Evidence map›Paper›PMID 35849575›Full record

ArticlePLoS genetics2022

Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization.

Eleanor Sanderson, Tom G Richardson, Tim T Morris, Kate Tilling, George Davey Smith

Abstract read
In one paragraph

Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

64 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Review
  7. Article
  8. Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Eleanor SandersonMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.ORCID 0000-0001-5188-5775
Tom G RichardsonMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-7918-2040
Tim T MorrisMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.ORCID 0000-0001-8178-6815
Kate TillingMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.
George Davey SmithMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-1407-8314

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853Medical Research Council MC_UU_00011/1Medical Research Council MC_UU_00011/3
6 · The paper itself

Abstract

Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual's lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.

Indexed as

Genetic VariationMendelian Randomization AnalysisCausality

Identifiers

PMID35849575
PMCPMC9348730

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.