Type: Package
Title: Semiparametric Likelihood Estimation with Errors in Variables
Version: 1.1.4
Description: Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: Rcpp (≥ 1.0.7), R (≥ 3.5.0)
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Suggests: lme4, MASS, rmarkdown, splines, testthat (≥ 3.0.0), R.rsp, tibble, knitr, quarto
LazyData: true
Config/testthat/edition: 3
URL: https://github.com/dragontaoran/sleev
BugReports: https://github.com/dragontaoran/sleev/issues
NeedsCompilation: yes
Packaged: 2025-06-27 15:57:15 UTC; taor2
Author: Sarah Lotspeich [aut], Ran Tao [aut, cre], Joey Sherrill [prg], Jiangmei Xiong [ctb]
Maintainer: Ran Tao <r.tao@vanderbilt.edu>
Repository: CRAN
Date/Publication: 2025-06-27 16:20:02 UTC

Extract Coefficients from linear2ph Model

Description

Extracts estimated coefficients from a two-phase linear regression model of class linear2ph.

Usage

## S3 method for class 'linear2ph'
coefficients(object, ...)

Arguments

object

An object of class linear2ph.

...

Additional arguments passed to other methods.

Value

A numeric vector of coefficients if the model converged, otherwise NULL with a warning.

See Also

coefficients

Extract Coefficients from logistic2ph Model

Description

Extracts estimated coefficients from a two-phase logistic regression model of class logistic2ph.

Usage

## S3 method for class 'logistic2ph'
coefficients(object, ...)

Arguments

object

An object of class logistic2ph.

...

Additional arguments passed to other methods.

Value

A numeric vector of coefficients if the model converged, otherwise NULL with a warning.

See Also

coefficients

Cross-validation log-likelihood prediction for linear2ph

Description

Performs cross-validation to calculate the average predicted log likelihood for the linear2ph function. This function can be used to select the B-spline basis that yields the largest average predicted log likelihood. See pacakge vigenette for code examples.

Usage

cv_linear2ph(
  y_unval = NULL,
  y = NULL,
  x_unval = NULL,
  x = NULL,
  z = NULL,
  data = NULL,
  nfolds = 5,
  max_iter = 2000,
  tol = 1e-04,
  verbose = FALSE
)

Arguments

y_unval

Specifies the column of the error-prone outcome that is continuous. Subjects with missing values of y_unval are omitted from the analysis. This argument is required.

y

Specifies the column 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. Subjects with missing values of x_unval are omitted from the analysis. 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. Subjects with missing values of z are omitted from the analysis. This argument is optional.

data

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

nfolds

Specifies the number of cross-validation folds. The default value is 5. Although nfolds can be as large as the sample size (leave-one-out cross-validation), it is not recommended for large datasets. The smallest value allowable is 3.

max_iter

Specifies the maximum number of iterations in the EM algorithm. The default number is 2000. This argument is optional.

tol

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

verbose

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

Details

cv_linear2ph gives log-likelihood prediction for models and data like those in linear2ph. Therefore, the arguments of cv_linear2ph is analogous to that of linear2ph.

Value

cv_linear2ph() returns a list that includes the following components:

avg_pred_loglike

The average predicted log likelihood across each fold.

pred_loglike

The predicted log likelihood in each fold.

converge

The convergence status of the EM algorithm in each run.

Examples

## Not run: 
  data("mock.vccc")
  # different B-spline sizes
  sns <- c(15, 20, 25, 30, 35, 40)
  # vector to hold mean log-likelihood
  pred_loglike.1 <- rep(NA, length(sns))
  # specify number of folds in the cross validation
  k <- 5
  for (i in 1:length(sns)) {
    # constructing B-spline basis using the same process as in Section 4.3.1
    sn <- sns[i]
    data.sieve <- spline2ph(x = "VL_unval", data = mock.vccc, size = sn,
                            degree = 3, group = "Sex")

