Description Usage Arguments Details Value References
Implements the regression calibration method described by Lyles and Kupper (J. Agric. Biol. Environ. Stat. 2012).
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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_family |
Character string specifying family of true disease model
(see |
mem_covariates |
Character vector specifying variables in measurement error model. |
all_imputed |
Logical value for whether to use imputed Z's for all subjects, even those with the actual Z's observed (i.e. internal validation subjects). |
boot_var |
Logical value for whether to calculate a bootstrap variance-covariance matrix. |
boots |
Numeric value specifying number of bootstrap samples to use. |
alpha |
Significance level for percentile bootstrap confidence interval. |
The true disease model is a GLM:
g[E(Y)] = beta_0 + beta_z Z + beta_c^T C + beta_b^T B
The measurement error model is:
log(Z) = alpha_0 + alpha_d^T D + alpha_c^T C + d, d ~ N(0, sigsq_d)
The procedure is as follows: in the validation study, fit the measurement error model to estimate (alpha, sigsq_d); in the main study, calculate E(Z|D,C) = exp(alpha_0 + alpha_d^T D + alpha_c^T C + sigq_d / 2 and fit the true disease model with those values in place of the unobserved Z's.
If boot_var = TRUE
, list containing parameter estimates,
variance-covariance matrix, and percentile bootstrap confidence intervals;
otherwise just the parameter estimates.
Lyles, R.H. and Kupper, L.L. (2012) "Approximate and pseudo-likelihood analysis for logistic regression using external validation data to model log exposure." J. Agric. Biol. Environ. Stat. 18(1): 22-38.
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