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

07/31/2020
by   Sarah Friedrich, et al.
0

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 and apply them to the example study exploring the question whether different methods might have led to different conclusions. 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 doubly robust g-computation, which is less frequently applied, in particular in clinical studies. Here, we investigate the properties of propensity score based methods including g-computation in small sample settings, typical for early trials, in a simulation study. We conclude that the doubly robust g-computation has some desirable properties and should be more frequently applied in clinical research. In the hydroxychloroquine study, g-computation resulted in a very wide confidence interval indicating much uncertainty. We speculate that application of the method might have prevented some of the hype surrounding hydroxychloroquine in the early stages of the SARS-CoV-2 pandemic. R code for the g-computation is provided.

READ FULL TEXT
research
05/28/2020

Clinical trials impacted by the COVID-19 pandemic: Adaptive designs to the rescue?

Very recently the new pathogen severe acute respiratory syndrome coronav...
research
08/30/2023

Covariate adjustment and estimation of difference in proportions in randomized clinical trials

Difference in proportions is frequently used to measure treatment effect...
research
11/27/2020

Design aspects of COVID-19 treatment trials: Improving probability and time of favourable events

As a reaction to the pandemic of the severe acute respiratory syndrome c...
research
07/12/2023

Doubly robust machine learning for an instrumental variable study of surgical care for cholecystitis

Comparative effectiveness research frequently employs the instrumental v...
research
04/29/2022

Redefining Populations of Inference for Generalizations from Small Studies

With the growth in experimental studies in education, policymakers and p...

Please sign up or login with your details

Forgot password? Click here to reset