Description Usage Arguments Details References
Implements the "algebraic" version of propensity score calibration as described by Sturmer et al. (Am. J. Epidemiol. 2005), but with an extra D variable in the MEM, which allows the error-prone propensity score to be included in the TDM rather than assumed uninformative of the outcome.
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. |
x_var |
Character string specifying name of X variable. |
d_var |
Character string specifying name of D 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 |
ep_data |
Character string controlling what data is used to fit the
error-prone propensity score model. Choices are |
delta_var |
Logical value for whether to calculate a Delta method variance-covariance matrix. May not be justified theoretically because propensity scores are estimated, but also may perform better than bootstrap in certain scenarios, e.g. if bootstrap is prone to extreme estimates. |
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_x X + beta_g G + beta_gstar Gstar
where G = P(X|C,Z), with C but not Z available in the main study, and Gstar = P(X|C). Logistic regression models are used to model P(X = 1|Z,C) and P(X = 1|C):
logit[P(X = 1)] = gamma_0 + gamma_z^T Z + gamma_c^T C logit[P(X = 1)] = gamma_0^* + gamma_c^*T C
A linear model is used to map fitted error-prone propensity scores Gstar to the expected gold standard propensity score G logistic regression):
E(G) = lambda_0 + lambda_d D + lambda_x X + lambda_g Gstar
There should be main study data with (Y, X, D, C) as well as external validation data with (X, D, C, Z).
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.
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