ArticleContemporary clinical trials2020
Causal inference methods for small non-randomized studies: Methods and an application in COVID-19.
Article in Contemporary clinical trials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 1 citations in OpenAlex.
- Sex-related variations in platelet reactivity in presence or absence of antiplatelet therapy.European heart journal. Cardiovascular pharmacotherapy · 2025Article
- Participation in community gathering place and social capital: A longitudinal study of older adults.SSM - population health · 2025Article
- Impact of MEK Inhibition on Childhood RASopathy-Associated Hypertrophic Cardiomyopathy.JACC. Basic to translational science · 2025Article
- Phase I study of a novel glioblastoma radiation therapy schedule exploiting cell-state plasticity.Neuro-oncology · 2023Article
- On the role of data, statistics and decisions in a pandemic.Advances in statistical analysis : AStA : a journal of the German Statistical Society · 2022Article
- Variation in Use of Repurposed Medications Among Patients With Coronavirus Disease 2019. From The Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study: Coronavirus Disease 2019 Registry Investigator Group.Critical care explorations · 2021Article
- Guest editors-in-chief note to COVID-19 special issue.Contemporary clinical trials · 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.
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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.