Modelling the association between patient characteristics and the change over time in a disease measure using observational cohort data

Add to your list(s) Download to your calendar using vCal

• Andrew Copas, Centre for sexual health & HIV research, UCL
• Tuesday 19 January 2010, 14:30-15:30
• Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge.

If you have a question about this talk, please contact Michael Sweeting.

L. Harrison, D. T. Dunn, H. Green and A. J. Copas Hub for Trials Methodology Research, MRC Clinical Trials Unit, London

In observational cohort studies we may wish to examine the associations between the fixed patient characteristics and the longitudinal changes from baseline in a repeated outcome measure. Many biological and other outcome measures are known to be subject to measurement error and biological variation. In an initial analysis we may fit a regression model to all outcome measurements, accounting for all the identified sources of variability, and see how the characteristics are linked to the change for typical patients. However, the characteristics may also be linked to different distributions of the underlying outcome value at baseline, which itself may be correlated with the change over time. Therefore, if we wish to examine the change over time for patients of different characteristics but with the same underlying baseline value then the initial approach is confounded by the baseline values. Furthermore, if we attempt to remove this confounding by including the observed baseline measure as a covariate in a model for later measurements, then this may provide an approximate solution but is likely to introduce some bias. We propose a method based on first following the initial approach but then, applying a correction to the parameter estimates. This allows the predicted trajectories to be plotted and valid significance tests of association with characteristics. Our approach is compared with other methods and illustrated through a simulation study and an analysis of the association between HIV -1 subtype and immunological response after starting antiretroviral therapy.

This talk is part of the MRC Biostatistics Unit Seminars series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• BHRU Annual Lecture 2015
• BHRU Annual Lecture 2016
• Biology
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Immunology
• Cambridge talks
• Cardiovascular Epidemiology Unit Special Seminars
• Chris Davis' list
• Department of Public Health and Primary Care
• Graduate-Seminars
• Interested Talks
• Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge
• Life Sciences
• Life Sciences
• ME Seminar
• MRC Biostatistics Unit Seminars
• Primary Care
• PublicHealth@Cambridge
• Trust & Technology Initiative - interesting events
• bld31
• my_list
• ndk22's list
• ob366-ai4er
• other talks
• primary care
• rp587

Note that ex-directory lists are not shown.