Simultaneously adjust for multiple biases

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,
  level = 0.95
)

Arguments

data_observed

Object of class data_observed corresponding to the data to perform bias analysis on.

data_validation

Object of class data_validation corresponding to the validation data used to adjust for bias in the observed data. The validation data should have data for the same variables as in data_observed, plus data for the missing variables leading to bias.

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 data_observed.

level

Value from 0-1 representing the full range of the confidence interval. Default is 0.95.

Value

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).

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.

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
)
#> $estimate
#> [1] 2.123305
#> 
#> $ci
#> [1] 2.041413 2.208482
#> 

# 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
)
#> $estimate
#> [1] 2.050886
#> 
#> $ci
#> [1] 1.971682 2.133272
#> 

# 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
)
#> $estimate
#> [1] 2.051169
#> 
#> $ci
#> [1] 1.998335 2.105399
#> 

# 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
)
#> $estimate
#> [1] 1.992133
#> 
#> $ci
#> [1] 1.940813 2.044810
#> 

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)
  )
)

multibias_adjust(
  data_observed = df_observed,
  bias_params = bp2
)
#> $estimate
#> [1] 1.994691
#> 
#> $ci
#> [1] 1.943281 2.047462
#>