rc_cond_exp: Regression Calibration (Conditional Expectation Method)
In vandomed/meuc: Measurement Error and Unmeasured Confounding

Description Usage Arguments Details Value References

Implements the "conditional expectation" version of regression calibration as described by Rosner et al. (Stat. Med. 1989). For the "algebraic" version, see rc_algebraic.

rc_cond_exp(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, tdm_family = "gaussian",
  mem_covariates = NULL, mem_family = "gaussian",
  all_imputed = FALSE, boot_var = FALSE, boots = 100, alpha = 0.05)

`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.
`tdm_family`	Character string specifying family of true disease model (see `glm`).
`mem_covariates`	Character vector specifying variables in measurement error model.
`mem_family`	Character string specifying family of measurement error model (see `glm`).
`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:

h[E(Z)] = alpha_0 + alpha_d^T D + alpha_c^T C

The procedure is as follows: in the validation study, fit the measurement error model to estimate alpha's; in the main study, calculate E(Z|D,C) 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.

Kuha, J. (1994) "Corrections for exposure measurement error in logistic regression models with an application to nutritional data." Stat. Med. 13(11): 1135-1148.

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.

Rosner, B., Willett, W. and Spiegelman, D. (1989) "Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error." Stat. Med. 8(9): 1051-69.

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(1): 139-160.

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