psc_cond_exp: Propensity Score Calibration (Conditional Expectation Method)

In vandomed/meuc: Measurement Error and Unmeasured Confounding

Description

Implements the "conditional expectation" version of propensity score calibration as described by Sturmer et al. (Am. J. Epidemiol. 2005). For the "algebraic" version, see psc_algebraic.

Usage

psc_cond_exp(all_data = NULL, main = NULL, internal = NULL,
  external = NULL, y_var, x_var, gs_vars, ep_vars,
  tdm_family = "gaussian", surrogacy = TRUE, ep_data = "separate",
  boot_var = FALSE, boots = 100, alpha = 0.05)

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.
x_var	Character string specifying name of X variable.
gs_vars	Character vector specifying names of variables for gold standard propensity score.
ep_vars	Character vector specifying names of variables for error-prone propensity score.
tdm_family	Character string specifying family of true disease model (see glm).
surrogacy	Logical value for whether to assume surrogacy, which means that the error-prone propensity score is not informative of Y given X and the gold standard propensity score. Have to assume surrogacy if validation data is external.
ep_data	Character string controlling what data is used to fit the error-prone propensity score model. Choices are "validation" for validation study data, "all" for main study and validation study data, and "separate" for validation data for first step and main study data for second step.
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.

Details

The true disease model is a GLM:

g[E(Y)] = beta_0 + beta_x X + beta_g G

where G = P(X|C,Z), with C but not Z available in the main study.

In a validation study with (X, C, Z), logistic regression is used to obtain fitted probabilities for G as well as an error-prone version G* = P(X|C). A linear model is fitted to map from G* to E(G|G*). Finally, in the main study, G*'s are calculated, then E(G|G*), and the disease model is fit for Y vs. (X, E(G|G*)).

References

Sturmer, T., Schneeweiss, S., Avorn, J. and Glynn, R.J. (2005) "Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration." Am. J. Epidemiol. 162(3): 279-289.

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