Evidence map›Paper›PMID 29912384›Full record

ArticleInternational journal of epidemiology2018

A Bayesian approach to investigate life course hypotheses involving continuous exposures.

Sreenath Madathil, Lawrence Joseph, Rebecca Hardy, Marie-Claude Rousseau, Belinda Nicolau

Abstract read
In one paragraph

Article in International journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–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

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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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.

Sreenath MadathilFaculty of Dentistry, McGill University, Montreal, QC, Canada.
Lawrence JosephDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
Rebecca HardyMRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK.
Marie-Claude RousseauFaculty of Dentistry, McGill University, Montreal, QC, Canada.
Belinda NicolauFaculty of Dentistry, McGill University, Montreal, QC, Canada.

Funding

CIHR MOP111207CIHR MOP 81172Medical Research Council MC_UU_12019/2
6 · The paper itself

Abstract

Background: Different hypotheses have been proposed in life course epidemiology on how a time-varying exposure can affect health or disease later in life. Researchers are often interested in investigating the probability of these hypotheses based on observed life course data. However, current techniques based on model/variable selection do not provide a direct estimate of this probability. We propose an alternative technique for a continuous exposure, using a Bayesian approach that has specific advantages, to investigate which life course hypotheses are supported by the observed data. Methods: We demonstrate the technique, the relevant life course exposure model (RLM), using simulations. We also analyse data from a case-control study on risk factors of oral cancer, with repeated measurements of betel quid chewing across life. We investigate the relative importance of chewing one quid of betel per day, at three life periods: ≤20 years, 21-40 years and above 40 years of age, on the risk of developing oral cancer. Results: RLM was able to correctly identify the life course hypothesis under which the data were simulated. Results from the case-control study showed that there was 74.3% probability that betel quid exposure earlier in life, compared with later, results in higher odds of developing oral cancer later in life. Conclusions: RLM is a useful option to identify the life course hypothesis supported by the observed data prior to the estimation of a causal effect.

Indexed as

Bayes TheoremModels, BiologicalModels, StatisticalAdultAge DistributionArecaCase-Control StudiesCausalityFemaleHumansIndiaMaleMouth NeoplasmsRisk FactorsYoung Adult

Identifiers

PMID29912384
PMCPMC6208282

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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.