Fit model using uniform imputation

Usage

fit_uniform_model(
  participant_level_data,
  obs_level_data,
  maxit = 1000,
  tolerance = 1e-08,
  n_imputations = 10
)

Arguments

participant_level_data

a data.frame or tibble with the following variables:

ID: participant ID
E: study enrollment date
L: date of last negative test for seroconversion
R: date of first positive test for seroconversion
Cohort` (optional): this variable can be used to stratify the modeling of the seroconversion distribution.

obs_level_data

a data.frame or tibble with the following variables:

ID: participant ID
O: biomarker sample collection dates
Y: MAA classifications (binary outcomes)

maxit

maximum iterations, passed to bigglm

tolerance

convergence criterion, passed to bigglm

n_imputations

number of imputed data sets to create

Value

a vector of logistic regression coefficient estimates

Examples

sim_data = simulate_interval_censoring(
  "theta" = c(0.986, -3.88),
  "study_cohort_size" = 4500,
  "preconversion_interval_length" = 365,
  "hazard_alpha" = 1,
  "hazard_beta" = 0.5)

theta_est_midpoint = fit_uniform_model(
  obs_level_data = sim_data$obs_data,
  participant_level_data = sim_data$pt_data
)