Causal inference methods for small non-randomized studies: Methods and an application in COVID-19.
Contemp Clin Trials
; 99: 106213, 2020 12.
Article
in English
| MEDLINE | ID: covidwho-919724
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antiviral Agents
/
Clinical Trials as Topic
/
COVID-19
/
COVID-19 Drug Treatment
/
Hydroxychloroquine
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
Contemp Clin Trials
Journal subject:
Medicine
/
Therapeutics
Year:
2020
Document Type:
Article
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