Modeling, Standardization and Testing for Provider Profiling
Get a bar plot for flagging percentage overall and stratified by provi...
Get a caterpillar plot to display confidence intervals for standardize...
Get confidence intervals for provider effects or standardized measures...
Get confidence intervals for provider effects or standardized measures...
Get confidence intervals for provider effects or standardized measures...
Get confidence intervals for provider effects or standardized measures...
Get confidence intervals for provider effects or standardized measures...
Get confidence intervals for provider effects or standardized measures...
Data quality check function
Main Function for fitting correlated random effect linear model
Main function for fitting the fixed effect linear model
Main Function for fitting the random effect linear model
Main Function for fitting correlated random effect logistic model
Main function for fitting the fixed effect logistic model
Main function for fitting the fixed effect logistic model using firth ...
Main Function for fitting the random effect logistic model
Get funnel plot from a fitted linear_fe object for institutional com...
Get funnel plot from a fitted logis_fe object for institutional comp...
Calculate direct/indirect standardized differences from a fitted `line...
Calculate direct/indirect standardized differences from a fitted `line...
Calculate direct/indirect standardized differences from a fitted `line...
Calculate direct/indirect standardized ratios/rates from a fitted `log...
Calculate direct/indirect standardized ratios/rates from a fitted `log...
Calculate direct/indirect standardized ratios/rates from a fitted `log...
Generic function for calculating standardized measures
Result Summaries of Covariate Estimates from a fitted linear_fe, `li...
Result Summaries of Covariate Estimates from a fitted logis_fe objec...
Result Summaries of Covariate Estimates from a fitted logis_re or `l...
Conduct hypothesis testing for provider effects from a fitted `linear_...
Conduct hypothesis testing for provider effects from a fitted `linear_...
Conduct hypothesis testing for provider effects from a fitted `linear_...
Conduct hypothesis testing for provider effects from a fitted `logis_c...
Conduct hypothesis testing for provider effects from a fitted `logis_f...
Conduct hypothesis testing for provider effects from a fitted `logis_r...
Generic function for hypothesis testing of provider effects
Implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.