    # cross validation, produce mean log-likelihood
    start.time <- Sys.time()
    res.1 <- cv_linear2ph(y = "CD4_val", y_unval = "CD4_unval",
                          x ="VL_val", x_unval = "VL_unval", z = "Sex",
                          data = data.sieve, nfolds = k, max_iter = 2000,
                          tol = 1e-04, verbose = FALSE)
    # save mean log-likelihood result
    pred_loglike.1[i] <- res.1$avg_pred_loglik
  }
  # Print predicted log-likelihood for different B-spline sizes
  print(pred_loglike.1)

## End(Not run)

Cross-validation log-likelihood prediction for logistic2ph

Description

Performs cross-validation to calculate the average predicted log likelihood for the logistic2ph function. This function can be used to select the B-spline basis that yields the largest average predicted log likelihood. See pacakge vigenette for code examples.

Usage

cv_logistic2ph(
  y_unval = NULL,
  y = NULL,
  x_unval = NULL,
  x = NULL,
  z = NULL,
  data,
  nfolds = 5,
  tol = 1e-04,
  max_iter = 1000,
  verbose = FALSE
)

Arguments

y_unval

Column name of the error-prone or unvalidated binary outcome. This argument is optional. If y_unval = NULL (the default), y is treated as error-free.

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. Subjects with missing values of z are omitted from the analysis. This argument is optional.

data

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

nfolds

Specifies the number of cross-validation folds. The default value is 5. Although nfolds can be as large as the sample size (leave-one-out cross-validation), it is not recommended for large datasets. The smallest value allowable is 3.

tol

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

max_iter

Specifies the maximum number of iterations in the EM algorithm. The default number is 2000. This argument is optional.

verbose

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

Details

cv_logistic2ph gives log-likelihood prediction for models and data like those in logistic2ph. Therefore, the arguments of cv_logistic2ph is analogous to that of logistic2ph.

Value

cv_logistic2ph() returns a list that includes the following components:

avg_pred_loglike

Stores the average predicted log likelihood.

pred_loglike

Stores the predicted log likelihood in each fold.

converge

Stores the convergence status of the EM algorithm in each run.

Examples

## Not run: 
data("mock.vccc")
# different B-spline sizes
sns <- c(15, 20, 25, 30, 35, 40)
# vector to hold mean log-likelihood
pred_loglike.1 <- rep(NA, length(sns))
# specify number of folds in the cross validation
k <- 5
for (i in 1:length(sns)) {
  # constructing B-spline basis using the same process as in Section 4.3.1
  sn <- sns[i]
  data.sieve <- spline2ph(x = "CD4_unval", size = 20, degree = 3,
                          data = mock.vccc, group = "Prior_ART",
                          split_group = TRUE)
  # cross validation, produce mean log-likelihood
  start.time <- Sys.time()
  res.1 <- cv_logistic2ph(y = "ADE_val", y_unval = "ADE_unval",
                          x = "CD4_val", x_unval = "CD4_unval",
                          z = "Prior_ART", data = data.sieve,
                          tol = 1e-04, max_iter = 1000, verbose = FALSE)

  # save mean log-likelihood result
  pred_loglike.1[i] <- res.1$avg_pred_loglik
}
# Print predicted log-likelihood for different B-spline sizes
print(pred_loglike.1)


## End(Not run)

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

Description

Performs efficient semiparametric estimation for general two-phase measurement error models when there are errors in both the outcome and covariates. See pacakge vigenette for code examples.

Usage

linear2ph(
  y_unval = NULL,
  y = NULL,
  x_unval = NULL,
  x = NULL,
  z = NULL,
  data = NULL,
  hn_scale = 1,
  se = TRUE,
  tol = 1e-04,
  max_iter = 1000,
  verbose = FALSE
)

Arguments

y_unval

Column name of the error-prone or unvalidated continuous outcome. Subjects with missing values of y_unval are omitted from the analysis. 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. Subjects with missing values of x_unval are omitted from the analysis. 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. Subjects with missing values of z are omitted from the analysis. This argument is optional.

