High-Performance Open-Source Archive
adjust() functions - remove
eval(parse()) and switch to matrix multiplication for
better efficiencymultibias_plot() to visualize sensitivity
analysis resultsmultibias_adjust() the
function now incorporates uncertainty of the effect estimates from the
validation data by sampling from each estimate’s mean and SE. Now, when
using validation data, the confidence intervals from multibias
bootstrapped results will represent two sources of uncertainty: random
error and systematic error.bias_params as an input for
multibias_adjust()multibias_adjust() now has built in bootstrappingsummary() method to
data_observedbias_params class to handle bias parameter
inputs to multibias_adjust()adjust() functions with a single
multibias_adjust() function. Users now specify the biases
they want to adjust for in the data_observed object. Bias
adjustment formulas are now found in the bias_params
documentation.bias input of data_observedevans data; now only used in vignettepkgdown web page:
www.paulbrendel.com/multibiasdata_validation as
an input for bias adjustment:
adjust_om_sel.Radjust_uc_sel.Radjust_uc_em.Radjust_uc_om.Radjust_uc_em_sel.Radjust_uc_om_sel.Rdata_validation as
an input for bias adjustment:
adjust_em_om.Radjust_em_sel.Radjust_em.R and
adjust_om.Rdata_observed and
data_validationdata_observed to represent observed
causal dataadjust functions now take
data_observed as inputdata_validation to represent causal
data that can be used as validaiton data for bias adjustmentdata_validation as
an input for bias adjustment:
adjust_uc.Radjust_em.Radjust_om.Radjust_sel.Remc
changed to emomc
changed to omadjust_multinom_uc_em_sel into
adjust_uc_em_seladjust_multinom_uc_om_sel into
adjust_uc_om_seladjust_uc_em_sel.Radjust_uc_om_sel.Radjust_multinom_emc_omc into
adjust_emc_omcadjust_multinom_uc_emc into
adjust_uc_emcadjust_multinom_uc_omc into
adjust_uc_omcadjust_emc_sel (exposure must be binary)adjust_omc_sel (outcome must be binary)adjust_uc_emc (exposure must be binary)adjust_uc_omc (outcome must be binary)adjust_multinom_uc_emc (exposure must be binary)adjust_multinom_uc_omc (outcome must be binary)df_omc_seldf_omc_sel_sourceadjust_ucadjust_emc (exposure must be binary)adjust_omc (outcome must be binary)adjust_seladjust_uc_seldf_uc_omcdf_uc_omc_sourcedf_uc_emcdf_uc_emc_sourcedf_uc and df_uc_source now both
have continuous and binary exposures and outcomes.adjust_uc_omc_sel &
adjust_multinom_uc_omc_sel.df_uc_omc_sel and
df_uc_omc_sel_source.df_uc_seldf_uc_sel_sourceadjust_emc_omc &
adjust_multinom_emc_omc.df_emc_omc and
df_emc_omc_source.df_emc_seldf_emc_sel_sourceadjust_omc_sel.df_omc_sel and df_omc_sel_source.df_ucdf_uc_sourcedf_emcdf_emc_sourcedf_omcdf_omc_sourcedf_seldf_sel_sourceadjust_omc that appears when using three
confoundersadjust_uc_omc
and adjust_multinom_uc_omc.df_uc_omc and
df_uc_omc_source.adjust_omc.df_emcdf_emc_sourcedf_omcdf_omc_sourcedf_seldf_sel_sourcedf_ucdf_uc_sourceadjust_sel had been weighing with the probability of
selection instead of the inverse probability of
selection.
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