Evidence map›Paper›PMID 35921210›Full record

ArticleAmerican journal of epidemiology2023

Is the Product Method More Efficient Than the Difference Method for Assessing Mediation?

Chao Cheng, Donna Spiegelman, Fan Li

Abstract read
In one paragraph

Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Correcting for bias due to mismeasured exposure in mediation analysis with a survival outcome.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. 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

3 authors.

Chao Cheng
Donna Spiegelman
Fan Li

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mediation analysis is widely used in biomedical research to quantify the extent to which the effect from an exposure on a health outcome is through a mediator and the extent to which the effect is direct. A traditional approach for quantifying mediation is through the difference method. The other popular approach uses a counterfactual framework from which the product method arises. However, there is little prior work to articulate which method is more efficient for estimating 2 key quantities in mediation analysis, the natural indirect effect and mediation proportion. To fill in this gap, we investigated the asymptotic relative efficiency for mediation measure estimators given by the product method and the difference method. We considered 4 data types characterized by continuous and binary mediators and outcomes. Under certain conditions, we show analytically that the product method is equally efficient to the difference method, or more efficient. However, our numerical studies demonstrate that the difference method is usually at least 90% as efficient as the product method under realistic scenarios in epidemiologic research, especially for estimating the mediation proportion. We demonstrate the efficiency results by analyzing the MaxART study (Eswatini, 2014-2017), which aimed to evaluate the effectiveness of the early access to antiretroviral therapy among human immunodeficiency virus-positive patients.

Indexed as

Biomedical ResearchMediation AnalysisEpidemiologic StudiesEswatiniHumansModels, Statisticalasymptotic relative efficiencymediation analysismediation proportion

Identifiers

PMID35921210
PMCPMC10144745

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.