#####STEP 0-1: Reset environment #####
rm(list=ls())
knitr::opts_chunk$set(echo = TRUE)
options(repos = structure(c(CRAN = "http://cran.rstudio.com/")))
#####STEP 0-2: Install packages #####
list.of.packages <- c( "grf", "metafor", "splitstackshape", "dplyr", "tidyverse", "foreach", "cowplot",
"reshape2", "doParallel", "survival", "readstata13", "ggplot2", "rsample", "DiagrammeR",
"e1071", "pscl", "pROC", "caret", "ModelMetrics", "MatchIt", "Hmisc", "scales",
"lmtest", "sandwich","haven", "rpms", "randomForest", "fabricatr", "gridExtra",
"VIM", "mice", "missForest", "lmtest", "ivreg", "kableExtra")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
lapply(list.of.packages, library, character.only = TRUE)
## Warning: package 'grf' was built under R version 4.3.3
## Warning: package 'metafor' was built under R version 4.3.3
## Warning: package 'metadat' was built under R version 4.3.3
## Warning: package 'numDeriv' was built under R version 4.3.1
## Warning: package 'splitstackshape' was built under R version 4.3.3
## Warning: package 'dplyr' was built under R version 4.3.3
## Warning: package 'tidyverse' was built under R version 4.3.3
## Warning: package 'ggplot2' was built under R version 4.3.3
## Warning: package 'tibble' was built under R version 4.3.3
## Warning: package 'tidyr' was built under R version 4.3.3
## Warning: package 'readr' was built under R version 4.3.3
## Warning: package 'purrr' was built under R version 4.3.3
## Warning: package 'stringr' was built under R version 4.3.3
## Warning: package 'forcats' was built under R version 4.3.3
## Warning: package 'lubridate' was built under R version 4.3.3
## Warning: package 'foreach' was built under R version 4.3.3
## Warning: package 'cowplot' was built under R version 4.3.3
## Warning: package 'reshape2' was built under R version 4.3.3
## Warning: package 'doParallel' was built under R version 4.3.3
## Warning: package 'iterators' was built under R version 4.3.3
## Warning: package 'readstata13' was built under R version 4.3.3
## Warning: package 'rsample' was built under R version 4.3.3
## Warning: package 'DiagrammeR' was built under R version 4.3.3
## Warning: package 'e1071' was built under R version 4.3.3
## Warning: package 'pscl' was built under R version 4.3.3
## Warning: package 'pROC' was built under R version 4.3.3
## Warning: package 'caret' was built under R version 4.3.3
## Warning: package 'ModelMetrics' was built under R version 4.3.3
## Warning: package 'MatchIt' was built under R version 4.3.3
## Warning: package 'Hmisc' was built under R version 4.3.3
## Warning: package 'scales' was built under R version 4.3.3
## Warning: package 'lmtest' was built under R version 4.3.3
## Warning: package 'zoo' was built under R version 4.3.3
## Warning: package 'sandwich' was built under R version 4.3.3
## Warning: package 'haven' was built under R version 4.3.3
## Warning: package 'rpms' was built under R version 4.3.3
## Warning: package 'randomForest' was built under R version 4.3.3
## Warning: package 'fabricatr' was built under R version 4.3.3
## Warning: package 'gridExtra' was built under R version 4.3.3
## Warning: package 'VIM' was built under R version 4.3.3
## Warning: package 'colorspace' was built under R version 4.3.3
## Warning: package 'mice' was built under R version 4.3.3
## Warning in check_dep_version(): ABI version mismatch:
## lme4 was built with Matrix ABI version 1
## Current Matrix ABI version is 0
## Please re-install lme4 from source or restore original 'Matrix' package
## Warning: package 'missForest' was built under R version 4.3.3
## Warning: package 'ivreg' was built under R version 4.3.3
## Warning: package 'kableExtra' was built under R version 4.3.3
## number of threads (affects grf results): 6
#####STEP 0-5: Set run-specific parameters #####
published_paper_run <- 0 # ANALYST FORM
if (published_paper_run == 1) {
print("save intermediate files into Cleaned_input_data/As_published/empirical/ folders")
warning("Changing this setting to 1 overwrites the input files required for replicating on different platforms.")
} else {
print("save intermediate files into Cleaned_input_data/Testing/empirical/ folder")
}
## [1] "save intermediate files into Cleaned_input_data/Testing/empirical/ folder"
#####STEP 0-6: Set file paths #####
# Set the processed path based on published_paper_run value
# ***Important: for file paths to work, use the drop-down menu on "Knit" to select the "Current Working Directory" under "Knit Directory." This assumes that you have cloned the repo to your local directory.***
processedpath <- if (published_paper_run == 1) {
"PP_Full_Analysis/Cleaned_input_data/As_published/empirical/"
} else {
"PP_Full_Analysis/Cleaned_input_data/Testing/empirical/"
}
# Print the processed path
print(paste("Processed data path:", processedpath))
## [1] "Processed data path: PP_Full_Analysis/Cleaned_input_data/Testing/empirical/"
#####STEP 1-1: Read data #####
# ***Important: for file paths to work, use the drop-down menu on "Knit" to select the "Current Working Directory" under "Knit Directory." This assumes that you have cloned the repo to your local directory.***
d <- read_dta("PP_Full_Analysis/Input_Data/data_for_analysis.dta")
print("Information on raw data:")
## [1] "Information on raw data:"
## Rows: 20,745
## Columns: 559
## $ person_id <dbl> 5, 8, 9, 16, 17, 18, 19, 23, 24, 29, 33, 34…
## $ in_inperson_data <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ sample_inp_resp <dbl+lbl> 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, …
## $ weight_total_inp <dbl> 1.1504, 0.8975, 0.0000, 1.0000, 1.2126, 1.0…
## $ dt_release_inp <date> 2009-12-28, 2010-03-09, NA, 2010-04-12, 20…
## $ dt_completed_inp <date> 2010-02-06, 2010-04-03, NA, 2010-05-01, 20…
## $ interview_location_inp <dbl+lbl> 1, 3, NA, 4, 4, 1, NA, 2, 1, 2,…
## $ interviewer_inp <dbl> 6, 3, NA, 17, 17, 44, NA, 5, 11, 14, NA, NA…
## $ scale_id <dbl+lbl> 29, 19, NA, 6, 2, 23, NA, 41, 30, 40,…
## $ stadio_id <dbl+lbl> 28, 20, NA, 6, 1, 24, NA, 41, 25, 40,…
## $ omron_id <dbl+lbl> 12, 15, NA, 4, 2, 21, NA, 11, 18, 36,…
## $ language_capi_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 1, 0,…
## $ interpreter_inp <dbl+lbl> 0, 0, NA, 0, 0, NA, NA, 1, 0, 0,…
## $ gender_inp <dbl+lbl> 1, 0, NA, 1, 0, 0, NA, 1, 0, 0,…
## $ age_inp <dbl> 60, 41, NA, 39, 52, 51, NA, 32, 34, 23, NA,…
## $ health_last12_inp <dbl+lbl> 5, 3, NA, 4, 4, 4, NA, 6, 3, 1,…
## $ health_change_inp <dbl+lbl> 2, 1, NA, 2, 2, 1, NA, 2, 2, 1,…
## $ sf4_inp <dbl+lbl> 2, 3, NA, 2, 5, 2, NA, 2, 3, 4,…
## $ ast_dx_pre_lottery <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 1,…
## $ dia_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ hbp_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 0,…
## $ chl_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ ami_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ chf_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ emp_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ kid_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ cancer_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ dep_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 1,…
## $ dia_dx_post_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ hbp_dx_post_lottery <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ chl_dx_post_lottery <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ dep_dx_post_lottery <dbl+lbl> 0, 0, NA, NA, 0, 0, NA, 0, 0, 0,…
## $ happiness_inp <dbl+lbl> 0, 1, NA, 1, 2, 1, NA, 1, 0, 2,…
## $ phqtot_inp <dbl> 1, 9, NA, 2, 13, 2, NA, 3, 2, 14, NA, NA, N…
## $ pcs8_score <dbl> 55.33, 20.08, NA, 50.22, 44.19, 46.48, NA, …
## $ mcs8_score <dbl> 45.38, 53.05, NA, 50.81, 47.71, 42.73, NA, …
## $ usual_clinic_inp <dbl+lbl> NA, 0, NA, 1, 0, 0, NA, 1, 0, 0,…
## $ needmet_med_inp <dbl+lbl> 1, 0, NA, 1, 0, 1, NA, 1, 1, 0,…
## $ needmet_ment_inp <dbl+lbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 0,…
## $ needmet_rx_inp <dbl+lbl> 1, 0, NA, 1, 1, 1, NA, 1, 1, 0,…
## $ med_qual_inp <dbl+lbl> 0, 2, NA, 2, 5, 0, NA, 5, 0, 1,…
## $ chl_chk_inp <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ pap_chk_inp <dbl+lbl> 0, NA, NA, 1, NA, NA, NA, 0, NA, NA,…
## $ mam_chk_inp <dbl+lbl> 0, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ fobt_chk_inp <dbl+lbl> 0, NA, NA, NA, 0, 0, NA, NA, NA, NA,…
## $ col_chk_inp <dbl+lbl> 0, NA, NA, NA, 0, 0, NA, NA, NA, NA,…
## $ psa_chk_inp <dbl+lbl> NA, NA, NA, NA, 0, 0, NA, NA, NA, NA,…
## $ did_flu_inp <dbl+lbl> 0, NA, NA, NA, 0, 0, NA, NA, NA, NA,…
## $ smk_curr_inp <dbl+lbl> 2, 0, NA, 2, 0, 0, NA, 2, 2, 2,…
## $ cvd_risk_point <dbl> 0.137, 0.112, NA, 0.033, 0.253, 0.156, NA, …
## $ doc_num_incl_probe_inp <dbl> 0, 6, NA, 12, 0, 0, NA, 5, 0, 5, NA, NA, NA…
## $ doc_any_incl_probe_inp <dbl+lbl> 0, 1, NA, 1, 0, 0, NA, 1, 0, 1,…
## $ ed_num_incl_probe_inp <dbl> 0, 2, NA, 1, 1, 0, NA, 0, 0, 10, NA, NA, NA…
## $ ed_any_incl_probe_inp <dbl+lbl> 0, 1, NA, 1, 1, 0, NA, 0, 0, 1,…
## $ surg_num_incl_probe_inp <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ surg_any_incl_probe_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1,…
## $ hosp_num_incl_probe_inp <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ hosp_any_incl_probe_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ tot_med_spend_other_inp <dbl> 0, 170, NA, 0, 456, 0, NA, 0, 0, 265, NA, N…
## $ any_oop_spending <dbl+lbl> 0, 1, NA, 0, 1, 0, NA, 0, 0, 1,…
## $ catastrophic_exp_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ ins_any_inp <dbl+lbl> 1, 0, NA, 1, 0, 0, NA, 1, 0, 0,…
## $ ins_ohp_inp <dbl+lbl> 0, 0, NA, 1, 0, 0, NA, 1, 0, 0,…
## $ ins_private_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ owe_inp <dbl+lbl> 0, 1, NA, 0, 1, 0, NA, 1, 0, 1,…
## $ borrow_inp <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ edu_inp <dbl+lbl> 2, 2, NA, 2, 1, 3, NA, 1, 1, 1,…
## $ hispanic_inp <dbl+lbl> 1, 0, NA, 0, 0, 0, NA, 1, 1, 0,…
## $ race_white_inp <dbl+lbl> 0, 1, NA, 1, 1, 0, NA, 0, 1, 0,…
## $ race_black_inp <dbl+lbl> 0, 0, NA, 0, 0, 1, NA, 0, 0, 1,…
## $ race_nwother_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1,…
## $ a1c_inp <dbl> 5.037, 5.201, NA, 5.854, 5.364, 5.527, NA, …
## $ hdl_inp <dbl> 48.33, 51.33, NA, 38.58, 51.33, 28.08, NA, …
## $ chl_inp <dbl> 241.0, 229.9, NA, 229.9, 235.4, 177.7, NA, …
## $ bmi_inp <dbl> 26.66, 35.23, NA, 37.12, 24.81, 27.02, NA, …
## $ bp_sar_inp <dbl> 144, 134, NA, 126, 168, 119, NA, 98, 108, 1…
## $ bp_dar_inp <dbl> 81, 82, NA, 94, 110, 79, NA, 59, 63, 76, NA…
## $ has_bp_inp <dbl+lbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 1,…
## $ has_waist_inp <dbl+lbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 1,…
## $ has_hght_wght_inp <dbl+lbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 1,…
## $ has_dbs_inp <dbl+lbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 1,…
## $ has_all_dbs_inp <dbl+lbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 1,…
## $ rx_any_mod_inp <dbl+lbl> 0, 1, NA, 1, 1, 0, NA, 0, 0, 1,…
## $ rx_num_mod_inp <dbl> 0, 2, NA, 2, NA, 0, NA, 0, 0, 3, NA, NA, NA…
## $ hbp_diure_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ antihyperlip_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ diabetes_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ antidep_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0,…
## $ meds_miss_comp <dbl+lbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 0,…
## $ household_id <dbl> 100005, 102094, 100009, 140688, 100017, 100…
## $ treatment <dbl+lbl> 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, …
## $ draw_lottery <dbl+lbl> 7, 8, 1, 2, 6, 4, 8, 3, 6, 6, 6, 4, 3, …
## $ numhh_list <dbl+lbl> 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, …
## $ have_phone_list <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ english_list <dbl+lbl> 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, …
## $ first_day_list <dbl+lbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ pobox_list <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ self_list <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, …
## $ sample_12m_resp <dbl+lbl> 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, …
## $ weight_12m <dbl> 1.000, 1.000, 1.000, 2.824, 0.000, 0.000, 0…
## $ ohp_all_ever_admin <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ ohp_all_ever_inperson <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ ohp_all_ever_survey <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ ohp_all_end_inperson <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ ohp_all_mo_inperson <dbl+lbl> 0, 0, 0, 15, 0, 0, 0, 25, 0, 0,…
## $ ohp_std_ever_inperson <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ age_decile_inp <dbl> 10, 5, NA, 5, 8, 8, NA, 3, 4, 1, NA, NA, NA…
## $ age_decile_dum_inp2 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ age_decile_dum_inp3 <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, NA, NA, NA,…
## $ age_decile_dum_inp4 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 1, 0, NA, NA, NA,…
## $ age_decile_dum_inp5 <dbl> 0, 1, NA, 1, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ age_decile_dum_inp6 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ age_decile_dum_inp7 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ age_decile_dum_inp8 <dbl> 0, 0, NA, 0, 1, 1, NA, 0, 0, 0, NA, NA, NA,…
## $ age_decile_dum_inp9 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ age_decile_dum_inp10 <dbl> 1, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ older <dbl> 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ younger <dbl> 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0…
## $ age_19_34_inp <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 1, 1, NA, NA, NA,…
## $ age_35_49_inp <dbl> 0, 1, NA, 1, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ age_50_64_inp <dbl> 1, 0, NA, 0, 1, 1, NA, 0, 0, 0, NA, NA, NA,…
## $ int_loc_cat_inp_1 <dbl> 1, 0, NA, 0, 0, 1, NA, 0, 1, 0, NA, NA, NA,…
## $ int_loc_cat_inp_2 <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 0, 1, NA, NA, NA,…
## $ int_loc_cat_inp_3 <dbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ int_loc_cat_inp_4 <dbl> 0, 0, NA, 1, 1, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ interview_season <dbl+lbl> 4, 1, NA, 1, 4, 3, NA, 2, 1, 1,…
## $ interview_season_1 <dbl> 0, 1, NA, 1, 0, 0, NA, 0, 1, 1, NA, NA, NA,…
## $ interview_season_2 <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, NA, NA, NA,…
## $ interview_season_3 <dbl> 0, 0, NA, 0, 0, 1, NA, 0, 0, 0, NA, NA, NA,…
## $ interview_season_4 <dbl> 1, 0, NA, 0, 1, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ interview_weekend <dbl> 1, 1, NA, 1, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ itvr_1 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_2 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_3 <dbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_4 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_5 <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, NA, NA, NA,…
## $ itvr_6 <dbl> 1, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_7 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_8 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_9 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_10 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_11 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 1, 0, NA, NA, NA,…
## $ itvr_12 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_13 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_14 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ itvr_15 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_16 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_17 <dbl> 0, 0, NA, 1, 1, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_18 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_19 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_20 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_21 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_22 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_23 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_24 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_25 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_26 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_27 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_28 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_29 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_30 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_31 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_32 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_33 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_34 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_35 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_36 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_37 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_38 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_39 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_40 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_41 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_42 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_43 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_44 <dbl> 0, 0, NA, 0, 0, 1, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_45 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_46 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_47 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_48 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvr_49 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_1 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_2 <dbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_3 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_4 <dbl> 0, 0, NA, 1, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_5 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_6 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_7 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_8 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_9 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_10 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_11 <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, NA, NA, NA,…
## $ omron_12 <dbl> 1, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_13 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_14 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_15 <dbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_16 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_17 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_18 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 1, 0, NA, NA, NA,…
## $ omron_19 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_20 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_21 <dbl> 0, 0, NA, 0, 0, 1, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_22 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_23 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_24 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_25 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_26 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_27 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_28 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_29 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_30 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_31 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_32 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_33 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_34 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_35 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_36 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ omron_37 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_38 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_39 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_40 