Description Usage Arguments Details Examples
Add integration model to a psdesign object
1  add_integration(psdesign, integration)

psdesign 
A psdesign object 
integration 
An integration object 
This is a list of the available integration models. The fundamental problem in surrogate evaluation is that there are unobserved values of the counterfactual surrogate responses S(1). In the estimated maximum likelihood framework, for subjects missing the S(1) values, we use an auxiliary pretreatment variable or set of variables W that is observed for every subject to estimate the distribution of S(1)  W. Typically, this W is a BIP. Then for each missing S(1), we integrate likelihood contributions over each nonmissing S(1) given their value of W, and average over the contributions.
integrate_parametric This is a parametric integration model that fits a linear model for the mean of S(1)  W and assumes a Gaussian distribution.
integrate_bivnorm This is another parametric integration model that assumes that S(1) and W are jointly normally distributed. The user must specify their mean, variances and correlation.
integrate_nonparametric This is a nonparametric integration model that is only valid for categorical S(1) and W. It uses the observed proportions to estimate the joint distribution of S(1), W.
integrate_semiparametric This is a semiparametric model that uses the semiparametric location scale model of Heagerty and Pepe (1999). Models are specified for the location of S(1)  W and the scale of S(1)  W. Then integrations are drawn from the empirical distribution of the residuals from that model, which are then transformed to the appropriate location and scale.
1 2 3  test < psdesign(generate_example_data(n = 100), Z = Z, Y = Y.obs, S = S.obs, BIP = BIP)
add_integration(test, integrate_parametric(S.1 ~ BIP))
test + integrate_parametric(S.1 ~ BIP) # same as above

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