Evidence map›Paper›PMID 40927350›Full record

ArticleJournal of applied statistics2025

Derivation of a multivariate longitudinal causal effects model.

Halima S Twabi, Samuel O M Manda, Dylan S Small, Hans-Peter Kohler

Abstract read
In one paragraph

Article in Journal of applied statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Halima S TwabiSchool of Natural and Applied Sciences, Department of Mathematical Science, University of Malawi, Zomba, Malawi.ORCID https://orcid.org/0000-0002-0166-9393
Samuel O M MandaDepartment of Statistics, University of Pretoria, Pretoria, South Africa.ORCID https://orcid.org/0000-0002-9672-3312
Dylan S SmallDepartment of Statistics, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0003-4928-2646
Hans-Peter KohlerDepartment of Sociology, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0003-0639-4349

Funding

STATISTICAL DATA ENCLAVE COREP30AG012836 · NIA · UNIVERSITY OF PENNSYLVANIA · PI COE, NORMA B · 1999 to 2024
$14.0M
Sci-Tech Core-Population Dynamics Research CenterP2CHD044964 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI EMILIO ALBERTO PARRADO · 2018 to 2026
$5.3M
Surviving an Epidemic: Families and Well-Being, Malawi 1998-2020R01HD087391 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI ANGLEWICZ, PHILIP ANTHONY, KOHLER, HANS-PETER · 2017 to 2022
$2.9M
NIA NIH HHS P30 AG012836NICHD NIH HHS P2C HD044964NICHD NIH HHS R01 HD087391Wellcome Trust
6 · The paper itself

Abstract

This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes. The proposed method is evaluated by simulation studies and with an application to estimate the effect of HIV positivity awareness on condom use and multiple sexual partners using the Malawi Longitudinal Study of Families and Health (MLSFH) data. The simulation study shows that the joint MSM-IPTW performs well with coverage within the expected 95% level for a large sample size (

Indexed as

causal effectsmarginal structural modelsMLSFHMultivariate outcomes

Identifiers

PMID40927350
PMCPMC12416008

What OpenQuestion holds

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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.