Evidence map›Paper›PMID 37884665›Full record

Observational studyInternational journal of obesity (2005)2023

On the estimation of the effect of weight change on a health outcome using observational data, by utilising the target trial emulation framework.

M Katsoulis, A G Lai, D K Kipourou, M Gomes, A Banerjee, S Denaxas, R T Lumbers, K Tsilidis, Maria Kostara, A Belot and 5 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in International journal of obesity (2005), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

15 authors.

M KatsoulisMRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK. m.katsoulis@ucl.ac.uk.ORCID 0000-0002-1756-9530
A G LaiInstitute of Health Informatics, University College London, London, UK.ORCID 0000-0001-8960-8095
D K KipourouInequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
M GomesDepartment of Applied Health Research, University College London, London, UK.
A BanerjeeInstitute of Health Informatics, University College London, London, UK.ORCID 0000-0001-8741-3411
S DenaxasInstitute of Health Informatics, University College London, London, UK.
R T LumbersInstitute of Health Informatics, University College London, London, UK.ORCID 0000-0002-9077-4741
K TsilidisDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.ORCID 0000-0002-8452-8472
Maria KostaraDepartment of Pediatrics, University Hospital of Ioannina, Ioannina, Greece.
A BelotInequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
C DaleDepartment of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
R SofatDepartment of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
C LeyratDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
H HemingwayInstitute of Health Informatics, University College London, London, UK.
K Diaz-OrdazDept of Statistical Science, Faculty of Maths & Physical Sciences, University College London, London, UK.

Funding

Cancer Research UK 18525Cancer Research UK 29018Medical Research Council MC_PC_20030Medical Research Council MC_PC_20051Medical Research Council MC_PC_20059Medical Research Council MR/T032448/1
6 · The paper itself

Abstract

BACKGROUND/

objectivesWhen studying the effect of weight change between two time points on a health outcome using observational data, two main problems arise initially (i) 'when is time zero?' and (ii) 'which confounders should we account for?' From the baseline date or the 1st follow-up (when the weight change can be measured)? Different methods have been previously used in the literature that carry different sources of bias and hence produce different results.

methodsWe utilised the target trial emulation framework and considered weight change as a hypothetical intervention. First, we used a simplified example from a hypothetical randomised trial where no modelling is required. Then we simulated data from an observational study where modelling is needed. We demonstrate the problems of each of these methods and suggest a strategy.

interventionsweight loss/gain vs maintenance.

resultsThe recommended method defines time-zero at enrolment, but adjustment for confounders (or exclusion of individuals based on levels of confounders) should be performed both at enrolment and the 1st follow-up.

conclusionsThe implementation of our suggested method [adjusting for (or excluding based on) confounders measured both at baseline and the 1st follow-up] can help researchers attenuate bias by avoiding some common pitfalls. Other methods that have been widely used in the past to estimate the effect of weight change on a health outcome are more biased. However, two issues remain (i) the exposure is not well-defined as there are different ways of changing weight (however we tried to reduce this problem by excluding individuals who develop a chronic disease); and (ii) immortal time bias, which may be small if the time to first follow up is short.

Indexed as

Outcome Assessment, Health CareWeight GainBiasHumans

Identifiers

PMID37884665
PMCPMC10663146

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Registered trials

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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.