ArticleAmerican journal of epidemiology2016
Invited Commentary: An Interdisciplinary Approach for Policy Evaluation.
Article in American journal of epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Basu et al. Respond to "Interdisciplinary Approach for Policy Evaluation".American journal of epidemiology · 2016Article
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Authors and funding
2 authors.
Funding
Abstract
Evidence-based policymaking is becoming the norm, but how do we acquire the evidence to inform policies? In their article in the present issue of the Journal, Basu et al. (Am J Epidemiol. 2016;183(6):531-538) used difference-in-difference-in-differences models and a synthetic control approach to examine the effects of the 1996 welfare reforms on health outcomes among single mothers. In the present commentary, we discuss the limitations of observational studies for policy evaluation. Difference-in-differences models, from the field of economics, offer a rigorous approach to cope with those limitations.
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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.