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Causal inference methods for small non-randomized studies: Methods and an application in COVID-19.

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.

journal_name

Contemp Clin Trials

journal_title

Contemporary clinical trials

authors

Friedrich S,Friede T

doi

10.1016/j.cct.2020.106213

subject

Has Abstract

pub_date

2020-12-01 00:00:00

pages

106213

eissn

1551-7144

issn

1559-2030

pii

S1551-7144(20)30291-3

journal_volume

99

pub_type

杂志文章

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