SynDI (version 0.1.0)

Initial.estimates: Internal estimation

Description

Calculate the initial estimates for external populations.

Usage

Initial.estimates(datan, gamma.I, X, B, beta, Btype)

Arguments

datan

internal data only

gamma.I

regression estimates using internal data only (datan)

X

a vector of predictor that were used in the external study, e.g. X = c('X1','X2','X3')

B

a vector of covariates that were not used in the external study, e.g. B=c('X4','B1','B2')

beta

a vector of external model estimates, the vector order should be the same as listed in X, e.g. names(beta) = c("int", "X1", "X2", "X3")

Btype

a vector of type of B, either continuous or binary. If "continuous", linear regression will be used; if "binary", logistic regression will be used. More types can be implemented manually.

Value

a numeric vector of estimated coefficients of the target model for the given external population. Assume the internal data contains p predictors. The vector is of dimension (p+1), including the estimates of the intercept.

References

Neuhaus, J. and Jewell, N. (1993). A geometric approach to assess bias due to omitted covariates in generalized linear models. Biometrika 80,807<U+2013>815.

Gu, T., Taylor, J.M.G. and Mukherjee, B. (2021) Regression inference for multiple populations by integrating summary-level data using stacked imputations https://arxiv.org/abs/2106.06835.

Examples

# NOT RUN {
#' data(initial_estimates_example)

datan = initial_estimates_example$datan
gamma.I = initial_estimates_example$gamma.I
beta = initial_estimates_example$beta

# calculate the initial gamma for population S=1
gamma.S1.origin = Initial.estimates(datan = datan, gamma.I = gamma.I, 
    X = c('X1', 'X2', 'X3'), B = c('X4', 'B1', 'B2'), 
    beta = beta, Btype = c('continuous', 'continuous', 'binary'))

# }