Stephen Burgess

Medical statistician, work with genetic data to disentangle causation from correlation. Author of book on Mendelian randomization.

[Stephen Burgess @stevesphd]

Guidelines on performing Mendelian randomization investigations written by an all-star line-up of MR researchers are now available on Wellcome Open Research: https://t.co/tdg3JHX58K - represents a consensus statement after 12+ months of deliberation. Comments welcome!

[ASHG @GeneticsSociety]

In their recent @AJHGNews Review, @karhunen_v, @stevesphd, & co discuss the key considerations and provide advice to produce a higher standard in planning, conducting, reviewing, and interpreting cis-Mendelian randomization studies: https://t.co/mVInDXMFvY #ASHG #HumanGenetics

[Stephen Burgess @stevesphd]

Thanks to all co-authors, and also to reviewers who helped and supported this work (including @kauralasoo, who kindly self-identified!) - comments and feedback welcome!

[Stephen Burgess @stevesphd]

Our review "Integrating genetic data with biological insight: A practical guide to cis-Mendelian randomization" is now published at @AJHGNews - led by @karhunen_v and @BarWoolf with critical insight from @dpsg108 and Pallav Bhatnagar. Thread follows:

[Stephen Burgess @stevesphd]

Cis-MR studies are not intrinsically superior to genome-wide MR studies, and algorithmically-performed cis-MR analyses will rarely be optimal. But when performed with care, cis-MR is a powerful tool to inform about putative causal effects.

[Stephen Burgess @stevesphd]

Thanks to Ash for leading this work, and to Frank DiTraglia for asking difficult questions about the statistical methodology. All comments (and suggestions for applications) welcome!

[Stephen Burgess @stevesphd]

New pre-print on treatment effect heterogeneity led by Ash Patel: "Efficient semiparametric estimation of marginal treatment effects with genetic instrumental variables" available at https://t.co/x6C4vSKGE4. Brief summary:

[Stephen Burgess @stevesphd]

Lots of interesting maths under the hood here in terms of flexibly estimating the function defining individuals' stickiness to change their behaviour (the propensity score), and ensuring our estimates are robust and efficient to the specification of this function.