Assess the Application of the E-Value in the Unmeasured Confounder Evaluation of Observational Pharmaceutical Studies

Lihong Huang,Jianbing Ma,Tao Suo,Xiaochun Qiu,Zhu Xiao

doi:10.1155/2021/9618161

Abstract

Public health is very important in big cities, and data analysis on public health studies is always a demanding issue that determines the study effectiveness. E-value was proposed as a standard sensitivity analysis tool to assess unmeasured confounders in observational studies, but its value is doubted. To evaluate the usefulness of E-value, in this paper, we collected 368 observational studies on drug effectiveness evaluation published from 1998 to September 2019 (out of 3426 searched studies) and evaluated the features of E-value. We selected the effects of primary outcomes or the largest effects in terms of hazard ratio, risk ratio, or odds ratio. Effects were transformed into estimated effect sizes following a standard E-value computation. In all 368 studies, the disease with the highest percentage was infections and infestations, at 21.7% (80/368). Our results showed that the median relative effect size was 1.89 (Q1-Q3: 1.41–2.95), and the corresponding median E-value was 3.19 with 95% confidence interval lower bound 1.77. Smaller studies yielded larger E-values for the effect size estimate and the relationship was considerably attenuated when considering the E-value for the lower bound of 95% confidence interval on the effect size. Notably, E-values have a monotonic, almost linear relationship with effect estimates. We found that E-value may cause misimpressions on the unmeasured confounder, and the same E-value does not reflect the varying nature of the unmeasured confounders in different studies, and there lacks a guidance on how E-value can be deemed as small or large, all of which limits the capability of E-value as a standard sensitivity analysis tool in real applications.

Highlights

Public health issues are drawing more and more attentions since they may cause harm to a large proportion of the population, especially in big cities where the density of population is high
When a randomized clinical trial (RCT) is not available, or the analysis is required to be based on real world data, an Scientific Programming increased attention has to be paid for the application of observational studies [2,3,4]
Two reviewers screened all titles/abstracts to apply the inclusion/ exclusion criteria independently and in duplicate. e two reviewers resolved any discrepancy at each stage through a consensus process, and all studies were screened by two reviewers in parallel; the resulting list of the included articles/trials was discussed by all researchers to ensure the accuracy of the final decision

Summary

Introduction

Public health issues are drawing more and more attentions since they may cause harm to a large proportion of the population, especially in big cities where the density of population is high. One main problem of dealing with public health events is that these events often involve large amount of data and complex factors that may affect the results. It is quite important, and in the meantime, very difficult, to assess if the results are reliable given that some of the factors are inevitably not addressed, due to ignorance or missing, called “confounding analysis.”. When a randomized clinical trial (RCT) is not available, or the analysis is required to be based on real world data, an Scientific Programming increased attention has to be paid for the application of observational studies [2,3,4]

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