Multibias Validation • multibias

library(multibias)

This example demonstrates how multibias is validated using simulation data. Here we are interested in quantifying the effect of exposure X on outcome Y. The causal system can be represented in the following directed acyclic graph (DAG):

The variables are defined:

X: true, unmeasured exposure
Y: outcome
C: measured confounder(s)
U: unmeasured confounder
X*: misclassified, measured exposure
S: study selection

The DAG indicates that there are three sources of bias: 1. There is uncontrolled confounding from (unobserved) variable U. 2. The true exposure, X, is unobserved, and the misclassified exposure X* is dependent on both the exposure and outcome. 3. Lastly, there is collider stratification at variable S since exposure and outcome both affect selection. The study naturally only assesses those who were selected into the study (i.e. those with S=1), which represents a fraction of all people in the source population from which we are trying to draw inference.

A simulated dataframe corresponding to this DAG, df_uc_emc_sel can be loaded from the multibias package.

head(df_uc_em_sel)
#>   Xstar Y C1 C2 C3
#> 1     1 0  0  0  1
#> 2     0 0  0  0  1
#> 3     0 0  0  0  0
#> 4     0 0  1  0  1
#> 5     1 0  0  0  1
#> 6     1 0  0  1  1

In this data, the true, unbiased exposure-outcome odds ratio (OR_YX) equals ~2. However, when we run a logistic regression of the outcome on the exposure and confounders, we do not observe an odds ratio of 2 due to the multiple bias sources.

biased_model <- glm(Y ~ Xstar + C1 + C2 + C3, ,
  family = binomial(link = "logit"),
  data = df_uc_em_sel
)
biased_or <- round(exp(coef(biased_model)[2]), 2)
print(paste0("Biased Odds Ratio: ", biased_or))
#> [1] "Biased Odds Ratio: 1.5"

The function multibias_adjust() can be used to “reconstruct” the unbiased data and return the exposure-outcome odds ratio that would be observed in the unbiased setting.

Models for the missing variables (U, X, S) are used to facilitate this data reconstruction. For the above DAG, the corresponding bias models are:

logit(P(U=1)) = α₀ + α₁X + α₂Y
logit(P(X=1)) = δ₀ + δ₁X* + δ₂Y + δ_2+jC_j
logit(P(S=1)) = β₀ + β₁X* + β₂Y + β_2+jC_j

where j indicates the number of measured confounders.

To perform the bias adjustment, it is necessary to obtain values of these bias parameters. Potential sources of these bias parameters may include validation data, estimates in the literature, and expert opinion. For purposes of demonstrating the methodology, we will obtain the exact values of these bias parameters. This is possible because we have access to the data of missing values that would otherwise be absent in real-world practice. This source data is available in multibias as df_uc_emc_sel_source.

u_model <- glm(U ~ X + Y,
  family = binomial(link = "logit"),
  data = df_uc_em_sel_source
)
x_model <- glm(X ~ Xstar + Y + C1 + C2 + C3,
  family = binomial(link = "logit"),
  data = df_uc_em_sel_source
)
s_model <- glm(S ~ Xstar + Y + C1 + C2 + C3,
  family = binomial(link = "logit"),
  data = df_uc_em_sel_source
)

In this example we’ll perform probabilistic bias analysis, representing each bias parameter as a draw from a Normal distribution. This allows us to account for our uncertainty in the “true” value of the bias parameters. We will leverage bootstrap sampling, which is built into multibias_adjust() to obtain a confidence interval that incorporates both uncertainty from random error and uncertainty from systematic error (our bias parameters). Each loop iteration will thus have slightly different values for the bias parameters.

We create our data_observed object, specifying the three biases of interest, and our bias_params object, bringing in the model results we determined above.

df_observed <- data_observed(
  data = df_uc_em_sel,
  bias = c("uc", "em", "sel"),
  exposure = "Xstar",
  outcome = "Y",
  confounders = c("C1", "C2", "C3")
)

bp <- bias_params(
  coef_list = list(
    u = c(
      rnorm(1, mean = u_model$coef[1], sd = summary(u_model)$coef[1, 2]),
      rnorm(1, mean = u_model$coef[2], sd = summary(u_model)$coef[2, 2]),
      rnorm(1, mean = u_model$coef[3], sd = summary(u_model)$coef[3, 2])
    ),
    x = c(
      rnorm(1, mean = x_model$coef[1], sd = summary(x_model)$coef[1, 2]),
      rnorm(1, mean = x_model$coef[2], sd = summary(x_model)$coef[2, 2]),
      rnorm(1, mean = x_model$coef[3], sd = summary(x_model)$coef[3, 2]),
      rnorm(1, mean = x_model$coef[4], sd = summary(x_model)$coef[4, 2]),
      rnorm(1, mean = x_model$coef[5], sd = summary(x_model)$coef[5, 2]),
      rnorm(1, mean = x_model$coef[6], sd = summary(x_model)$coef[6, 2])
    ),
    s = c(
      rnorm(1, mean = s_model$coef[1], sd = summary(s_model)$coef[1, 2]),
      rnorm(1, mean = s_model$coef[2], sd = summary(s_model)$coef[2, 2]),
      rnorm(1, mean = s_model$coef[3], sd = summary(s_model)$coef[3, 2]),
      rnorm(1, mean = s_model$coef[4], sd = summary(s_model)$coef[4, 2]),
      rnorm(1, mean = s_model$coef[5], sd = summary(s_model)$coef[5, 2]),
      rnorm(1, mean = s_model$coef[6], sd = summary(s_model)$coef[6, 2])
    )
  )
)

These are specified as inputs of multibias. In addition, we will indicate that we want results ran over 1,000 bootstrap samples.

multibias_adjust(
  data_observed = df_observed,
  bias_params = bp,
  bootstrap = TRUE,
  bootstrap_reps = 1000,
  level = 0.95
)
#> $estimate
#> [1] 2.053982
#> 
#> $ci
#> [1] 1.986672 2.120928

We arrive at an estimate of the effect of X on Y of approximately 2, correctly corresponding to the relationship that was built into the data. This showcases the power of multibias to allow researchers to arrive at valid causal effect estimates in the presence of missing or incomplete data.