adjust_uc_em_sel {multibias} | R Documentation |
adjust_uc_em_sel
returns the exposure-outcome odds ratio and
confidence interval, adjusted for uncontrolled confounding, exposure
misclassificaiton, and selection bias. Two different options for the bias
parameters are availale here: 1) parameters from separate models
of U and X (u_model_coefs
and x_model_coefs
)
or 2) parameters from a joint model of U and X
(x1u0_model_coefs
, x0u1_model_coefs
, and
x1u1_model_coefs
). Both approaches require s_model_coefs
.
adjust_uc_em_sel(
data_observed,
u_model_coefs = NULL,
x_model_coefs = NULL,
x1u0_model_coefs = NULL,
x0u1_model_coefs = NULL,
x1u1_model_coefs = NULL,
s_model_coefs,
level = 0.95
)
data_observed |
Object of class |
u_model_coefs |
The regression coefficients corresponding to the model: logit(P(U=1)) = α0 + α1X + α2Y, where U is the binary unmeasured confounder, X is the binary true exposure, and Y is the outcome. The number of parameters therefore equals 3. |
x_model_coefs |
The regression coefficients corresponding to the model: logit(P(X=1)) = δ0 + δ1X* + δ2Y + δ2+jCj, where X represents binary true exposure, X* is the binary misclassified exposure, Y is the outcome, C represents the vector of measured confounders (if any), and j corresponds to the number of measured confounders. The number of parameters therefore equals 3 + j. |
x1u0_model_coefs |
The regression coefficients corresponding to the model: log(P(X=1,U=0)/P(X=0,U=0)) = γ1,0 + γ1,1X* + γ1,2Y + γ1,2+jCj, where X is the binary true exposure, U is the binary unmeasured confounder, X* is the binary misclassified exposure, Y is the outcome, C represents the vector of measured confounders (if any), and j corresponds to the number of measured confounders. |
x0u1_model_coefs |
The regression coefficients corresponding to the model: log(P(X=0,U=1)/P(X=0,U=0)) = γ2,0 + γ2,1X* + γ2,2Y + γ2,2+jCj, where X is the binary true exposure, U is the binary unmeasured confounder, X* is the binary misclassified exposure, Y is the outcome, C represents the vector of measured confounders (if any), and j corresponds to the number of measured confounders. |
x1u1_model_coefs |
The regression coefficients corresponding to the model: log(P(X=1,U=1)/P(X=0,U=0)) = γ3,0 + γ3,1X* + γ3,2Y + γ3,2+jCj, where X is the binary true exposure, U is the binary unmeasured confounder, X* is the binary misclassified exposure, Y is the outcome, C represents the vector of measured confounders (if any), and j corresponds to the number of measured confounders. |
s_model_coefs |
The regression coefficients corresponding to the model: logit(P(S=1)) = β0 + β1X* + β2Y + β2+jC2+j, where S represents binary selection, X* is the binary misclassified exposure, Y is the outcome, C represents the vector of measured confounders (if any), and j corresponds to the number of measured confounders. The number of parameters therefore equals 3 + j. |
level |
Value from 0-1 representing the full range of the confidence interval. Default is 0.95. |
Values for the regression coefficients can be applied as
fixed values or as single draws from a probability
distribution (ex: rnorm(1, mean = 2, sd = 1)
). The latter has
the advantage of allowing the researcher to capture the uncertainty
in the bias parameter estimates. To incorporate this uncertainty in the
estimate and confidence interval, this function should be run in loop across
bootstrap samples of the dataframe for analysis. The estimate and
confidence interval would then be obtained from the median and quantiles
of the distribution of odds ratio estimates.
A list where the first item is the odds ratio estimate of the effect of the exposure on the outcome and the second item is the confidence interval as the vector: (lower bound, upper bound).
df <- data_observed(
data = df_uc_em_sel,
exposure = "Xstar",
outcome = "Y",
confounders = c("C1", "C2", "C3")
)
# Using u_model_coefs, x_model_coefs, s_model_coefs -------------------------
adjust_uc_em_sel(
df,
u_model_coefs = c(-0.32, 0.59, 0.69),
x_model_coefs = c(-2.44, 1.62, 0.72, 0.32, -0.15, 0.85),
s_model_coefs = c(0.00, 0.26, 0.78, 0.03, -0.02, 0.10)
)
# Using x1u0_model_coefs, x0u1_model_coefs, x1u1_model_coefs, s_model_coefs
adjust_uc_em_sel(
df,
x1u0_model_coefs = c(-2.78, 1.62, 0.61, 0.36, -0.27, 0.88),
x0u1_model_coefs = c(-0.17, -0.01, 0.71, -0.08, 0.07, -0.15),
x1u1_model_coefs = c(-2.36, 1.62, 1.29, 0.25, -0.06, 0.74),
s_model_coefs = c(0.00, 0.26, 0.78, 0.03, -0.02, 0.10)
)