View source: R/confounders.array.R
confounders.array  R Documentation 
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
)
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:

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. 
Schneeweiss, S., 2006. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Safety 15: 291303.
# Example from Schneeweiss, S. Sensitivity analysis and external adjustment for
# unmeasured confounders in epidemiologic database studies of therapeutics.
# Pharmacoepidemiol Drug Safety 2006; 15: 291303.
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.,
# MassoGonzalez, E., Bartels, D.B., Liu, J., and Schneeweiss, S. Claimsbased
# studies of oral glucoselowering 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))
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