Evidence map›Paper›PMID 25275571›Full record

ArticleEpidemiology (Cambridge, Mass.)2015

Time at risk and intention-to-treat analyses: parallels and implications for inference.

Sunni L Mumford, Enrique F Schisterman, Stephen R Cole, Daniel Westreich, Robert W Platt

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2015. 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. Observational
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Sunni L MumfordFrom the aEpidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD; bDepartment of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC; and cDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
Enrique F Schisterman
Stephen R Cole
Daniel Westreich
Robert W Platt

Funding

ROC Curve MethodologyZIAHD008761 · NICHD · EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT · PI SCHISTERMAN, ENRIQUE F. · 2009 to 2021
$2.1M
Intramural NIH HHS ZIA HD008761
6 · The paper itself

Abstract

Although the standard recommendation is to exclude person-time not at risk (ie, time during which the outcome could not have occurred) from the denominators of disease rates, there are scenarios where person-time not at risk should be included. In particular, we draw an analogy between including person-time not at risk and intention-to-treat (ITT) analyses of randomized trials, and excluding person-time not at risk and compliance-corrected analysis of these same trials. Excluding person-time not at risk is appropriate when addressing questions of the biologic or mechanistic effects of an exposure, whereas the ITT-type approach typically addresses questions regarding the effect of an exposure under observed compliance patterns. The choice of approach directly affects the causal question being addressed and subsequent inference, with potential implications for public health. When interested in estimating treatment effects that allow and account for potential noncompliance, or where the exposure may be associated with the time at risk, we argue that person-time not at risk should be included. In the case of time to pregnancy, although the ITT-type analysis may underestimate the biological fecundity of the population, it may also yield an answer to a question that is of more interest to couples trying to become pregnant.

Indexed as

Intention to Treat AnalysisStatistics as TopicTime-to-PregnancyAdultFemaleHumansPregnancyResearch Design

Identifiers

PMID25275571
PMCPMC6191842

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.