help evalue (E-value in Epidemiology)

Title

evalue -- calculate E-values

Syntax

evalue
theObservedEffect
[if] [in]
[, options
]

Options
measurement The effect measurement (risk ratio, odds ratio, and hazard ratio); Default is risk ratio.

lowlimit The low limite of 95%CI

uplimit The up limite of 95%CI

rareoutcome Rare outcome assumption, for example the prevalence <= 0.15

Description

Command evalue calculate E-value for an observed risk ratio, odds ratio, and hazard ratio.

Options

measurement specifies the effect measurement, including 1 (risk ratio), 2 (odds ratio), and 3 (hazard ratio). Default is 1 (risk ratio).

lowlimit specifies the value for the low limit of 95%CI

uplimit specifies the value for the low limit of 95%CI

rareoutcome the rare outcome assmption; Default value=1 assumes the outcome is relatively rare (the prevalence<=0.15), otherwise, input 0 if the the outcome is relatively common.

Examples: click on the text in blue

1. E-value for an observed risk ratio

.evalue 2.95, m(1)

1. E-value for an observed risk ratio and the low limit of 95%CI

.evalue 1.61, m(1) l(1.06)

1. E-value for an observed odds ratio if the outcome is relatively rare (the prevalence <=0.15)

.evalue 2.02, m(2)

1. E-value for an observed odds ratio and the low limit of 95%CI if the outcome is relatively rare (the prevalence <=0.15)

.evalue 2.02, m(2) l(1.08)

1. E-value for an observed odds ratio if the outcome is common (the prevalence >0.15)

.evalue 2.02, m(2) r(0)

1. E-value for an observed odds ratio andthe low limit of 95%CI if the outcome is common (the prevalence >0.15)

.evalue 2.02, m(2) l(1.08) r(0)

1. E-value for an observed hazard ratio if the outcome is relatively rare (the prevalence <=0.15)

.evalue 0.10, m(3)

1. E-value for an observed hazard ratio and the uplimit of 95%CI if the outcome is relatively rare (the prevalence <=0.15)

.evalue 0.10, m(3) u(0.26)

1. E-value for an observed odds ratio if the outcome is common (the prevalence >0.15)

.evalue 0.10, m(3) r(0)

1. E-value for an observed odds ratio and the up limit of 95%CI if the outcome is common (the prevalence >0.15)

.evalue 0.10, m(3) u(0.26) r(0)

More examples

click on her

References

VanderWeele TJ, Ding P.

Sensitivity Analysis in Observational Research: Introducing the E-Value.

Author

Chunsen Wu, the University of Southern Denmark; Odense University Hospital, Denmark

cwu@health.sdu.dk

chunsen.wu@rsyd.dk

Also see

Help: dag, biasepi