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 Healthcare decision-making requires reliable estimates of relative treatment effects. In an ideal scenario, these are provided by high-quality randomised controlled trials (RCTs) comparing the treatments of interest, in a relevant target population. However, it is often the case that head-to-head RCTs are not available between all relevant treatments. Instead, standard network meta-analysis and indirect comparison methods can be used to estimate relative treatment effects between treatments of interest by combining aggregate data from multiple studies, assuming that any variables that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods aim to relax this assumption by adjusting for differences in effect modifiers using available individual patient data from one or more trials.

In this talk, I will give an overview of different population adjustment methods including Matching Adjusted Indirect Comparison, Simulated Treatment Comparison, and a new approach, Multilevel Network Meta-Regression. These methods will be illustrated using an applied example, and I will discuss the results of an extensive simulation study designed to assess the performance of the methods in a range of realistic scenarios under various failures of assumptions.

This is a free event, which will be taking place online via Zoom. To register your interest in attending this talk please click HERE.

Forthcoming events

Infectious Disease Seminar Series: Hepatitis B diagnosis, prevention and treatment: laboratory approaches to the elimination agenda

Monday, 06 February 2023, 1pm to 2pm @ BDI Seminar Room LG 0-1, Old Road Campus, Headington, OX3 7LF

Richard Doll Seminar: Edgar Sydenstricker: Household Equivalence Scales and the Causes of Pellagra

Tuesday, 07 February 2023, 1pm to 2pm @ Richard Doll Lecture Theatre, Richard Doll Building, Old Road Campus, OX3 7LF

Ethox seminar- Feminist-Ethical Perspectives on Digital (Health) Technologies

Tuesday, 14 February 2023, 11am to 12.30pm @ Big Data Institute, Lower Ground Seminar Room 1, Oxford Population Heath, University of Oxford

Richard Doll Seminar - E-Freeze trial results

Tuesday, 14 February 2023, 1pm to 2pm @ Richard Doll Lecture Theatre, Richard Doll Building, Old Road Campus, OX3 7LF

Infectious Disease Seminar Series: Informing on Neisseria gonorrhoeae treatment and management through pathogen genomics

Monday, 20 February 2023, 1pm to 2pm @ BDI Seminar Room LG 0-1, Old Road Campus, Headington, OX3 7LF

Richard Doll Seminar- Triangulation of evidence in aetiological epidemiology: principles, prospects and limitations.

Tuesday, 21 February 2023, 1pm to 2pm @ Richard Doll Lecture Theatre, Richard Doll Building, Old Road Campus, OX3 7LF

Aetiological epidemiology is concerned with the identification of causal influences on disease risk. Randomized controlled trials are, when possible, the cornerstone of knowledge as to whether interventions based on aetiological studies are merited. It is not feasible to subject all of the many candidate causes to large-scale RCTs, however, even in situations where they are in principle possible. Triangulation of evidence is an approach that attempts to formally combine findings from different domains to strengthen causal inference. Triangulation embraces the variety of evidence thesis, that inferential strength depends not only on the quantity of available evidence, but also on its variety: the greater the variety, the stronger the resulting support. An essential condition is that the systematic errors and biases are unrelated across different study types. For example, the effect of raising circulating HDL cholesterol on the risk of coronary heart disease can be estimated from RCTs or through Mendelian randomization using genetic variants related to HDL level. Both the results of RCTs and Mendelian randomization studies could be biased. However, the potential biases in one study design would not influence estimates from the other approach: the biases are unrelated to each other. In observational epidemiology approaches that can be applied include the use of negative control exposures or outcomes; the deliberate use of data from contexts in which confounding structures differ; the use of instrumental variables and related approaches, such as regression discontinuity; quasi-experimental studies; the estimation of the expected magnitude of associations generated by confounding and the incorporation of mechanistic data, amongst others. Pre-registration of protocols for the triangulation of evidence increases confidence in the findings produced.