EValue (version 4.1.3)

svalues.RR: Compute selection bias E-value for a risk ratio or rate ratio and its confidence interval limits

Description

Returns a data frame containing point estimates, the lower confidence limit, and the upper confidence limit for the risk ratio (as provided by the user) as well as selection bias E-values for the point estimate and the confidence interval limit closer to the null.

Usage

svalues.RR(
  est,
  lo = NA,
  hi = NA,
  true = 1,
  sel_pop = FALSE,
  S_eq_U = FALSE,
  risk_inc = FALSE,
  risk_dec = FALSE,
  ...
)

Arguments

est

The point estimate

lo

The lower limit of the confidence interval

hi

The upper limit of the confidence interval

true

The true RR to which to shift the observed point estimate. Typically set to 1 to consider a null true effect.

sel_pop

Whether inference is specific to selected population (TRUE) or entire population (FALSE). Defaults to FALSE.

S_eq_U

Whether the unmeasured factor is assumed to be a defining characteristic of the selected population. Defaults to FALSE.

risk_inc

Whether selection is assumed to be associated with increased risk of the outcome in both exposure groups. Defaults to FALSE.

risk_dec

Whether selection is assumed to be associated with decreased risk of the outcome in both exposure groups. Defaults to FALSE.

...

Arguments passed to other methods.

Details

A selection bias E-value is a summary measure that helps assess susceptibility of a result to selection bias. Each of one or more parameters characterizing the extent of the bias must be greater than or equal to this value to be sufficient to shift an estimate (est) to the null or other true value (true). The parameters, as defined in Smith and VanderWeele 2019, depend on assumptions an investigator is willing to make (see arguments sel_pop, S_eq_U, risk_inc, risk_dec). The svalues.XX functions print a message about which parameters the selection bias E-value refers to given the assumptions made. See the cited article for details.

Examples

# NOT RUN {
# Examples from Smith and VanderWeele 2019

# Zika virus example
svalues.RR(est = 73.1, lo = 13.0)

# Endometrial cancer example
svalues.RR(est = 2.30, true = 11.98, S_eq_U = TRUE, risk_inc = TRUE)

# Obesity paradox example
svalues.RR(est = 1.50, lo = 1.22, sel_pop = TRUE)
# }