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.

se

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

Details

Models for linear2ph() are specified through the arguments. The dataset input should at least contain columns for unvalidated error-prone outcome, validated error-prone outcome, unvalidated error-prone covariate(s), validated error-prone covariate(s), and B-spline basis. B-spline basis can be generated from splines::bs() function, with argument x being the unvalidated error-prone covariate(s). See vignette for options in tuning the B-spline basis.

Value

linear2ph() returns an object of class "linear2ph". The function coef() is used to obtain the coefficients of the fitted model. The function summary() is used to obtain and print a summary of results.

An object of class "linear2ph" is a list containing at least the following components:

call

the matched call.

coefficients

A named vector of the linear regression coefficient estimates.

sigma

The residual standard error.

covariance

The covariance matrix of the linear 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.

References

Tao, R., Mercaldo, N. D., Haneuse, S., Maronge, J. M., Rathouz, P. J., Heagerty, P. J., & Schildcrout, J. S. (2021). Two-wave two-phase outcome-dependent sampling designs, with applications to longitudinal binary data. Statistics in Medicine, 40(8), 1863–1876. https://doi.org/10.1002/sim.8876

See Also

cv_linear2ph() to calculate the average predicted log likelihood of this function.

Examples

## Not run: 
# Regression model: CD4 ~ VL + Sex. CD4 and VL are partially validated.
data("mock.vccc")
sn <- 20
data.linear <- spline2ph(x = "VL_unval", data = mock.vccc, size = sn,
                         degree = 3,  group = "Sex")
res_linear <- linear2ph(y_unval = "CD4_unval", y = "CD4_val",
                        x_unval = "VL_unval", x = "VL_val",
                        z = "Sex", data = data.linear,  hn_scale = 1,
                        se = TRUE, tol = 1e-04, max_iter = 1000,
                        verbose = FALSE)

## End(Not run)

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). See pacakge vigenette for code examples.

Usage

logistic2ph(
  y_unval = NULL,
  y = NULL,
  x_unval = NULL,
  x = NULL,
  z = NULL,
  data = NULL,
  hn_scale = 1,
  se = TRUE,
  tol = 1e-04,
  max_iter = 1000,
  verbose = FALSE
)

Arguments

y_unval

Column name of the error-prone or unvalidated binary outcome. This argument is optional. If y_unval = NULL (the default), y is treated as error-free.

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.

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.

se

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

Details

Models for logistic2ph() are specified through the arguments. The dataset input should at least contain columns for unvalidated error-prone outcome, validated error-prone outcome, unvalidated error-prone covariate(s), validated error-prone covariate(s), and B-spline basis. B-spline basis can be generated from splines::bs() function, with argument x being the unvalidated error-prone covariate(s). See vignette for options in tuning the B-spline basis.

Value

logistic2ph() returns an object of class "logistic2ph". The function coef() is used to obtain the coefficients of the fitted model. The function summary() is used to obtain and print a summary of results.

An object of class "logistic2ph" is a list containing at least the following components:

call

the matched call.

coefficients

A named vector of the logistic regression coefficient estimates.

covariance

The covariance matrix of the logistic 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.

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

Examples

## Not run: 
# Regression model: ADE ~ CD4 + Prior_ART. ADE and CD4 are partially validated.
data("mock.vccc")
sn <- 20
data.logistic <- spline2ph(x = "CD4_unval", size = 20, degree = 3,
                           data = mock.vccc, group = "Prior_ART",
                           split_group = TRUE)
res_logistic <- logistic2ph(y = "ADE_val", y_unval = "ADE_unval",
                           x = "CD4_val", x_unval = "CD4_unval",
                            z = "Prior_ART", data = data.logistic,
                            hn_scale = 1/2, se = TRUE, tol = 1e-04,
                            max_iter = 1000, verbose = FALSE)

## End(Not run)

Mock VCCC dataset.

Description

A simulated dataset constructed to imitate the Vanderbilt Comprehensive Care Clinic (VCCC) patient records, which have been fully validated and therefore contain validated and unvalidated versions of all variables. The VCCC cohort is a good candidate for the purpose of illustration. The data presented in this section are a mocked-up version of the actual data due to confidentiality, but the data structure and features, such as mean and variability, closely resemble the real dataset.

