multibias_adjust {multibias} | R Documentation |
Simultaneously adjust for multiple biases
Description
multibias_adjust
returns the exposure-outcome odds ratio and confidence
interval, adjusted for one or more biases.
Usage
multibias_adjust(
data_observed,
data_validation = NULL,
bias_params = NULL,
bootstrap = FALSE,
bootstrap_reps = 100,
level = 0.95
)
Arguments
data_observed |
Object of class |
data_validation |
Object of class |
bias_params |
Object of class 'bias_params' corresponding to the
bias parameters used to adjust for bias in the observed data. There must
be parameters corresponding to the bias or biases specified in
|
bootstrap |
Boolean for whether to perform bootstrapping to obtain the estimate and confidence interval. |
bootstrap_reps |
Integer number of bootstrap samples to run in bootstrapping. |
level |
Value from 0-1 representing the full range of the confidence interval. Default is 0.95. |
Details
Bias adjustment can be performed by inputting either a validation dataset or
the necessary bias parameters. Values for the bias parameters
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.
Value
A list including: the bias-adjusted effect estimate of the exposure on the outcome, the standard error, and the confidence interval as the vector: (lower bound, upper bound).
Examples
# Adjust for exposure misclassification -------------------------------------
df_observed <- data_observed(
data = df_em,
bias = "em",
exposure = "Xstar",
outcome = "Y",
confounders = "C1"
)
# Using validation data
df_validation <- data_validation(
data = df_em_source,
true_exposure = "X",
true_outcome = "Y",
confounders = "C1",
misclassified_exposure = "Xstar"
)
multibias_adjust(
data_observed = df_observed,
data_validation = df_validation
)
# Using bias_params
bp <- bias_params(coef_list = list(x = c(-2.10, 1.62, 0.63, 0.35)))
multibias_adjust(
data_observed = df_observed,
bias_params = bp
)
# Adjust for three biases ---------------------------------------------------
df_observed <- data_observed(
data = df_uc_om_sel,
bias = c("uc", "om", "sel"),
exposure = "X",
outcome = "Ystar",
confounders = c("C1", "C2", "C3")
)
# Using validation data
df_validation <- data_validation(
data = df_uc_om_sel_source,
true_exposure = "X",
true_outcome = "Y",
confounders = c("C1", "C2", "C3", "U"),
misclassified_outcome = "Ystar",
selection = "S"
)
multibias_adjust(
data_observed = df_observed,
data_validation = df_validation
)
# Using bias_params
bp1 <- bias_params(
coef_list = list(
u = c(-0.32, 0.59, 0.69),
y = c(-2.85, 0.71, 1.63, 0.40, -0.85, 0.22),
s = c(0.00, 0.74, 0.19, 0.02, -0.06, 0.02)
)
)
multibias_adjust(
data_observed = df_observed,
bias_params = bp1
)
bp2 <- bias_params(
coef_list = list(
u1y0 = c(-0.20, 0.62, 0.01, -0.08, 0.10, -0.15),
u0y1 = c(-3.28, 0.63, 1.65, 0.42, -0.85, 0.26),
u1y1 = c(-2.70, 1.22, 1.64, 0.32, -0.77, 0.09),
s = c(0.00, 0.74, 0.19, 0.02, -0.06, 0.02)
)
)
# with bootstrapping
## Not run:
multibias_adjust(
data_observed = df_observed,
bias_params = bp2,
bootstrap = TRUE,
bootstrap_reps = 1000
)
## End(Not run)