Description Usage Arguments Details Value References
Implements an efficient version of regression calibration for main study/internal validation designs with one surrogate, as described by Spiegelman et al. (Stat. Med. 2001). Uses ideas from Greenland (Stat. Med. 1988).
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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. |
y_var |
Character string specifying name of Y variable. |
z_var |
Character string specifying name of Z variable. |
d_var |
Character string specifying name of D variable. |
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_family |
Character string specifying family of true disease model
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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:
E(Z) = alpha_d D + alpha_c^T C
And the naive disease model is:
g[E(Y)] = beta*_0 + beta*_Z D + beta*_C^T C + beta*_B^T B
The procedure involves obtaining two sets of beta estimates: one by fitting the true disease model with internal validation data, and the other by treating the validation study as external and doing a main study/external validation study correction. The two estimates are weighted by the inveres of their estimated variances.
List containing parameter estimates and variance estimates.
Greenland, S. (1988) "Variance estimation for epidemiologic effect estimates under misclassification." Stat. Med. 7: 745-757.
Spiegelman, D., Carroll, R.J., and Kipnis, V. (2001) "Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument." Stat. Med. 20: 139-160.
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