ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2014
Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- Randomized clinical trial protocol: Acceptability and feasibility of combination treatment for cervical precancer among South African women living with HIV (ACT 2).Contemporary clinical trials · 2026Trial
- Article
- Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts.Epidemiology (Cambridge, Mass.) · 2024Article
- Sensitivity analysis for causality in observational studies for regulatory science.Journal of clinical and translational science · 2023Article
- BOUNDS ON THE CONDITIONAL AND AVERAGE TREATMENT EFFECT WITH UNOBSERVED CONFOUNDING FACTORS.Annals of statistics · 2022Article
- Clarifying the causes of consistent and inconsistent findings in genetics.Genetic epidemiology · 2022Review
- Nonparametric inference of complier quantile treatment effects in randomized trials with imperfect compliance.Biostatistics & epidemiology · 2022Article
- Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial.Journal of causal inference · 2020Article
- In Pursuit of Evidence in Air Pollution Epidemiology: The Role of Causally Driven Data Science.Epidemiology (Cambridge, Mass.) · 2020Article
- Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail.Human genetics · 2020Article
- Comment.Journal of the American Statistical Association · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.