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Registration fees for this event are as follows:

\n- Members o
f PSI = Free of charge

\n- Non-Members of PSI = £\;20+VAT

To register for this event\, please **click here**. \
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The webinar aims to provide information on th e purpose and methods for covariate adjustment in randomized clinical tria ls (RCTs). The speakers will review and compare different approaches for a djusting for covariates in terms of the properties of the statistics\, est imands\, convergence and handling of missing data. The FDA's revised guida nce on adjusting for covariates in RCTs and relevant commentary will be di scussed with the objective to provide practical examples for analysis plan ning in studies. The speakers will offer insights into issues such as coll apsibility\, marginal and conditional effects and provide examples on how to use covariates to describe effects.

\n**Biography**

**Abstract**

\n Jonathan Bartlett

\n
(*London School of Hygiene &\; Tropical Medicine*)

Jonathan Bartlett is a Professor in Medical Statistics at the Lo ndon School of Hygiene &\; Tropical Medicine. His research interests ar e focused around missing data and causal inference methods\, and more rece ntly\, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University o f Bath\, and maintains a blog https:// thestatsgeek.com/

\n \n \n**An introduction to covari
ate adjustment in trials.\n **In this introductory
talk I will begin by describing the motivations for covariate adjustment
in randomised trials. I will then describe the usual way a covariate adjus
ted analysis is performed\, namely via fitting a suitable outcome regressi
on model\, highlighting the type of estimand such an approach targets and
the statistical assumptions it relies on. Next\, I will describe the stand
ardisation or G-computation approach to covariate adjustment\, as describe
d in the FDA&rsquo\;s covariate adjustment guidance. I will contrast it wi
th the usual approach in terms of efficiency\, robustness and in terms of
the estimand it targets.

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\n Florian
Voss (*Boehringer Ingelheim*)

Florian Voss is Ex pert Therapeutic Area (TA) Statistician at Boehringer Ingelheim. He works in all phases of clinical development and provides statistical consultatio n and advice for the statisticians working for TA Inflammation at BI. His is interested in applications of statistical methods in clinical developme nt\, e.g. paediatric extrapolation\, covariate adjustment\, interim analys es and inclusion of historical data.

\n \n < td valign="top" style="width: 523px\;">\n**Summary of
regulatory guidances (FDA\, EMA) on covariate adjustment and comments fro
m EFSPI/PSI Regulatory SIG.\n **In many clinical t
rials there are known baseline factors that are prognostic or predictive f
or outcomes in the trial. It is important to suitably account for such cov
ariates in the planning and statistical analysis of a clinical trial to im
prove sensitivity for treatment effects\, and to minimise the impact of an
y chance baseline imbalances between arms.

Regulatory agencies generally support inclusion of covariates in the primary analysis model. The ICH E9 guidance recommends to identify covariates that have an important influence on the primary endpoint and to account for them in th e analysis. More detailed guidance is provided in the EMA &ldquo\;Guidelin e on adjustment for baseline covariates in clinical trials&rdquo\; from 20 15 and the more recent FDA Guidance for the Industry &ldquo\;Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Product s&rdquo\; from 2021.

\nThis presentation will summarize the available regulatory guidance and the comments provided by the EFSPI/P SI regulatory SIG. We will review the difference between conditional and u nconditional treatment effects for linear and some non-linear models and d iscuss them in the context of ICH E9 (R1) &ldquo\;Estimands and Sensitivit y Analysis in Clinical Trials&rdquo\;.

\n \n

\n
Rhian Daniel

\n (*Cardiff University*)

Rhian Daniel is Professor of Statistics at the Division of Populat ion Medicine\, Cardiff University. Her research interests include several areas of causal inference methods and their application.

\n \n \n < p>\n

\n Tim Morris

\n (*MRC Clinical Trials Unit at UCL*)

Tim Morris is a principal research fellow based at the MRC Clinical Tr ials Unit at UCL. He works on the development\, evaluation and understandi ng of statistical methods. His interests include simulation studies\, hand ling missing data\, sensitivity analysis\, covariate adjustment\, estimand s\, and IPD meta-analysis.

\n \n \n**Planning a covariate
adjustment method for your SAP.\n **It has long b
een advised to adjust for covariates in the analysis of phase III clinical
trials. The key justifications are: 1) adjustment increases power\; 2) wh
en randomisation was stratified\, ignoring covariates in the analysis lead
s to incorrect estimates of uncertainty (such as confidence intervals that
are too wide).

In practice we need to define how we w ill adjust for covariates in a statistical analysis plan. The usual approa ch is direct adjustment\, where an outcome regression model includes the c ovariates and the treatment effect is estimated as a parameter of this mod el. Two alternative approaches that have received less attention from the trials community are standardisation and inverse-probability-of-treatment weighting (IPTW).

\nThis presentation will compare the t hree broad approaches in terms of planning which to write into a SAP. Begi nning with the summary measure attribute of the estimand\, I will discuss points to consider such as efficiency\, variance estimation\, convergence issues\, and handling of missing data. No single approach is best for all situations but standardisation and IPTW deserve far more consideration tha n they currently receive.

\n \n \n < tr>\n \n*
\n Seth Seegobin (<
em>AstraZeneca*)

Seth Seegobin is the Head of Biost atistics\, Vaccine and Immune Therapies at AstraZeneca. His statistical in terests include programming efficient simulations\, interim analyses and t he use of sampling error during trial design.

\n \n \n**
Immunogenicity and prognostic effects.\n **Immunob
ridging trials aim to infer vaccine effectiveness (in the absence of effic
acy data) by comparing the immune response of a vaccine candidate with an
approved vaccine.

Within an RCT\, typically we rely up on randomisation to safeguard against treatment arm imbalance for importan t response prognostic variables\, however the prioritisation of demographi c groups within national vaccine programmes can create several challenges for immunobridging trials that follow.

\nThis presentati on will summarize the challenges and possible solutions to accurately desc ribe and compare immunogenicity between booster and primary series treatme nt arms\, imbalanced with respect to prognostic baseline characteristics\, using covariate adjustment.

\n \n \n \n\n\;

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