confounders.array: Sensitivity analysis for unmeasured confounders based on...
In episensr: Basic Sensitivity Analysis of Epidemiological Results

Description Usage Arguments Value References Examples

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.

confounders.array(
  crude.risk,
  type = c("binary", "continuous", "RD"),
  bias_parms = NULL,
  dec = 2,
  print = TRUE
)

`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').
`dec`	Number of decimals in the printout.
`print`	A logical scalar. Should the results be printed?

A vector with elements:

`crude.risk`	The crude relative risk or risk difference.
`RR_CD`	The association between the confounder and the outcome.
`P_C1`	The prevalence of the confounder among the exposed, or mean value of the confounder among the exposed.
`P_C0`	The prevalence of the confounder among the unexposed, or mean value of the confounder among the unexposed.
`risk_adj`	The adjusted exposure relative risk or risk difference.
`bias_perc`	The bias as a percentage: (crude.RR - risk_adj)/risk_adj * 100.

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

# 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),
dec = 4)