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ omron_41 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_1 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_2 <dbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_3 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_4 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_5 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_6 <dbl> 0, 0, NA, 1, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_7 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_8 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_9 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_10 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_11 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_12 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_13 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_14 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_15 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_16 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_17 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_18 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_19 <dbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_20 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_21 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_22 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_23 <dbl> 0, 0, NA, 0, 0, 1, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_24 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_25 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_26 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_27 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_28 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_29 <dbl> 1, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_30 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 1, 0, NA, NA, NA,…
## $ scale_31 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_32 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_33 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_34 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_35 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_36 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_37 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_38 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_39 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_40 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ scale_41 <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, NA, NA, NA,…
## $ scale_42 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ scale_43 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_1 <dbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_2 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_3 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_4 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_5 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_6 <dbl> 0, 0, NA, 1, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_7 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_8 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_9 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_10 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_11 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_12 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_13 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_14 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_15 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_16 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_17 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_18 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_19 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_20 <dbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_21 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_22 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_23 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_24 <dbl> 0, 0, NA, 0, 0, 1, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_25 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 1, 0, NA, NA, NA,…
## $ stadio_26 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_27 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_28 <dbl> 1, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_29 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_30 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_31 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_32 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_33 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_34 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_35 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_36 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_37 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_38 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_39 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_40 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ stadio_41 <dbl> 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, NA, NA, NA,…
## $ stadio_42 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ stadio_43 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ itvw_english_inp <dbl> 1, 1, NA, 1, 1, 1, NA, 0, 0, 1, NA, NA, NA,…
## $ valid_meds_inp <dbl> 1, 1, NA, 1, 0, 1, NA, 1, 1, 1, NA, NA, NA,…
## $ pain_low_inp <dbl> 1, 0, NA, 1, 0, 1, NA, 1, 0, 0, NA, NA, NA,…
## $ health_last12_good <dbl+lbl> 1, 0, NA, 1, 1, 1, NA, 1, 0, 0,…
## $ health_last12_notbad <dbl+lbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 0,…
## $ health_change_noworse <dbl+lbl> 1, 0, NA, 1, 1, 0, NA, 1, 1, 0,…
## $ obese <dbl> 0, 1, NA, 1, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ bp_prehyper <dbl> 1, 1, NA, 1, 1, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ bp_hyper <dbl> 1, 0, NA, 1, 1, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ a1c_dia <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ a1c_pre_dia <dbl> 0, 0, NA, 1, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ chl_high <dbl> 1, 1, NA, 1, 1, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ chl_h <dbl> 1, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ hdl_low <dbl> 0, 0, NA, 1, 0, 1, NA, 1, 1, 0, NA, NA, NA,…
## $ hdl_high <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ phqtot_high <dbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ phq_prob <dbl> 0.002, 0.129, NA, 0.002, 0.580, 0.002, NA, …
## $ any_dx_pre_lottery <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ med_qual_bin_inp <dbl+lbl> NA, 0, NA, 0, 1, NA, NA, 1, NA, 0,…
## $ smk_curr_bin_inp <dbl+lbl> 0, 1, NA, 0, 1, 1, NA, 0, 0, 0,…
## $ poshappiness_bin_inp <dbl+lbl> 1, 1, NA, 1, 0, 1, NA, 1, 1, 0,…
## $ mam50_chk_inp <dbl> 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ tr_tot_med_spend_other_inp <dbl> 0, 170, NA, 0, 456, 0, NA, 0, 0, 265, NA, N…
## $ doc_num_mod_inp <dbl> 0, 6, NA, 12, 0, 0, NA, 5, 0, 5, NA, NA, NA…
## $ ed_num_mod_inp <dbl> 0, 2, NA, 1, 1, 0, NA, 0, 0, 10, NA, NA, NA…
## $ surg_num_mod_inp <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ hosp_num_mod_inp_2 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA,…
## $ response_time <dbl> 40, 25, NA, 19, 16, 23, NA, 9, 17, 18, NA, …
## $ has_anthro_inp <dbl> 1, 1, NA, 1, 1, 1, NA, 1, 1, 1, NA, NA, NA,…
## $ any_oop_inp <dbl> 0, 1, NA, 0, 1, 0, NA, 0, 0, 1, NA, NA, NA,…
## $ tr_tot_spend_inp <dbl> 0, 170, NA, 0, 456, 0, NA, 0, 0, 265, NA, N…
## $ tot_spend_inp <dbl> 0, 170, NA, 0, 456, 0, NA, 0, 0, 265, NA, N…
## $ constant <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ nnnnumhh_li_2 <dbl> 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1…
## $ nnnnumhh_li_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ llldraw_lot_2 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ llldraw_lot_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0…
## $ llldraw_lot_4 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ llldraw_lot_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ llldraw_lot_6 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0…
## $ llldraw_lot_7 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ llldraw_lot_8 <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ sampbase2_inp <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ sample_0m <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ wave_survey0m <dbl+lbl> 7, 3, 3, 6, 4, 4, 8, 3, 1, 5, 6, 4, 3, …
## $ dt_mail_0m <date> 2008-09-07, 2008-07-14, 2008-07-14, 2008-0…
## $ returned_0m <dbl+lbl> 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, …
## $ dt_returned_0m <date> NA, 2008-08-05, 2008-08-29, NA, 2008-09-10…
## $ ret_mode_0m <chr> "", "Mail", "Phone", "", "Phone", "", "", "…
## $ surv_lang_0m <chr> "", "English", "English", "", "English", ""…
## $ in_survey_0m <dbl+lbl> 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, …
## $ app_received_0m <dbl+lbl> NA, 1, 1, NA, 1, NA, NA, 1, NA, 0,…
## $ app_sentin_0m <dbl+lbl> NA, 1, 0, NA, 0, NA, NA, 1, NA, NA,…
## $ app_prob_inc_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, NA,…
## $ app_prob_ins_0m <dbl+lbl> NA, NA, 1, NA, 0, NA, NA, NA, NA, NA,…
## $ app_prob_fin_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, NA,…
## $ app_prob_not_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, NA,…
## $ app_prob_hass_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, NA,…
## $ app_prob_find_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, NA,…
## $ app_prob_ofind_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, NA,…
## $ app_prob_some_0m <dbl+lbl> NA, NA, 0, NA, 1, NA, NA, NA, NA, NA,…
## $ app_prob_dont_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, NA,…
## $ app_approved_det_0m <dbl+lbl> NA, 2, NA, NA, NA, NA, NA, 1, NA, NA,…
## $ app_deny_high_0m <dbl+lbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA,…
## $ app_deny_long_0m <dbl+lbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA,…
## $ app_deny_late_0m <dbl+lbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA,…
## $ app_deny_pap_0m <dbl+lbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA,…
## $ app_deny_opap_0m <dbl+lbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA,…
## $ app_deny_oth_0m <dbl+lbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA,…
## $ app_deny_dont_0m <dbl+lbl> NA, 1, NA, NA, NA, NA, NA, 0, NA, NA,…
## $ ins_ohp_0m <dbl+lbl> NA, 0, 0, NA, NA, NA, NA, 1, NA, NA,…
## $ ins_medicare_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ ins_employer_0m <dbl+lbl> NA, 0, 1, NA, 0, NA, NA, 0, NA, 0,…
## $ ins_privpay_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ ins_othcov_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ ins_noins_0m <dbl+lbl> NA, 1, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ ins_months_0m <dbl+lbl> NA, 0, 3, NA, 0, NA, NA, 0, NA, 0,…
## $ usual_place_0m <dbl+lbl> NA, 1, 1, NA, 0, NA, NA, 1, NA, 0,…
## $ usual_care_0m <dbl+lbl> NA, 2, 1, NA, NA, NA, NA, 4, NA, NA,…
## $ usual_clinic_0m <dbl+lbl> NA, 1, 1, NA, 0, NA, NA, 0, NA, 0,…
## $ need_med_0m <dbl+lbl> NA, 1, 1, NA, 1, NA, NA, 1, NA, 1,…
## $ needmet_qn_med_0m <dbl+lbl> NA, 1, 2, NA, 2, NA, NA, 1, NA, 2,…
## $ needmet_med_0m <dbl+lbl> NA, 1, 0, NA, 0, NA, NA, 1, NA, 0,…
## $ reason_care_cost_0m <dbl+lbl> NA, NA, 0, NA, 1, NA, NA, NA, NA, 1,…
## $ reason_care_ins_0m <dbl+lbl> NA, NA, 1, NA, 0, NA, NA, NA, NA, 1,…
## $ reason_care_doc_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 1,…
## $ reason_care_owe_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 1,…
## $ reason_care_apt_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 1,…
## $ reason_care_closed_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 1,…
## $ reason_care_nodoc_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 1,…
## $ reason_care_other_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 1,…
## $ reason_care_dont_0m <dbl+lbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 0,…
## $ need_rx_0m <dbl+lbl> NA, 1, 1, NA, 0, NA, NA, 0, NA, 0,…
## $ needmet_qn_rx_0m <dbl+lbl> NA, 1, 1, NA, NA, NA, NA, NA, NA, NA,…
## $ needmet_rx_0m <dbl+lbl> NA, 1, 1, NA, 1, NA, NA, 1, NA, 1,…
## $ reason_rx_cost_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ reason_rx_ins_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ reason_rx_doc_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ reason_rx_get_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ reason_rx_pharm_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ reason_rx_other_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ reason_rx_dont_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ rx_num_mod_0m <dbl> NA, 2, 1, NA, 0, NA, NA, 0, NA, 0, NA, NA, …
## $ rx_any_0m <dbl+lbl> NA, 1, 1, NA, 0, NA, NA, 0, NA, 0,…
## $ need_dent_0m <dbl+lbl> NA, 1, 1, NA, 0, NA, NA, 1, NA, 1,…
## $ needmet_qn_dent_0m <dbl+lbl> NA, 1, 2, NA, NA, NA, NA, 1, NA, 2,…
## $ needmet_dent_0m <dbl+lbl> NA, 1, 0, NA, 1, NA, NA, 1, NA, 0,…
## $ doc_any_0m <dbl+lbl> NA, 1, 1, NA, 0, NA, NA, 1, NA, 0,…
## $ doc_num_mod_0m <dbl> NA, 3, 1, NA, 0, NA, NA, 1, NA, 0, NA, NA, …
## $ er_any_0m <dbl+lbl> NA, 0, 0, NA, 1, NA, NA, 0, NA, 1,…
## $ er_num_mod_0m <dbl> NA, 0, 0, NA, 1, NA, NA, 0, NA, 4, NA, NA, …
## $ er_noner_0m <dbl+lbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA,…
## $ reason_er_need_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_closed_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_apt_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_doc_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_copay_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_go_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_other_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_rx_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ reason_er_dont_0m <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0,…
## $ hosp_any_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ hosp_num_mod_0m <dbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0, NA, NA, …
## $ total_hosp_0m <dbl> NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ cost_rx_oop_0m <dbl> NA, 30, 10, NA, NA, NA, NA, 0, NA, NA, NA, …
## $ cost_tot_oop_0m <dbl> NA, 30.0, 190.0, NA, 0.0, NA, NA, 0.0, NA, …
## $ cost_any_oop_0m <dbl+lbl> NA, 1, 1, NA, 0, NA, NA, 0, NA, 1,…
## $ cost_borrow_0m <dbl+lbl> NA, 1, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ cost_any_owe_0m <dbl+lbl> NA, 1, 0, NA, 0, NA, NA, 0, NA, 1,…
## $ cost_tot_owe_0m <dbl> NA, 30, 0, NA, 0, NA, NA, 0, NA, 5000, NA, …
## $ cost_refused_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ health_gen_0m <dbl+lbl> NA, 4, 2, NA, NA, NA, NA, 3, NA, 5,…
## $ health_gen_bin_0m <dbl+lbl> NA, 0, 1, NA, NA, NA, NA, 0, NA, 0,…
## $ baddays_phys_0m <dbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 30, NA, NA,…
## $ baddays_ment_0m <dbl> NA, NA, 5, NA, 30, NA, NA, 0, NA, 30, NA, N…
## $ baddays_tot_0m <dbl> NA, 0, 5, NA, 0, NA, NA, 0, NA, 15, NA, NA,…
## $ health_chg_0m <dbl+lbl> NA, 2, 2, NA, 3, NA, NA, 2, NA, 1,…
## $ health_chg_bin_0m <dbl+lbl> NA, 0, 0, NA, 1, NA, NA, 0, NA, 0,…
## $ dia_dx_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ ast_dx_0m <dbl+lbl> NA, 1, 0, NA, 0, NA, NA, 0, NA, 1,…
## $ hbp_dx_0m <dbl+lbl> NA, 1, 0, NA, 1, NA, NA, 0, NA, 0,…
## $ emp_dx_0m <dbl+lbl> NA, 1, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ chf_dx_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ dep_dx_0m <dbl+lbl> NA, 1, 1, NA, 1, NA, NA, 0, NA, 1,…
## $ female_0m <dbl+lbl> NA, 0, 0, NA, 1, NA, NA, 1, NA, 1,…
## $ birthyear_0m <dbl> NA, 1968, 1977, NA, 1957, NA, NA, 1977, NA,…
## $ employ_0m <dbl+lbl> NA, 0, 0, NA, 1, NA, NA, 1, NA, 1,…
## $ employ_det_0m <dbl+lbl> NA, 3, 3, NA, 2, NA, NA, 1, NA, 2,…
## $ hhinc_cat_0m <dbl+lbl> NA, 1, 8, NA, 7, NA, NA, 2, NA, 1,…
## $ race_hisp_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 1, NA, 1,…
## $ race_white_0m <dbl+lbl> NA, 1, 1, NA, 1, NA, NA, 0, NA, 0,…
## $ race_black_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 1,…
## $ race_amerindian_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 1,…
## $ race_asian_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ race_pacific_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 0, NA, 0,…
## $ race_other_qn_0m <dbl+lbl> NA, 0, 0, NA, 0, NA, NA, 1, NA, 1,…
## $ employ_hrs_0m <dbl+lbl> NA, 1, 1, NA, 2, NA, NA, NA, NA, 4,…
## $ edu_0m <dbl+lbl> NA, 1, 3, NA, 1, NA, NA, 1, NA, 2,…
## $ living_arrange_0m <dbl+lbl> NA, 2, 4, NA, 1, NA, NA, 2, NA, 3,…
## $ hhsize_0m <dbl> NA, 2, 2, NA, NA, NA, NA, 8, NA, 2, NA, NA,…
## $ hhinc_pctfpl_0m <dbl> NA, 0.000, 111.531, NA, NA, NA, NA, 3.377, …
## $ num19_0m <dbl> NA, 0, 1, NA, NA, NA, NA, 6, NA, 0, NA, NA,…
## $ num19_hi_0m <dbl+lbl> NA, 3, 1, NA, NA, NA, NA, 1, NA, NA,…
## $ cost_tot_oop_correct_0m <dbl> NA, 180.0, 240.0, NA, 0.0, NA, NA, 0.0, NA,…
## $ cost_medical_oop_0m <dbl> NA, 0.0, 180.0, NA, 0.0, NA, NA, 0.0, NA, 2…
## $ sample_ed <dbl> 1, 1, 1, 1, NA, 1, NA, 1, 1, 1, NA, 1, 1, 1…
## $ any_visit_pre_ed <dbl+lbl> 0, 0, 1, 1, NA, 1, NA, 0, 0, 1,…
## $ any_visit_ed <dbl+lbl> 0, 1, 0, 1, NA, 0, NA, 0, 0, 1,…
## $ num_visit_pre_cens_ed <dbl> 0, 0, 1, 1, NA, 2, NA, 0, 0, 7, NA, 0, 0, 0…
## $ num_visit_cens_ed <dbl> 0, 2, 0, 5, NA, 0, NA, 0, 0, 5, NA, 0, 0, 0…
## $ any_hosp_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_hosp_ed <dbl+lbl> 0, 0, 0, 1, NA, 0, NA, 0, 0, 0,…
## $ num_hosp_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_hosp_cens_ed <dbl> 0, 0, 0, 1, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_out_pre_ed <dbl+lbl> 0, 0, 1, 1, NA, 1, NA, 0, 0, 1,…
## $ any_out_ed <dbl+lbl> 0, 1, 0, 1, NA, 0, NA, 0, 0, 1,…
## $ num_out_pre_cens_ed <dbl> 0, 0, 1, 1, NA, 2, NA, 0, 0, 7, NA, 0, 0, 0…
## $ num_out_cens_ed <dbl> 0, 2, 0, 4, NA, 0, NA, 0, 0, 5, NA, 0, 0, 0…
## $ any_on_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 1, NA, 0, 0, 1,…
## $ any_on_ed <dbl+lbl> 0, 1, 0, 1, NA, 0, NA, 0, 0, 1,…
## $ num_on_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 1, NA, 0, 0, 1, NA, 0, 0, 0…
## $ num_on_cens_ed <dbl> 0, 2, 0, 3, NA, 0, NA, 0, 0, 3, NA, 0, 0, 0…
## $ any_off_pre_ed <dbl+lbl> 0, 0, 1, 1, NA, 1, NA, 0, 0, 1,…
## $ any_off_ed <dbl+lbl> 0, 0, 0, 1, NA, 0, NA, 0, 0, 1,…
## $ num_off_pre_cens_ed <dbl> 0, 0, 1, 1, NA, 1, NA, 0, 0, 6, NA, 0, 0, 0…
## $ num_off_cens_ed <dbl> 0, 0, 0, 2, NA, 0, NA, 0, 0, 2, NA, 0, 0, 0…
## $ num_edcnnp_pre_ed <dbl> 0.0000, 0.0000, 0.5000, 0.3303, NA, 0.0000,…
## $ num_edcnnp_ed <dbl> 0.0000, 0.3303, 0.0000, 0.5417, NA, 0.0000,…
## $ num_edcnpa_pre_ed <dbl> 0.00000, 0.00000, 0.00000, 0.00000, NA, 0.2…
## $ num_edcnpa_ed <dbl> 0.0000, 0.9811, 0.0000, 0.1774, NA, 0.0000,…
## $ num_epct_pre_ed <dbl> 0.0000, 0.0000, 0.5000, 0.6697, NA, 0.8612,…
## $ num_epct_ed <dbl> 0.0000, 0.6886, 0.0000, 1.5726, NA, 0.0000,…
## $ num_ne_pre_ed <dbl> 0.0000, 0.0000, 0.0000, 0.0000, NA, 0.8974,…
## $ num_ne_ed <dbl> 0.000, 0.000, 0.000, 1.708, NA, 0.000, NA, …
## $ num_unclas_pre_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 1, NA, 0, 0, 0…
## $ num_unclas_ed <dbl> 0, 0, 0, 1, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_acsc_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_acsc_ed <dbl+lbl> 0, 1, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_acsc_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_acsc_cens_ed <dbl> 0, 1, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_chron_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 1,…
## $ any_chron_ed <dbl+lbl> 0, 1, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_chron_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 1, NA, 0, 0, 0…
## $ num_chron_cens_ed <dbl> 0, 1, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_inj_pre_ed <dbl+lbl> 0, 0, 1, 0, NA, 0, NA, 0, 0, 0,…
## $ any_inj_ed <dbl+lbl> 0, 0, 0, 1, NA, 0, NA, 0, 0, 1,…
## $ num_inj_pre_cens_ed <dbl> 0, 0, 1, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_inj_cens_ed <dbl> 