industry in a relaxed and informal setting. An exciting program of events and a chance to work in small groups on a data analysis challenge. Lunch provided.
A Non-PSI Event - Protecting confidentiality and privacy in clinical trial and medical data sets
We are increasingly living in a data driven world. Data are collected in many different ways for a variety of purposes. As such, concerns around protecting the privacy of individuals have increased in recent times.
A PSI Training Course - Practical Approaches to Designing Adaptive Clinical Trials
This hands-on course will provide a deep dive into 4 software packages used to design adaptive clinical trials.
The course will start by providing a general overview of adaptive designs, explaining the different type of adaptations possible and the benefits of each design. Following this, participants will be given the opportunity to have a go at designing trials in R (using RPACTS), EAST, FACTS, and nQuery.
PSI Training Course - Bayesian Practical Course using R and SAS
This practical training course will give a deep dive into performing Bayesian analyses in R and SAS. It is aimed at statisticians who need to be able to conduct Bayesian analyses as part of their day to day work. By the end of the course participants will be able to conduct their own analyses.
This webinar will address operational issues of paramount importance within the healthcare industry with a view to using statistics for the benefit of patients. In attending this webinar, you'll hear more about work being conducted to address some operational issues we face in the health care industrys e.g. patient rectuitment, drug supply and meeting NHS 18 week targets.
PSI Toxicology SIG workshop – 16th and 17th March 2020
The Toxicology SIG provides a forum for statisticians working in regulatory/investigative toxicology, as well as most other pre-clinical areas, to discuss issues and interact with one another.
This 1.5-day workshop will involve approximately 20 statisticians, focusing on discussions around “best practice” in the statistical analysis of various data types.
The afternoon of Day 1 will include a 4.5 hour Bayesian training course focused towards applications in toxicology/pre-clinical, provided by Prof. Dr. Katja Ickstadt and is included in the workshop fee.
The cost will be £270 including VAT per delegate, inclusive of food and one night’s accommodation (and the training course). The workshop is being held at the Crowne Plaza Hotel, Heathrow.
The agenda and topics that will be discussed are yet to be finalised, but please get in touch with firstname.lastname@example.org if you have suggestions. Full details will be circulated in the coming weeks.
This course is aimed at Statisticians and Programmers experienced in SAS, but little or no experience with R.
An Introduction to R studio and the R language, statistical graphics, programming statistical models, simulations and more…
Non-proportional hazards and applications in immuno-oncology
Designs of clinical trials with time to event primary endpoints usually rely on hazards being constant over time. A major challenge in immuno-oncology is the delayed onset of benefit with such therapies and the presence of non-proportional hazards. The impact of this needs to be accounted for in sample size calculations, analysis methodology and reporting. At this meeting, we will examine possible strategies to handle such features, which may not be fully known when the trial is initiated.
The ITIT course will take 25 delegates new to the industry on a complete drug development experience from discovery to marketing. They will visit 6 companies from October 2020 to July 2021 to learn about 6 topics from experts in their field. The ITIT course will have 6 sessions in continental Europe and 3 - 4 sessions in the UK. It promises to be a truly memorable course.