Evidence map›Paper›PMID 40552251›Full record

ArticleThe annals of applied statistics2021

CAUSAL MEDIATION ANALYSIS FOR SPARSE AND IRREGULAR LONGITUDINAL DATA.

Shuxi Zeng, Stacy Rosenbaum, Susan C Alberts, Elizabeth A Archie, Fan Li

Abstract read
In one paragraph

Article in The annals of applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Mediation analysis with graph mediator.Biostatistics (Oxford, England) · 2025
    Article
  3. Article
  4. Article
  5. Practical challenges in mediation analysis: a guide for applied researchers.Health services & outcomes research methodology · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Social bonds do not mediate the relationship between early adversity and adult glucocorticoids in wild baboons.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  15. 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

5 authors.

Shuxi ZengDepartment of Statistical Science, Duke University.
Stacy RosenbaumDepartment of Anthropology, University of Michigan.
Susan C AlbertsDepartments of Biology and Evolutionary Anthropology, Duke University.
Elizabeth A ArchieDepartment of Biological Sciences, University of Notre Dame.
Fan LiDepartment of Statistical Science, Duke University.

Funding

THE RANGE OF MALE-FEMALE MORTALITY DIFFERENCESP01AG031719 · NIA · DUKE UNIVERSITY · PI ZENG, YI · 2009 to 2020
$10.2M
A life course perspective on the effects of cumulative early adversity on healthR01AG053330 · NIA · UNIVERSITY OF NOTRE DAME · PI Elizabeth Archie · 2017 to 2026
$3.0M
The biodemography of early adversity: social behavioral processes in a wild animal model.R01AG053308 · NIA · DUKE UNIVERSITY · PI ALBERTS, SUSAN C. · 2018 to 2022
$2.2M
Early adversity and DNA methylation in a primate model of stress and development.R01HD088558 · NICHD · DUKE UNIVERSITY · PI TUNG, JENNY · 2017 to 2021
$2.0M
NIA NIH HHS P01 AG031719NIA NIH HHS R01 AG053308NIA NIH HHS R01 AG053330NICHD NIH HHS R01 HD088558
6 · The paper itself

Abstract

Causal mediation analysis seeks to investigate how the treatment effect of an exposure on outcomes is mediated through intermediate variables. Although many applications involve longitudinal data, the existing methods are not directly applicable to settings where the mediator and outcome are measured on sparse and irregular time grids. We extend the existing causal mediation framework from a functional data analysis perspective, viewing the sparse and irregular longitudinal data as realizations of underlying smooth stochastic processes. We define causal estimands of direct and indirect effects accordingly and provide corresponding identification assumptions. For estimation and inference, we employ a functional principal component analysis approach for dimension reduction and use the first few functional principal components instead of the whole trajectories in the structural equation models. We adopt the Bayesian paradigm to accurately quantify the uncertainties. The operating characteristics of the proposed methods are examined via simulations. We apply the proposed methods to a longitudinal data set from a wild baboon population in Kenya to investigate the causal relationships between early adversity, strength of social bonds between animals and adult glucocorticoid hormone concentrations. We find that early adversity has a significant direct effect (a 9-14% increase) on females' glucocorticoid concentrations across adulthood but find little evidence that these effects were mediated by weak social bonds.

Indexed as

Causal inferencefunctional principal component analysislongitudinal datamediationsparse and irregular data

Identifiers

PMID40552251
PMCPMC12183660

What OpenQuestion holds

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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.