Description Usage Arguments Details Value
Calculates maximum likelihood estimates for measurement error/unmeasured confounding scenario where true disease model and measurement error model are both logistic regression.
1 2 3 4 |
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
|
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 |
The true disease model is:
logit[P(Y = 1)] = beta_0 + beta_z Z + beta_c^T C + beta_b^T B
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.
List containing:
Numeric vector of parameter estimates.
Variance-covariance matrix (if estimate_var = TRUE
).
Returned nlminb
object from maximizing the
log-likelihood function.
Akaike information criterion (AIC).
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