confounders.array: Sensitivity analysis for unmeasured confounders based on...

In episensr: Basic Sensitivity Analysis of Epidemiological Results

Sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposed

Description

Sensitivity analysis to explore effect of residual confounding using simple algebraic transformation (array approach). It indicates the strength of an unmeasured confounder and the necessary imbalance among exposure categories to affect the observed (crude) relative risk.

Usage

confounders.array(
  crude_risk,
  type = c("binary", "continuous", "RD"),
  bias_parms = NULL
)

Arguments

crude_risk	Crude (apparent or observed) relative risk between the exposure and the outcome. If type RD, this is the crude (observed) risk difference.
type	Choice of implementation, for binary covariates, continuous covariates, or on risk difference scale.
bias_parms	Numeric vector defining the necessary bias parameters. This vector has 3 elements, in the following order: the association between the confounder and the outcome (RR, relative risk), the prevalence of the confounder among the exposed (between 0 and 1, if type binary), or mean value of the confounder among the exposed (if type continuous or RD), and the prevalence of the confounder among the unexposed (between 0 and 1, if type binary), or mean value of the confounder among the unexposed (if type continuous or RD).

Value

A list with elements:

model	Bias analysis performed.
bias_parms	Input bias parameters.
adj_measures	Output results, with bias as a percentage: (crude_RR - risk_adj)/risk_adj * 100.

References

Schneeweiss, S., 2006. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Safety 15: 291-303.

See Also

Other confounding: confounders(), confounders.evalue(), confounders.ext(), confounders.limit(), probsens.irr.conf()

Examples

# Example from Schneeweiss, S. Sensitivity analysis and external adjustment for
# unmeasured confounders in epidemiologic database studies of therapeutics.
# Pharmacoepidemiol Drug Safety 2006; 15: 291-303.
confounders.array(crude_risk = 1.5, type = "binary",
bias_parms = c(5.5, 0.5, 0.1))

# Examples from Patorno E., Gopalakrishnan, C., Franklin, J.M., Brodovicz, K.G.,
# Masso-Gonzalez, E., Bartels, D.B., Liu, J., and Schneeweiss, S. Claims-based
# studies of oral glucose-lowering medications can achieve balance in critical
# clinical variables only observed in electronic health records 2017; 20(4): 974-
# 984.
confounders.array(crude_risk = 1.5, type = "binary",
bias_parms = c(3.25, 0.333, 0.384))

confounders.array(crude_risk = 1.5, type = "continuous",
bias_parms = c(1.009, 7.8, 7.9))

confounders.array(crude_risk = 0.05, type = "RD", bias_parms = c(0.009, 8.5, 8))

episensr documentation built on June 8, 2025, 10:34 a.m.