EM Algorithm for Finite Mixture Models with Survival Endpoints

Usage

fmm_em_algorithm(
  input_df,
  weights_input,
  starting_values_input_df,
  k,
  outc_model_formula,
  outc_model_time,
  outc_model_status,
  outc_model_covars,
  outc_distribution,
  covariates_subgroup_model,
  n_inits = 5,
  tolerance = 0.001,
  conv_pct_criteria = -1,
  max_iter = 200
)

Arguments

input_df: Input data frame containing 1 row/observation, along with each observation's event status (0=censored, 1=event) and event time variables
weights_input: Variable for inverse probability of treatment weights, if applicable. For IPCW-FMM, supply the IPCW. For IPCW-FMM that also uses IPTW, the product of the ITPW and IPCW may be supplied.
starting_values_input_df: Input dataset with starting values for algorithm
k: Number of subgroups
outc_model_formula: Formula object for subgroup-specific outcome models
outc_model_covars: Names of covariates to include in the outcome models for each subgroup
outc_distribution: Outcome distribution for subgroup-specific outcome models. Currently allowed values are "Weibull" and "Log-Normal" (not case-sensitive)
covariates_subgroup_model: Vector of covariates to include in subgroup membership model
n_inits: Number of initial partitions for the EM algorithm. Default is 5. A higher number of initial partitions may result in greater stability of estimates.
tolerance: Convergence criteria for the change in the log-likelihood for the EM algorithm.
conv_pct_criteria: Convergence criteria for the percentage of observations changing subgroup. Specify -1 to only use the log-likelihood as the convergence criteria.
max_iter: Maximum number of iterations. Default is 200.

Value

List