The BACprior.lm function provides estimates of the posterior mean and posterior standard deviation of the exposure effect for selected values of omega in the covariate inclusion indicators' prior distribution. The output allows the user to evaluate how sensitive the BAC procedure is to the choice of the hyperparameter omega value.
The BACprior.CV function proposes two cross-validation procedures to select BAC's omega value in an attempt to minimize the mean squared error (MSE) of the exposure effect estimator. The data are split into half V times. Each time, the exposure effect is estimated with omega = infinity on one half of the data. On the other half of the data, the exposure effect is estimated for each of the selected omega values. A criterion to be minimized is then computed. Details can be found in Lefebvre et al. (2014), which is inspired by a procedure proposed by Brookhart and van der Laan (2006). The BACprior.CV function uses the BACprior.lm function to estimate the exposure effect.
The BACprior package contains functions to help the user select the omega value appearing in the BAC prior distribution of the covariate inclusion indicators of the outcome and exposure models.
The BACprior.boot function proposes a bootstrap procedure to select BAC's omega value in an attempt to minimize the mean squared error (MSE) of the exposure effect estimator. A number B of bootstrap samples are taken from the original sample. Then, the MSE is estimated for each selected omega value, considering the exposure estimate with omega = infinity from the original sample as the true value. The BACprior.boot function uses the BACprior.lm function to estimate the exposure effect.
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