University of Cambridge > Talks.cam > MRC Biostatistics Unit Seminars > Causal inference and stratified medicine: an illustration of trial designs incorporating biomarker information for mechanisms evaluation

Causal inference and stratified medicine: an illustration of trial designs incorporating biomarker information for mechanisms evaluation

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The development of personalised (stratified) medicine is intrinsically dependent on an understanding of treatment-effect mechanisms (effects on therapeutic targets that mediate the effect of the treatment on clinical outcomes). There is a need for novel clinical trial designs for the joint evaluation of treatment efficacy, the utility of predictive markers as indicators of treatment efficacy, and the meditational mechanisms proposed as the explanation of these effects. We review the problem of confounding (common causes) for the drawing of valid inferences concerning treatment-effect mechanisms, even when the data has been generated using a randomised controlled trial. We illustrate the potential of the predictive biomarker-stratified design, together with baseline measurement of all known prognostic markers, to enable us to evaluate both the utility of the predictive biomarker in such a stratification and, perhaps more importantly, to estimate how much of the treatment’s effect is actually explained by changes in the putative mediator. We call this a biomarker stratified efficacy and mechanisms evaluation (BS-EME) trial. The analysis strategy involves the use of instrumental variable regression, using the treatment by predictive biomarker interaction as an instrumental variable – a refined, subtle and potentially more powerful use of Mendelian randomisation.

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

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