Evidence map›Paper›PMID 38058013›Full record

ArticleBiostatistics (Oxford, England)2024

A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes.

Pantelis Samartsidis, Shaun R Seaman, Abbie Harrison, Angelos Alexopoulos, Gareth J Hughes, Christopher Rawlinson, Charlotte Anderson, André Charlett, Isabel Oliver, Daniela De Angelis

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biostatistics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Pantelis SamartsidisMRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.ORCID 0000-0002-4491-9655
Shaun R SeamanMRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
Abbie HarrisonUK Health Security Agency, London, E14 4PU, UK.
Angelos AlexopoulosMRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
Gareth J HughesUK Health Security Agency, London, E14 4PU, UK.
Christopher RawlinsonUK Health Security Agency, London, E14 4PU, UK.
Charlotte AndersonUK Health Security Agency, London, E14 4PU, UK.
André CharlettUK Health Security Agency, London, E14 4PU, UK.
Isabel OliverUK Health Security Agency, London, E14 4PU, UK.
Daniela De AngelisMRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.

Funding

Medical Research Council MC_UU_00002/11
6 · The paper itself

Abstract

Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England's Test and Trace programme for COVID-19.

Indexed as

Bayes TheoremCOVID-19CausalityFactor Analysis, StatisticalHumansMarkov ChainsModels, StatisticalMonte Carlo MethodMultivariate AnalysisObservational Studies as TopicCausal inferenceContact tracingData augmentationFactor analysisPolicy evaluation

Identifiers

PMID38058013
PMCPMC11247182

What Socratic 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 Socratic graph.