Usage

mock.vccc

Format

A data frame with 2087 rows and 8 variables:

ID

patient ID

VL_unval

viral load at antiretroviral therapy (ART) initiation, error-prone outcome, continuous

VL_val

viral load at antiretroviral therapy (ART) initiation, validated outcome, continuous

ADE_unval

having an AIDS-defining event (ADE) within one year of ART initiation, error-prone outcome, binary

ADE_val

having an AIDS-defining event (ADE) within one year of ART initiation, validated outcome, binary

CD4_unval

CD4 count at ART initiation, error-prone covariate, continuous

CD4_val

CD4 count at ART initiation, validated covariate, continuous

Prior_ART

whether patient is ART naive at enrollment, error-free covariate, binary

Sex

sex of patient, 1 indicates male and 0 indicates female & error-free covariate, binary

Age

age of patient, error-free covariate, continuous

Source

https://www.vanderbilthealth.com/clinic/comprehensive-care-clinic

Print Method for linear2ph Objects

Description

Prints the details of a linear2ph object.

Usage

## S3 method for class 'linear2ph'
print(x, ...)

Arguments

x

An object of class linear2ph.

...

Additional arguments passed to methods

Print Method for logistic2ph Objects

Description

Prints the details of a logistic2ph object.

Usage

## S3 method for class 'logistic2ph'
print(x, ...)

Arguments

x

An object of class logistic2ph.

...

Additional arguments passed to methods

Print Method for summary.linear2ph Objects

Description

Prints a structured summary of a linear2ph model.

Usage

## S3 method for class 'summary.linear2ph'
print(x, ...)

Arguments

x

An object of class summary.linear2ph.

...

Additional arguments passed to methods

Value

Invisibly returns x.

Print Method for summary.logistic2ph Objects

Description

Prints a structured summary of a logistic2ph model.

Usage

## S3 method for class 'summary.logistic2ph'
print(x, ...)

Arguments

x

An object of class summary.logistic2ph.

...

Additional arguments passed to methods

Value

Invisibly returns x.

Splines for two-phase regression functions

Description

Creates splines for two-phase regression function in this package, including linear2ph, logistic2ph, cv_linear2ph, cv_logistic2ph.

Usage

spline2ph(
  x,
  data,
  size = 20,
  degree = 3,
  bs_names = NULL,
  group = NULL,
  split_group = TRUE
)

Arguments

x

Column names of the covariate of the dataset.

data

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

size

Pass on to the df argument in splines::bs(). Degrees of freedom for EACH variable.

degree

Pass on to the degree argument in splines::bs(). Degree of the piecewise polynomial. Default is 3 for cubic splines.

bs_names

Optional. Vecotr of column names of the output B-spline basis matrix. When not specified, a default will be provided.

group

Optional. Column name of the categorical variable of which might have heterogeneous errors among different groups.

split_group

Optional. Whether to split by group proportion for the group with B-spline size if the group argument is provided. If FALSE, then the split will be averaged across all groups. Default is TRUE.

Details

This function can be directly applied for regression model with one or more error-prone continuous covariates.

Value

the data.frame object including the original dataset and the B-spline bases.

Examples

# example code
data("mock.vccc")
sn <- 20
data.linear <- spline2ph(x = "VL_unval", data = mock.vccc, size = sn,
                         degree = 3,  group = "Sex")

Summary Method for linear2ph Objects

Description

Summarizes the details of a linear2ph object.

Usage

## S3 method for class 'linear2ph'
summary(object, ...)

Arguments

object

An object of class linear2ph.

...

Additional arguments passed to methods

Value

An object of class summary.linear2ph, containing the call, coefficients, and covariance.

Summary Method for logistic2ph Objects

Description

Summarizes the details of a logistic2ph object.

Usage

## S3 method for class 'logistic2ph'
summary(object, ...)

Arguments

object

An object of class logistic2ph.

...

Additional arguments passed to methods

Value

An object of class summary.logistic2ph, containing the call, coefficients, and covariance.