0, 1, 0, 3, NA, 0, NA, 0, 0, 6, NA, 0, 0, 0…
## $ any_skin_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_skin_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 1,…
## $ num_skin_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_skin_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 1, NA, 0, 0, 0…
## $ any_abdo_pre_ed <dbl+lbl> 0, 0, 0, 1, NA, 0, NA, 0, 0, 0,…
## $ any_abdo_ed <dbl+lbl> 0, 1, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_abdo_pre_cens_ed <dbl> 0, 0, 0, 1, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_abdo_cens_ed <dbl> 0, 1, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_back_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_back_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_back_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_back_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_heart_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_heart_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_heart_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_heart_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_head_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_head_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_head_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_head_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_depres_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_depres_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_depres_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_depres_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_psysub_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ any_psysub_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0,…
## $ num_psysub_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ num_psysub_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA, 0, 0, 0…
## $ any_mail_match_ed <dbl> 0, 0, 0, NA, NA, NA, NA, 0, NA, 1, NA, NA, …
## $ num_mail_match_ed <dbl> 0, 0, 0, NA, NA, NA, NA, 0, NA, 2, NA, NA, …
## $ any_inp_match_ed <dbl> 0, 1, NA, 1, NA, 0, NA, 0, 0, 1, NA, NA, NA…
## $ num_inp_match_ed <dbl> 0, 1, NA, 2, NA, 0, NA, 0, 0, 7, NA, NA, NA…
## $ charg_tot_pre_ed <dbl> 0.0, 0.0, 789.5, 1888.2, NA, 1715.3, NA, 0.…
## $ charg_tot_ed <dbl> 0, 2751, 0, 15233, NA, 0, NA, 0, 0, 8436, N…
## $ ed_charg_tot_pre_ed <dbl> 0.0, 0.0, 789.5, 1888.2, NA, 1006.3, NA, 0.…
## $ ed_charg_tot_ed <dbl> 0, 2751, 0, 7101, NA, 0, NA, 0, 0, 7067, NA…
## $ any_hiun_pre_ed <dbl+lbl> 0, 0, 1, 1, NA, 0, NA, 0, 0, 1,…
## $ any_hiun_ed <dbl+lbl> 0, 1, 0, 1, NA, 0, NA, 0, 0, 1,…
## $ num_hiun_pre_cens_ed <dbl> 0, 0, 1, 1, NA, 0, NA, 0, 0, 2, NA, 0, 0, 0…
## $ num_hiun_cens_ed <dbl> 0, 2, 0, 5, NA, 0, NA, 0, 0, 3, NA, 0, 0, 0…
## $ any_loun_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 1, NA, 0, 0, 1,…
## $ any_loun_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 1,…
## $ num_loun_pre_cens_ed <dbl> 0, 0, 0, 0, NA, 2, NA, 0, 0, 5, NA, 0, 0, 0…
## $ num_loun_cens_ed <dbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 2, NA, 0, 0, 0…
covariates <- c("person_id", "treatment", "ohp_all_ever_inperson", "numhh_list", "gender_inp", "age_inp",
"hispanic_inp", "race_white_inp", "race_black_inp", "race_nwother_inp", "ast_dx_pre_lottery",
"dia_dx_pre_lottery", "hbp_dx_pre_lottery", "chl_dx_pre_lottery", "ami_dx_pre_lottery",
"chf_dx_pre_lottery", "emp_dx_pre_lottery", "kid_dx_pre_lottery", "cancer_dx_pre_lottery",
"dep_dx_pre_lottery", "charg_tot_pre_ed", "ed_charg_tot_pre_ed", "num_visit_pre_cens_ed", "any_depres_pre_ed",
"household_id", "edu_inp")
weights <- c("weight_total_inp")
outcomes <- c("bp_sar_inp", "bp_dar_inp", "hbp_dx_post_lottery", "hbp_diure_med_inp", "chl_inp", "hdl_inp",
"chl_dx_post_lottery", "antihyperlip_med_inp", "a1c_inp", "dia_dx_post_lottery", "diabetes_med_inp",
"bmi_inp", "phqtot_inp", "dep_dx_post_lottery", "antidep_med_inp", "cvd_risk_point",
"owe_inp", "borrow_inp", "catastrophic_exp_inp", "doc_num_mod_inp", "charg_tot_ed", "ed_charg_tot_ed",
"doc_any_incl_probe_inp", "ed_any_incl_probe_inp",
"hosp_num_mod_inp_2", "hosp_any_incl_probe_inp", "rx_num_mod_inp", "rx_any_mod_inp", "any_oop_spending", "ed_num_mod_inp")
# collect only the covariates and outcomes that we care about for this study
d.gf <- d[, which(colnames(d) %in%
c(covariates, # All covariates
weights, # Weights
outcomes # All outcomes
))]
print("Information on data restricted to variables of interest:")
## [1] "Information on data restricted to variables of interest:"
## Rows: 20,745
## Columns: 57
## $ person_id <dbl> 5, 8, 9, 16, 17, 18, 19, 23, 24, 29, 33, 34, 3…
## $ weight_total_inp <dbl> 1.1504, 0.8975, 0.0000, 1.0000, 1.2126, 1.0000…
## $ gender_inp <dbl+lbl> 1, 0, NA, 1, 0, 0, NA, 1, 0, 0, NA…
## $ age_inp <dbl> 60, 41, NA, 39, 52, 51, NA, 32, 34, 23, NA, NA…
## $ ast_dx_pre_lottery <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 1, NA…
## $ dia_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ hbp_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 0, NA…
## $ chl_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ ami_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ chf_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ emp_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ kid_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ cancer_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ dep_dx_pre_lottery <dbl+lbl> 0, 0, NA, 0, 1, 0, NA, 0, 0, 1, NA…
## $ dia_dx_post_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ hbp_dx_post_lottery <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ chl_dx_post_lottery <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ dep_dx_post_lottery <dbl+lbl> 0, 0, NA, NA, 0, 0, NA, 0, 0, 0, NA…
## $ phqtot_inp <dbl> 1, 9, NA, 2, 13, 2, NA, 3, 2, 14, NA, NA, NA, …
## $ cvd_risk_point <dbl> 0.137, 0.112, NA, 0.033, 0.253, 0.156, NA, 0.0…
## $ doc_any_incl_probe_inp <dbl+lbl> 0, 1, NA, 1, 0, 0, NA, 1, 0, 1, NA…
## $ ed_any_incl_probe_inp <dbl+lbl> 0, 1, NA, 1, 1, 0, NA, 0, 0, 1, NA…
## $ hosp_any_incl_probe_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ any_oop_spending <dbl+lbl> 0, 1, NA, 0, 1, 0, NA, 0, 0, 1, NA…
## $ catastrophic_exp_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ owe_inp <dbl+lbl> 0, 1, NA, 0, 1, 0, NA, 1, 0, 1, NA…
## $ borrow_inp <dbl+lbl> 0, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ edu_inp <dbl+lbl> 2, 2, NA, 2, 1, 3, NA, 1, 1, 1, NA…
## $ hispanic_inp <dbl+lbl> 1, 0, NA, 0, 0, 0, NA, 1, 1, 0, NA…
## $ race_white_inp <dbl+lbl> 0, 1, NA, 1, 1, 0, NA, 0, 1, 0, NA…
## $ race_black_inp <dbl+lbl> 0, 0, NA, 0, 0, 1, NA, 0, 0, 1, NA…
## $ race_nwother_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 1, NA…
## $ a1c_inp <dbl> 5.037, 5.201, NA, 5.854, 5.364, 5.527, NA, 5.0…
## $ hdl_inp <dbl> 48.33, 51.33, NA, 38.58, 51.33, 28.08, NA, 31.…
## $ chl_inp <dbl> 241.0, 229.9, NA, 229.9, 235.4, 177.7, NA, 173…
## $ bmi_inp <dbl> 26.66, 35.23, NA, 37.12, 24.81, 27.02, NA, 26.…
## $ bp_sar_inp <dbl> 144, 134, NA, 126, 168, 119, NA, 98, 108, 125,…
## $ bp_dar_inp <dbl> 81, 82, NA, 94, 110, 79, NA, 59, 63, 76, NA, N…
## $ rx_any_mod_inp <dbl+lbl> 0, 1, NA, 1, 1, 0, NA, 0, 0, 1, NA…
## $ rx_num_mod_inp <dbl> 0, 2, NA, 2, NA, 0, NA, 0, 0, 3, NA, NA, NA, 0…
## $ hbp_diure_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ antihyperlip_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ diabetes_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ antidep_med_inp <dbl+lbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA…
## $ household_id <dbl> 100005, 102094, 100009, 140688, 100017, 100018…
## $ treatment <dbl+lbl> 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, …
## $ numhh_list <dbl+lbl> 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, …
## $ ohp_all_ever_inperson <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ doc_num_mod_inp <dbl> 0, 6, NA, 12, 0, 0, NA, 5, 0, 5, NA, NA, NA, 1…
## $ ed_num_mod_inp <dbl> 0, 2, NA, 1, 1, 0, NA, 0, 0, 10, NA, NA, NA, 2…
## $ hosp_num_mod_inp_2 <dbl> 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, NA, NA, NA, 0,…
## $ num_visit_pre_cens_ed <dbl> 0, 0, 1, 1, NA, 2, NA, 0, 0, 7, NA, 0, 0, 0, 0…
## $ any_depres_pre_ed <dbl+lbl> 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, NA…
## $ charg_tot_pre_ed <dbl> 0.0, 0.0, 789.5, 1888.2, NA, 1715.3, NA, 0.0, …
## $ charg_tot_ed <dbl> 0, 2751, 0, 15233, NA, 0, NA, 0, 0, 8436, NA, …
## $ ed_charg_tot_pre_ed <dbl> 0.0, 0.0, 789.5, 1888.2, NA, 1006.3, NA, 0.0, …
## $ ed_charg_tot_ed <dbl> 0, 2751, 0, 7101, NA, 0, NA, 0, 0, 7067, NA, 0…
## [1] "Missing values in columns:"
## person_id weight_total_inp gender_inp
## 0 0 8516
## age_inp ast_dx_pre_lottery dia_dx_pre_lottery
## 8517 8516 8516
## hbp_dx_pre_lottery chl_dx_pre_lottery ami_dx_pre_lottery
## 8516 8516 8516
## chf_dx_pre_lottery emp_dx_pre_lottery kid_dx_pre_lottery
## 8516 8516 8516
## cancer_dx_pre_lottery dep_dx_pre_lottery dia_dx_post_lottery
## 8516 8516 8559
## hbp_dx_post_lottery chl_dx_post_lottery dep_dx_post_lottery
## 8800 8851 8650
## phqtot_inp cvd_risk_point doc_any_incl_probe_inp
## 8584 11324 8540
## ed_any_incl_probe_inp hosp_any_incl_probe_inp any_oop_spending
## 8541 8540 8551
## catastrophic_exp_inp owe_inp borrow_inp
## 8950 8637 8533
## edu_inp hispanic_inp race_white_inp
## 8527 8545 8555
## race_black_inp race_nwother_inp a1c_inp
## 8555 8555 8605
## hdl_inp chl_inp bmi_inp
## 8573 8571 8570
## bp_sar_inp bp_dar_inp rx_any_mod_inp
## 8557 8557 8519
## rx_num_mod_inp hbp_diure_med_inp antihyperlip_med_inp
## 8833 8516 8516
## diabetes_med_inp antidep_med_inp household_id
## 8516 8516 0
## treatment numhh_list ohp_all_ever_inperson
## 0 0 0
## doc_num_mod_inp ed_num_mod_inp hosp_num_mod_inp_2
## 8587 8570 8570
## num_visit_pre_cens_ed any_depres_pre_ed charg_tot_pre_ed
## 3661 3653 3659
## charg_tot_ed ed_charg_tot_pre_ed ed_charg_tot_ed
## 3663 3664 3667
Remove observations with missing values for age_inp, gender_inp, all race variables, treatment, and edu_inp.
