ArticleMedical care2019
Falsification Tests for Instrumental Variable Designs With an Application to Tendency to Operate.
Article in Medical care, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Reinforcement learning recommends early vasopressin in septic shock: Implications of the OVISS study.Journal of intensive medicine · 2026Article
- Conceptualizing Experimental Controls Using the Potential Outcomes Framework.The American statistician · 2025Article
- Reading and conducting instrumental variable studies: guide, glossary, and checklist.BMJ (Clinical research ed.) · 2024Article
- Doubly robust machine learning-based estimation methods for instrumental variables with an application to surgical care for cholecystitis.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- The Application of Mendelian Randomization in Cardiovascular Disease Risk Prediction: Current Status and Future Prospects.Reviews in cardiovascular medicine · 2024Review
- The COVID-19 pandemic and temporal change in metabolic risk factors for cardiovascular disease: A natural experiment within the HELIUS study.SSM - population health · 2023Article
- Mendelian randomization.Nature reviews. Methods primers · 2022Article
- The Influence of Increasing Levels of Provider-Patient Discussion on Quit Behavior: An Instrumental Variable Analysis of a National Survey.International journal of environmental research and public health · 2021Article
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
backgroundInstrumental variable (IV) methods are becoming an increasingly important tool in health services research as they can provide consistent estimates of causal effects in the presence of unobserved confounding. However, investigators must provide justifications that the IV is independent with any unmeasured confounder and its effect on the outcome occurs only through receipt of the exposure. These assumptions, while plausible in some contexts, cannot be verified from the data.
methodsFalsification tests can be applied to provide evidence for the key IV assumptions. A falsification test cannot prove the assumptions hold, but can provide decisive evidence when the assumption fails. We provide a general overview of falsification tests for IV designs. We highlight a falsification test that utilizes a subpopulation of the data where an overwhelming proportion of units are treated or untreated. If the IV assumptions hold, we should find the intention-to-treat effect is zero within these subpopulations.
resultsWe demonstrate the usage of falsification tests for IV designs using an IV known as tendency to operate from health services research. We show that the falsification test provides no evidence against the IV assumptions in this application.
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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.