logistic2ph {sleev}

Sieve maximum likelihood estimator (SMLE) for two-phase logistic regression problems

Description

This function returns the sieve maximum likelihood estimators (SMLE) for the logistic regression model from Lotspeich et al. (2021).

Usage

logistic2ph(
  Y_unval = NULL,
  Y = NULL,
  X_unval = NULL,
  X = NULL,
  Z = NULL,
  Bspline = NULL,
  data = NULL,
  hn_scale = 1,
  noSE = FALSE,
  TOL = 1e-04,
  MAX_ITER = 1000,
  verbose = FALSE
)

Arguments

Y_unval

Column name of the error-prone or unvalidated binary outcome. This argument is required.

Y

Column name that stores the validated value of Y_unval in the second phase. Subjects with missing values of Y are considered as those not selected in the second phase. This argument is required.

X_unval

Specifies the columns of the error-prone covariates. This argument is required.

X

Specifies the columns that store the validated values of X_unval in the second phase. Subjects with missing values of X are considered as those not selected in the second phase. This argument is required.

Z

Specifies the columns of the accurately measured covariates. This argument is optional.

Bspline

Specifies the columns of the B-spline basis. This argument is required.

data

Specifies the name of the dataset. This argument is required.

hn_scale

Specifies the scale of the perturbation constant in the variance estimation. For example, if hn_scale = 0.5, then the perturbation constant is 0.5n^{-1/2}, where n is the first-phase sample size. The default value is 1. This argument is optional.

noSE

If TRUE, then the variances of the parameter estimators will not be estimated. The default value is FALSE. This argument is optional.

TOL

Specifies the convergence criterion in the EM algorithm. The default value is 1E-4. This argument is optional.

MAX_ITER

Maximum number of iterations in the EM algorithm. The default number is 1000. This argument is optional.

verbose

If TRUE, then show details of the analysis. The default value is FALSE.

Value

coefficients

Stores the analysis results.

outcome_err_coefficients

Stores the outcome error model results.

Bspline_coefficients

Stores the final B-spline coefficient estimates.

covariance

Stores the covariance matrix of the regression coefficient estimates.

converge

In parameter estimation, if the EM algorithm converges, then converge = TRUE. Otherwise, converge = FALSE.

converge_cov

In variance estimation, if the EM algorithm converges, then converge_cov = TRUE. Otherwise, converge_cov = FALSE.

converge_msg

In parameter estimation, if the EM algorithm does not converge, then converged_msg is a string description.

References

Lotspeich, S. C., Shepherd, B. E., Amorim, G. G. C., Shaw, P. A., & Tao, R. (2021). Efficient odds ratio estimation under two-phase sampling using error-prone data from a multi-national HIV research cohort. Biometrics, biom.13512. https://doi.org/10.1111/biom.13512