## [1] "Dimensions of newd:"
## [1] 20745 57
## [1] "Summary stats of age_inp before removing missing:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 19 31 41 41 50 71 8517
## [1] "Dimensions after removing missing age_inp:"
## [1] 12228 57
## [1] "Summary stats of age_inp after removing missing:"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 19.0 31.0 41.0 40.8 50.0 71.0
## [1] "Summary stats of gender_inp before removing transgender individuals:"
##
## 0 1 2
## 5312 6915 1
## [1] "Dimensions after removing transgender individuals:"
## [1] 12227 57
## [1] "Summary stats of gender_inp after removing transgender individuals:"
##
## 0 1
## 5312 6915
## [1] "Summary stats of race variables before removing observations missing all race variables:"
## [1] "hispanic_inp:"
##
## 0 1 <NA>
## 10001 2197 29
## [1] "race_white_inp:"
##
## 0 1 <NA>
## 3800 8388 39
## [1] "race_black_inp:"
##
## 0 1 <NA>
## 10929 1259 39
## [1] "race_nwother_inp:"
##
## 0 1 <NA>
## 10430 1758 39
## [1] "Dimensions after removing observations missing all race variables:"
## [1] 12211 57
## [1] "Summary stats of race variables after removing observations missing all race variables:"
## [1] "hispanic_inp:"
##
## 0 1 <NA>
## 10001 2197 13
## [1] "race_white_inp:"
##
## 0 1 <NA>
## 3800 8388 23
## [1] "race_black_inp:"
##
## 0 1 <NA>
## 10929 1259 23
## [1] "race_nwother_inp:"
##
## 0 1 <NA>
## 10430 1758 23
# Replace missing values of all race variables with 0
newd$hispanic_inp[is.na(newd$hispanic_inp)] <- 0
newd$race_white_inp[is.na(newd$race_white_inp)] <- 0
newd$race_black_inp[is.na(newd$race_black_inp)] <- 0
newd$race_nwother_inp[is.na(newd$race_nwother_inp)] <- 0
print("Dimensions after replacing missing race variables with 0:")
## [1] "Dimensions after replacing missing race variables with 0:"
## [1] 12211 57
## [1] "Summary stats of race variables after replacing missing race variables with 0:"
## [1] "hispanic_inp:"
##
## 0 1
## 10014 2197
## [1] "race_white_inp:"
##
## 0 1
## 3823 8388
## [1] "race_black_inp:"
##
## 0 1
## 10952 1259
## [1] "race_nwother_inp:"
##
## 0 1
## 10453 1758
## [1] "Summary stats of treatment before removing missing:"
##
## 0 1
## 5836 6375
## [1] "Dimensions after removing missing treatment:"
## [1] 12211 57
## [1] "Summary stats of treatment after removing missing:"
##
## 0 1
## 5836 6375
## [1] "Summary stats of edu_inp before removing missing:"
##
## 1 2 3 4 <NA>
## 2503 5545 2772 1388 3
## [1] "Dimensions after removing missing edu_inp:"
## [1] 12208 57
## [1] "Summary stats of edu_inp after removing missing:"
##
## 1 2 3 4
## 2503 5545 2772 1388
## person_id weight_total_inp gender_inp
## 0 0 0
## age_inp ast_dx_pre_lottery dia_dx_pre_lottery
## 0 0 0
## hbp_dx_pre_lottery chl_dx_pre_lottery ami_dx_pre_lottery
## 0 0 0
## chf_dx_pre_lottery emp_dx_pre_lottery kid_dx_pre_lottery
## 0 0 0
## cancer_dx_pre_lottery dep_dx_pre_lottery dia_dx_post_lottery
## 0 0 41
## hbp_dx_post_lottery chl_dx_post_lottery dep_dx_post_lottery
## 281 332 131
## phqtot_inp cvd_risk_point doc_any_incl_probe_inp
## 64 2801 17
## ed_any_incl_probe_inp hosp_any_incl_probe_inp any_oop_spending
## 18 17 28
## catastrophic_exp_inp owe_inp borrow_inp
## 424 114 10
## edu_inp hispanic_inp race_white_inp
## 0 0 0
## race_black_inp race_nwother_inp a1c_inp
## 0 0 89
## hdl_inp chl_inp bmi_inp
## 57 55 54
## bp_sar_inp bp_dar_inp rx_any_mod_inp
## 41 41 2
## rx_num_mod_inp hbp_diure_med_inp antihyperlip_med_inp
## 311 0 0
## diabetes_med_inp antidep_med_inp household_id
## 0 0 0
## treatment numhh_list ohp_all_ever_inperson
## 0 0 0
## doc_num_mod_inp ed_num_mod_inp hosp_num_mod_inp_2
## 64 47 47
## num_visit_pre_cens_ed any_depres_pre_ed charg_tot_pre_ed
## 2055 2050 2053
## charg_tot_ed ed_charg_tot_pre_ed ed_charg_tot_ed
## 2055 2058 2056
Create lessHS and HSorGED variables. Individuals equal to 0 for both of these variables are “3” or “4” for edu_inp.
