Sensitivity analysis to correct for unknown or unmeasured confounding in the presence of effect modification
Source:R/confounders.emm.R
confounders.emm.RdSimple sensitivity analysis to correct for unknown or unmeasured confounding in the presence of effect modification. Implementation for ratio measures (relative risk -- RR, or odds ratio -- OR) and difference measures (risk difference -- RD).
Usage
confounders.emm(
case,
exposed,
type = c("RR", "OR", "RD"),
bias_parms = NULL,
alpha = 0.05
)Arguments
- case
Outcome variable. If a variable, this variable is tabulated against.
- exposed
Exposure variable.
- type
Choice of implementation, with no effect measure modification for ratio measures (relative risk -- RR; odds ratio -- OR) or difference measures (risk difference -- RD).
- bias_parms
Numeric vector defining the 4 necessary bias parameters. This vector has 4 elements, in the following order:
the association between the confounder and the outcome among those who were exposed,
the association between the confounder and the outcome among those who were not exposed,
the prevalence of the confounder among the exposed (between 0 and 1), and
the prevalence of the confounder among the unexposed (between 0 and 1).
- alpha
Significance level.
Value
A list with elements:
- obs.data
The analyzed 2 x 2 table from the observed data.
- cfder.data
The same table for Confounder +.
- nocfder.data
The same table for Confounder -.
- obs.measures
A table of relative risk with confidence intervals; Total, for Confounder +, and for Confounder -.
- adj.measures
A table of Standardized Morbidity Ratio and Mantel-Haenszel estimates.
- bias.parms
Input bias parameters.
References
Lash, T.L., Fox, M.P, Fink, A.K., 2009 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.59--78, Springer.
Examples
# The data for this example come from:
# Tyndall M.W., Ronald A.R., Agoki E., Malisa W., Bwayo J.J., Ndinya-Achola J.O.
# et al.
# Increased risk of infection with human immunodeficiency virus type 1 among
# uncircumcised men presenting with genital ulcer disease in Kenya.
# Clin Infect Dis 1996;23:449-53.
confounders.emm(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RR",
bias_parms = c(.4, .7, .8, .05))
#> --Observed data--
#> Outcome: HIV+
#> Comparing: Circ+ vs. Circ-
#>
#> Circ+ Circ-
#> HIV+ 105 85
#> HIV- 527 93
#>
#> 2.5% 97.5%
#> Crude Relative Risk: 0.3479151 0.2757026 0.4390417
#> Relative Risk, Confounder +: 0.3765894
#> Relative Risk, Confounder -: 0.6590315
#> ---
#> Adjusted RR
#> Standardized Morbidity Ratio: 0.4509163 0.7715736
#> Mantel-Haenszel: 0.6369911 0.5461852
confounders.emm(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "OR",
bias_parms = c(.4, .7, .8, .05))
#> --Observed data--
#> Outcome: HIV+
#> Comparing: Circ+ vs. Circ-
#>
#> Circ+ Circ-
#> HIV+ 105 85
#> HIV- 527 93
#>
#> 2.5% 97.5%
#> Crude Odds Ratio: 0.2179931 0.1519254 0.3127916
#> Odds Ratio, Confounder +: 0.2359595
#> Odds Ratio, Confounder -: 0.4129292
#> ---
#> Adjusted OR
#> Standardized Morbidity Ratio: 0.2825305 0.7715736
#> Mantel-Haenszel: 0.3969272 0.5492017
confounders.emm(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RD",
bias_parms = c(-.6, -.3, .8, .05))
#> --Observed data--
#> Outcome: HIV+
#> Comparing: Circ+ vs. Circ-
#>
#> Circ+ Circ-
#> HIV+ 105 85
#> HIV- 527 93
#>
#> 2.5% 97.5%
#> Crude Risk Difference: -0.3113888 -0.3902969 -0.2324808
#> Risk Difference, Confounder +: -0.1463888
#> Risk Difference, Confounder -: 0.1536112
#> ---
#> Adjusted RD
#> Mantel-Haenszel: 0.1212498 -0.4326386