Evidence map›Paper›PMID 23569196›Full record

ArticleInternational journal of epidemiology2013

Markov chain Monte Carlo: an introduction for epidemiologists.

Ghassan Hamra, Richard MacLehose, David Richardson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 25 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed, 25 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

111 citing papers in PubMed, 25 syntheses or guidelines pooled it.

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  19. Comparative efficacy and safety of the treatment by Omalizumab for chronic idiopathic urticaria in the general population: A systematic review and network meta-analysis.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024
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51 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

3 authors.

Ghassan HamraDivision of Environment and Radiation, International Agency for Research on Cancer, Lyon, France. hamrag@fellows.iarc.fr
Richard MacLehose
David Richardson

Funding

Multilevel latent class and social network models for observational adolescent obU01HD061940 · NICHD · UNIVERSITY OF MINNESOTA · PI WALL, MELANIE M · 2009 to 2010
$632k
Evaluation of Tritium: Relative Biological Effectiveness and Cancer MortalityR03OH009800 · OH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI RICHARDSON, DAVID B · 2010 to 2011
$138k
NICHD NIH HHS 1U01-HD061940NIEHS NIH HHS ES07018NIOSH CDC HHS 1R03OH009800-01
6 · The paper itself

Abstract

Markov Chain Monte Carlo (MCMC) methods are increasingly popular among epidemiologists. The reason for this may in part be that MCMC offers an appealing approach to handling some difficult types of analyses. Additionally, MCMC methods are those most commonly used for Bayesian analysis. However, epidemiologists are still largely unfamiliar with MCMC. They may lack familiarity either with he implementation of MCMC or with interpretation of the resultant output. As with tutorials outlining the calculus behind maximum likelihood in previous decades, a simple description of the machinery of MCMC is needed. We provide an introduction to conducting analyses with MCMC, and show that, given the same data and under certain model specifications, the results of an MCMC simulation match those of methods based on standard maximum-likelihood estimation (MLE). In addition, we highlight examples of instances in which MCMC approaches to data analysis provide a clear advantage over MLE. We hope that this brief tutorial will encourage epidemiologists to consider MCMC approaches as part of their analytic tool-kit.

Indexed as

Epidemiologic StudiesMarkov ChainsMonte Carlo MethodBayes TheoremComputer SimulationHumansLikelihood Functions

Identifiers

PMID23569196
PMCPMC3619958

What OpenQuestion holds

Textmetadata
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.