## [1] "Summary statistics for HS or less:"
##
## 0 1
## 9705 2503
## [1] "Summary statistics for At least HS/GED:"
##
## 0 1
## 6663 5545
## [1] "Dimensions of newd after creating new variables:"
## [1] 12208 59
## [1] "Dimensions of newd_adjusted after dropping edu_inp:"
## [1] 12208 58
## [1] "Information on newd_adjusted:"
## Rows: 12,208
## Columns: 58
## $ person_id <dbl> 5, 8, 16, 17, 18, 23, 24, 29, 47, 57, 59, 68, …
## $ weight_total_inp <dbl> 1.1504, 0.8975, 1.0000, 1.2126, 1.0000, 1.0033…
## $ gender_inp <dbl+lbl> 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, …
## $ age_inp <dbl> 60, 41, 39, 52, 51, 32, 34, 23, 43, 46, 38, 25…
## $ ast_dx_pre_lottery <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, …
## $ dia_dx_pre_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ hbp_dx_pre_lottery <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, …
## $ chl_dx_pre_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ami_dx_pre_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ chf_dx_pre_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ emp_dx_pre_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ kid_dx_pre_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cancer_dx_pre_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ dep_dx_pre_lottery <dbl+lbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, …
## $ dia_dx_post_lottery <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ hbp_dx_post_lottery <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ chl_dx_post_lottery <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ dep_dx_post_lottery <dbl+lbl> 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0…
## $ phqtot_inp <dbl> 1, 9, 2, 13, 2, 3, 2, 14, 11, 8, 7, 3, 2, 0, 2…
## $ cvd_risk_point <dbl> 0.1370, 0.1120, 0.0330, 0.2530, 0.1560, 0.0120…
## $ doc_any_incl_probe_inp <dbl+lbl> 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, …
## $ ed_any_incl_probe_inp <dbl+lbl> 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, …
## $ hosp_any_incl_probe_inp <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ any_oop_spending <dbl+lbl> 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, …
## $ catastrophic_exp_inp <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ owe_inp <dbl+lbl> 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, …
## $ borrow_inp <dbl+lbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, …
## $ hispanic_inp <dbl+lbl> 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ race_white_inp <dbl+lbl> 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, …
## $ race_black_inp <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, …
## $ race_nwother_inp <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ a1c_inp <dbl> 5.037, 5.201, 5.854, 5.364, 5.527, 5.037, 5.44…
## $ hdl_inp <dbl> 48.33, 51.33, 38.58, 51.33, 28.08, 31.08, 25.8…
## $ chl_inp <dbl> 241.0, 229.9, 229.9, 235.4, 177.7, 173.8, 152.…
## $ bmi_inp <dbl> 26.66, 35.23, 37.12, 24.81, 27.02, 26.26, 27.7…
## $ bp_sar_inp <dbl> 144, 134, 126, 168, 119, 98, 108, 125, 100, 10…
## $ bp_dar_inp <dbl> 81, 82, 94, 110, 79, 59, 63, 76, 77, 63, 84, 6…
## $ rx_any_mod_inp <dbl+lbl> 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, …
## $ rx_num_mod_inp <dbl> 0, 2, 2, NA, 0, 0, 0, 3, 0, 3, 4, 0, 4, 2, 1, …
## $ hbp_diure_med_inp <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, …
## $ antihyperlip_med_inp <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ diabetes_med_inp <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ antidep_med_inp <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ household_id <dbl> 100005, 102094, 140688, 100017, 100018, 115253…
## $ treatment <dbl+lbl> 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, …
## $ numhh_list <dbl+lbl> 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, …
## $ ohp_all_ever_inperson <dbl+lbl> 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, …
## $ doc_num_mod_inp <dbl> 0, 6, 12, 0, 0, 5, 0, 5, 1, 12, 6, 0, 3, 0, 10…
## $ ed_num_mod_inp <dbl> 0, 2, 1, 1, 0, 0, 0, 10, 2, 6, 0, 2, 0, 0, 0, …
## $ hosp_num_mod_inp_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2…
## $ num_visit_pre_cens_ed <dbl> 0, 0, 1, NA, 2, 0, 0, 7, 0, NA, 0, 0, 1, 0, 0,…
## $ any_depres_pre_ed <dbl+lbl> 0, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0…
## $ charg_tot_pre_ed <dbl> 0.0, 0.0, 1888.2, NA, 1715.3, 0.0, 0.0, 5743.9…
## $ charg_tot_ed <dbl> 0, 2751, 15233, NA, 0, 0, 0, 8436, 0, NA, 0, 0…
## $ ed_charg_tot_pre_ed <dbl> 0.0, 0.0, 1888.2, NA, 1006.3, 0.0, 0.0, 4542.4…
## $ ed_charg_tot_ed <dbl> 0.0, 2751.4, 7100.8, NA, 0.0, 0.0, 0.0, 7067.0…
## $ lessHS <dbl> 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1…
## $ HSorGED <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0…
## [1] "Missing values in columns:"
## person_id weight_total_inp gender_inp
## 0 0 0
## age_inp ast_dx_pre_lottery dia_dx_pre_lottery
## 0 0 0
## hbp_dx_pre_lottery chl_dx_pre_lottery ami_dx_pre_lottery
## 0 0 0
## chf_dx_pre_lottery emp_dx_pre_lottery kid_dx_pre_lottery
## 0 0 0
## cancer_dx_pre_lottery dep_dx_pre_lottery dia_dx_post_lottery
## 0 0 41
## hbp_dx_post_lottery chl_dx_post_lottery dep_dx_post_lottery
## 281 332 131
## phqtot_inp cvd_risk_point doc_any_incl_probe_inp
## 64 2801 17
## ed_any_incl_probe_inp hosp_any_incl_probe_inp any_oop_spending
## 18 17 28
## catastrophic_exp_inp owe_inp borrow_inp
## 424 114 10
## hispanic_inp race_white_inp race_black_inp
## 0 0 0
## race_nwother_inp a1c_inp hdl_inp
## 0 89 57
## chl_inp bmi_inp bp_sar_inp
## 55 54 41
## bp_dar_inp rx_any_mod_inp rx_num_mod_inp
## 41 2 311
## hbp_diure_med_inp antihyperlip_med_inp diabetes_med_inp
## 0 0 0
## antidep_med_inp household_id treatment
## 0 0 0
## numhh_list ohp_all_ever_inperson doc_num_mod_inp
## 0 0 64
## ed_num_mod_inp hosp_num_mod_inp_2 num_visit_pre_cens_ed
## 47 47 2055
## any_depres_pre_ed charg_tot_pre_ed charg_tot_ed
## 2050 2053 2055
## ed_charg_tot_pre_ed ed_charg_tot_ed lessHS
## 2058 2056 0
## HSorGED
## 0
Convert all columns to numeric, then convert numhh_list to factor.
#####STEP 1-4: Convert types #####
# Convert all columns to numeric
i <- c(1:ncol(newd_adjusted))
newd_adjusted[, i] <- apply(newd_adjusted[, i], 2,
function(x) as.numeric(as.character(x)))
# Convert numhh_list to factor
newd_adjusted$numhh_list <- as.factor(newd_adjusted$numhh_list)
# Check the structure after type conversion
print("Information on newd_adjusted after converting to numeric (factor for numhh_list):")
## [1] "Information on newd_adjusted after converting to numeric (factor for numhh_list):"
## Rows: 12,208
## Columns: 58
## $ person_id <dbl> 5, 8, 16, 17, 18, 23, 24, 29, 47, 57, 59, 68, …
## $ weight_total_inp <dbl> 1.1504, 0.8975, 1.0000, 1.2126, 1.0000, 1.0033…
## $ gender_inp <dbl> 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1…
## $ age_inp <dbl> 60, 41, 39, 52, 51, 32, 34, 23, 43, 46, 38, 25…
## $ ast_dx_pre_lottery <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0…
## $ dia_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ hbp_dx_pre_lottery <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0…
## $ chl_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ ami_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ chf_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ emp_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ kid_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ cancer_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1…
## $ dep_dx_pre_lottery <dbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1…
## $ dia_dx_post_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ hbp_dx_post_lottery <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ chl_dx_post_lottery <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ dep_dx_post_lottery <dbl> 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ phqtot_inp <dbl> 1, 9, 2, 13, 2, 3, 2, 14, 11, 8, 7, 3, 2, 0, 2…
## $ cvd_risk_point <dbl> 0.1370, 0.1120, 0.0330, 0.2530, 0.1560, 0.0120…
## $ doc_any_incl_probe_inp <dbl> 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1…
## $ ed_any_incl_probe_inp <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0…
## $ hosp_any_incl_probe_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ any_oop_spending <dbl> 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1…
## $ catastrophic_exp_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ owe_inp <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1…
## $ borrow_inp <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0…
## $ hispanic_inp <dbl> 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ race_white_inp <dbl> 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0…
## $ race_black_inp <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0…
## $ race_nwother_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ a1c_inp <dbl> 5.037, 5.201, 5.854, 5.364, 5.527, 5.037, 5.44…
## $ hdl_inp <dbl> 48.33, 51.33, 38.58, 51.33, 28.08, 31.08, 25.8…
## $ chl_inp <dbl> 241.0, 229.9, 229.9, 235.4, 177.7, 173.8, 152.