S3RI Seminar - Covariate adjustment and prediction of mean response in randomised trials, Jonathan Bartlett (AstraZeneca) Seminar

Time:: 14:00 - 15:00
Date:: 26 October 2017
Venue:: Lecture Theatre 8B, Room 8033, Building 54, Mathematical Sciences, University of Southampton, Highfield Campus, SO17 1BJ

For more information regarding this seminar, please email Helen Ogden at H.Ogden@southampton.ac.uk .

Event details

A key quantity which is almost always reported from a randomised trial is the mean outcome in each treatment group. When baseline covariates are collected, these can be used to adjust these means to account for imbalance in the baseline covariates between groups, thereby resulting in a more precise estimate. Qu and Luo (DOI: 10.1002/pst.1658) recently described an approach for estimating baseline adjusted treatment group means which, when the outcome model is non-linear (e.g. logistic regression), is more appropriate than the conventional approach which predicts the mean outcome for each treatment group, setting the baseline covariates to their mean values.

In this talk I will first emphasize that when the outcome model is non-linear, the aforementioned `conventional’ approach estimates a different quantity than the unadjusted group means and `Qu and Luo’ estimator. I will then describe how for many commonly used outcome model types, the Qu and Luo estimates are unbiased even when the outcome model is misspecified. Qu and Luo described how standard errors and confidence intervals can be calculated for these estimates, but treated the baseline covariates as fixed constants. When, as is usually the case in trials, the baseline covariates of patients would not be fixed in repeated sampling, I show that these standard errors are too small. I will describe a simple modification to their approach which provides valid standard errors and confidence intervals. I will also describe an estimator of the marginal means which exploits baseline covariate yet remains always consistent under misspecification of a working model for the outcome.

I will discuss the impact of stratified randomisation and missing outcomes on the preceding results, and give suggestions for when baseline adjusted means may or may not be preferred to unadjusted means. The analytical results will be illustrated through simulations and application to a recently conducted trial with recurrent events analysed using negative binomial regression.

The seminar will also be available via a live web-cast at
https://coursecast.soton.ac.uk/Panopto/Pages/Viewer.aspx?id=7fc35625-f5cb-42a4-a111-8bf1b4c91354

Speaker information

Jonathan Bartlett , AstraZeneca. Jonathan Bartlett is currently employed by AstraZeneca, as a Statistical Science Director in the Statistical Innovation Group. Previous to this he was a lecturer at the London School of Hygiene & Tropical Medicine. He is currently Vice-Chair of the Medical Section of the Royal Statistical Society, and an Associate Editor for the journal Biostatistics.