ml_linear_logistic: Maximum Likelihood with Linear Regression Disease Model and...

In vandomed/meuc: Measurement Error and Unmeasured Confounding

Description

Calculates maximum likelihood estimates for measurement error/unmeasured confounding scenario where true disease model is linear regression and measurement error model is logistic regression.

Usage

ml_linear_logistic(all_data = NULL, main = NULL, internal = NULL,
  external = NULL, y_var, z_var, d_vars = NULL, c_vars = NULL,
  b_vars = NULL, tdm_covariates = NULL, mem_covariates = NULL,
  estimate_var = TRUE, ...)

Arguments

all_data	Data frame with data for main study and validation study.
main	Data frame with data for the main study.
internal	Data frame with data for internal validation study.
external	Data frame with data for the external validation study.
y_var	Character string specifying name of Y variable.
z_var	Character string specifying name of Z variable.
d_vars	Character string specifying name of D variables.
c_vars	Character vector specifying names of C variables.
b_vars	Character vector specifying names of variables in true disease model but not in measurement error model.
tdm_covariates	Character vector specifying variables in true disease model. The Z variable is automatically included whether you include it in tdm_covariates or not.
mem_covariates	Character vector specifying variables in measurement error model.
estimate_var	Logical value for whether to return variance-covariance matrix for parameter estimates.
...	Additional arguments to pass to nlminb.

Details

The true disease model is:

Y = beta_0 + beta_z Z + beta_c^T C + beta_b^T B + e, e ~ N(0, sigsq_e)

The measurement error model is:

logit[P(Z = 1)] = alpha_0 + alpha_d^T D + alpha_c^T C

There should be main study data with (Y, D, C, B) as well as internal validation data with (Y, Z, D, C, B) and/or external validation data with (Z, D, C). Parameters are theoretically identifiable without validation data, but estimation may be impractical in that scenario.

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

vandomed/meuc documentation built on May 12, 2019, 6:17 p.m.