…
## $ bmi_inp <dbl> 26.66, 35.23, 37.12, 24.81, 27.02, 26.26, 27.7…
## $ bp_sar_inp <dbl> 144, 134, 126, 168, 119, 98, 108, 125, 100, 10…
## $ bp_dar_inp <dbl> 81, 82, 94, 110, 79, 59, 63, 76, 77, 63, 84, 6…
## $ rx_any_mod_inp <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1…
## $ rx_num_mod_inp <dbl> 0, 2, 2, NA, 0, 0, 0, 3, 0, 3, 4, 0, 4, 2, 1, …
## $ hbp_diure_med_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0…
## $ antihyperlip_med_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ diabetes_med_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ antidep_med_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1…
## $ household_id <dbl> 100005, 102094, 140688, 100017, 100018, 115253…
## $ treatment <dbl> 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0…
## $ numhh_list <fct> 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ ohp_all_ever_inperson <dbl> 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1…
## $ doc_num_mod_inp <dbl> 0, 6, 12, 0, 0, 5, 0, 5, 1, 12, 6, 0, 3, 0, 10…
## $ ed_num_mod_inp <dbl> 0, 2, 1, 1, 0, 0, 0, 10, 2, 6, 0, 2, 0, 0, 0, …
## $ hosp_num_mod_inp_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2…
## $ num_visit_pre_cens_ed <dbl> 0, 0, 1, NA, 2, 0, 0, 7, 0, NA, 0, 0, 1, 0, 0,…
## $ any_depres_pre_ed <dbl> 0, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0,…
## $ charg_tot_pre_ed <dbl> 0.0, 0.0, 1888.2, NA, 1715.3, 0.0, 0.0, 5743.9…
## $ charg_tot_ed <dbl> 0, 2751, 15233, NA, 0, 0, 0, 8436, 0, NA, 0, 0…
## $ ed_charg_tot_pre_ed <dbl> 0.0, 0.0, 1888.2, NA, 1006.3, 0.0, 0.0, 4542.4…
## $ ed_charg_tot_ed <dbl> 0.0, 2751.4, 7100.8, NA, 0.0, 0.0, 0.0, 7067.0…
## $ lessHS <dbl> 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1…
## $ HSorGED <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0…
## [1] "Missing values in columns:"
## person_id weight_total_inp gender_inp
## 0 0 0
## age_inp ast_dx_pre_lottery dia_dx_pre_lottery
## 0 0 0
## hbp_dx_pre_lottery chl_dx_pre_lottery ami_dx_pre_lottery
## 0 0 0
## chf_dx_pre_lottery emp_dx_pre_lottery kid_dx_pre_lottery
## 0 0 0
## cancer_dx_pre_lottery dep_dx_pre_lottery dia_dx_post_lottery
## 0 0 41
## hbp_dx_post_lottery chl_dx_post_lottery dep_dx_post_lottery
## 281 332 131
## phqtot_inp cvd_risk_point doc_any_incl_probe_inp
## 64 2801 17
## ed_any_incl_probe_inp hosp_any_incl_probe_inp any_oop_spending
## 18 17 28
## catastrophic_exp_inp owe_inp borrow_inp
## 424 114 10
## hispanic_inp race_white_inp race_black_inp
## 0 0 0
## race_nwother_inp a1c_inp hdl_inp
## 0 89 57
## chl_inp bmi_inp bp_sar_inp
## 55 54 41
## bp_dar_inp rx_any_mod_inp rx_num_mod_inp
## 41 2 311
## hbp_diure_med_inp antihyperlip_med_inp diabetes_med_inp
## 0 0 0
## antidep_med_inp household_id treatment
## 0 0 0
## numhh_list ohp_all_ever_inperson doc_num_mod_inp
## 0 0 64
## ed_num_mod_inp hosp_num_mod_inp_2 num_visit_pre_cens_ed
## 47 47 2055
## any_depres_pre_ed charg_tot_pre_ed charg_tot_ed
## 2050 2053 2055
## ed_charg_tot_pre_ed ed_charg_tot_ed lessHS
## 2058 2056 0
## HSorGED
## 0
#####STEP 1-5: Rename outcome variables #####
newd_adjusted <- newd_adjusted %>%
rename(
sbp = bp_sar_inp,
dbp = bp_dar_inp,
hypertension = hbp_dx_post_lottery,
hypertension_med = hbp_diure_med_inp,
chl_level = chl_inp,
hdl_level = hdl_inp,
highcholesterol = chl_dx_post_lottery,
cholesterol_med = antihyperlip_med_inp,
a1c = a1c_inp,
diabetes = dia_dx_post_lottery,
diabetes_med = diabetes_med_inp,
bmi = bmi_inp,
phq = phqtot_inp,
depression = dep_dx_post_lottery,
depression_med = antidep_med_inp,
cvd_risk = cvd_risk_point,
oop_spend = any_oop_spending,
debt = owe_inp,
borrow = borrow_inp,
catastrophic = catastrophic_exp_inp,
doc_num = doc_num_mod_inp,
doc_any = doc_any_incl_probe_inp,
ed_num = ed_num_mod_inp,
ed_any = ed_any_incl_probe_inp,
hosp_num = hosp_num_mod_inp_2,
hosp_any = hosp_any_incl_probe_inp,
prescriptions = rx_num_mod_inp,
prescriptions_any = rx_any_mod_inp
)
# Print dimensions and column names
print(sprintf("Dimentions of newd_adjusted: %d rows, %d columns",
dim(newd_adjusted)[1], dim(newd_adjusted)[2]))
## [1] "Dimentions of newd_adjusted: 12208 rows, 58 columns"
## [1] "Column names of newd_adjusted:"
## [1] "person_id" "weight_total_inp" "gender_inp"
## [4] "age_inp" "ast_dx_pre_lottery" "dia_dx_pre_lottery"
## [7] "hbp_dx_pre_lottery" "chl_dx_pre_lottery" "ami_dx_pre_lottery"
## [10] "chf_dx_pre_lottery" "emp_dx_pre_lottery" "kid_dx_pre_lottery"
## [13] "cancer_dx_pre_lottery" "dep_dx_pre_lottery" "diabetes"
## [16] "hypertension" "highcholesterol" "depression"
## [19] "phq" "cvd_risk" "doc_any"
## [22] "ed_any" "hosp_any" "oop_spend"
## [25] "catastrophic" "debt" "borrow"
## [28] "hispanic_inp" "race_white_inp" "race_black_inp"
## [31] "race_nwother_inp" "a1c" "hdl_level"
## [34] "chl_level" "bmi" "sbp"
## [37] "dbp" "prescriptions_any" "prescriptions"
## [40] "hypertension_med" "cholesterol_med" "diabetes_med"
## [43] "depression_med" "household_id" "treatment"
## [46] "numhh_list" "ohp_all_ever_inperson" "doc_num"
## [49] "ed_num" "hosp_num" "num_visit_pre_cens_ed"
## [52] "any_depres_pre_ed" "charg_tot_pre_ed" "charg_tot_ed"
## [55] "ed_charg_tot_pre_ed" "ed_charg_tot_ed" "lessHS"
## [58] "HSorGED"
#####STEP 1-7: Add negative treatment effects #####
# I only created negative values for objective measurements and costs where lower is always better.
newd_adjusted <- newd_adjusted %>%
mutate(
sbp_neg = -sbp,
dbp_neg = -dbp,
chl_level_neg = -chl_level,
hdl_level_neg = -hdl_level,
a1c_neg = - a1c,
bmi_neg = -bmi,
phq_neg = -phq,
cvd_risk_neg = -cvd_risk,
debt_neg = 1 - debt,
borrow_neg = 1 - borrow,
catastrophic_neg = 1 - catastrophic
)
# Print summary statistics of original and negative outcome variables for mutated variables
print("Summary statistics of original and negative outcome variables for mutated variables:")
## [1] "Summary statistics of original and negative outcome variables for mutated variables:"
## [1] "Systolic blood pressure original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 72 108 117 119 128 229 41
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -229 -128 -117 -119 -108 -72 41
## [1] "Diastolic blood pressure original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 39.0 67.0 75.0 75.8 83.0 158.0 41
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -158.0 -83.0 -75.0 -75.8 -67.0 -39.0 41
## [1] "Total cholesterol original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 117 182 203 205 226 488 55
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -488 -226 -203 -205 -182 -117 55
## [1] "HDL cholesterol original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -1.92 38.58 46.08 47.70 55.08 139.08 57
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -139.08 -55.08 -46.08 -47.70 -38.58 1.92 57
## [1] "A1C original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.73 5.04 5.20 5.33 5.45 11.25 89
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -11.25 -5.45 -5.20 -5.33 -5.04 -3.73 89
## [1] "BMI original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 14.3 24.5 28.5 29.9 33.7 93.9 54
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -93.9 -33.7 -28.5 -29.9 -24.5 -14.3 54
## [1] "PHQ-9 depression score original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 2.00 5.00 6.82 10.00 24.00 64
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -24.00 -10.00 -5.00 -6.82 -2.00 0.00 64
## [1] "Cardiovascular risk score original/negative:"
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 0.0 0.1 0.1 0.1 0.3 2801
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -0.3 -0.1 -0.1 -0.1 0.0 0.0 2801
## [1] "Debt original/negative:"
##
## 0 1 <NA>
## 5468 6626 114
##
## 0 1 <NA>
## 6626 5468 114
## [1] "Borrowing original/negative:"
##
## 0 1 <NA>
## 9456 2742 10
##
## 0 1 <NA>
## 2742 9456 10
## [1] "Catastrophic expenditure original/negative:"
##
## 0 1 <NA>
## 11244 540 424
##
## 0 1 <NA>
## 540 11244 424
## [1] "Final data structure:"
## Rows: 12,208
## Columns: 69
## $ person_id <dbl> 5, 8, 16, 17, 18, 23, 24, 29, 47, 57, 59, 68, 70…
## $ weight_total_inp <dbl> 1.1504, 0.8975, 1.0000, 1.2126, 1.0000, 1.0033, …
## $ gender_inp <dbl> 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, …
## $ age_inp <dbl> 60, 41, 39, 52, 51, 32, 34, 23, 43, 46, 38, 25, …
## $ ast_dx_pre_lottery <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ dia_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ hbp_dx_pre_lottery <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, …
## $ chl_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ami_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ chf_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ emp_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ kid_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cancer_dx_pre_lottery <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, …
## $ dep_dx_pre_lottery <dbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, …
## $ diabetes <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ hypertension <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ highcholesterol <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ depression <dbl> 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ phq <dbl> 1, 9, 2, 13, 2, 3, 2, 14, 11, 8, 7, 3, 2, 0, 2, …
## $ cvd_risk <dbl> 0.1370, 0.1120, 0.0330, 0.2530, 0.1560, 0.0120, …
## $ doc_any <dbl> 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, …
## $ ed_any <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, …
## $ hosp_any <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ oop_spend <dbl> 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, …
## $ catastrophic <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ debt <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, …
## $ borrow <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, …
## $ hispanic_inp <dbl> 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ race_white_inp <dbl> 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, …
## $ race_black_inp <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ race_nwother_inp <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ a1c <dbl> 5.037, 5.201, 5.854, 5.364, 5.527, 5.037, 5.446,…
## $ hdl_level <dbl> 48.33, 51.33, 38.58, 51.33, 28.08, 31.08, 25.83,…
## $ chl_level <dbl> 241.0, 229.9, 229.9, 235.4, 177.7, 173.8, 152.7,…
## $ bmi <dbl> 26.66, 35.23, 37.12, 24.81, 27.02, 26.26, 27.70,…
## $ sbp <dbl> 144, 134, 126, 168, 119, 98, 108, 125, 100, 104,…
## $ dbp <dbl> 81, 82, 94, 110, 79, 59, 63, 76, 77, 63, 84, 62,…
## $ prescriptions_any <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, …
## $ prescriptions <dbl> 0, 2, 2, NA, 0, 0, 0, 3, 0, 3, 4, 0, 4, 2, 1, 4,…
## $ hypertension_med <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, …
## $ cholesterol_med <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ diabetes_med <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ depression_med <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, …
## $ household_id <dbl> 100005, 102094, 140688, 100017, 100018, 115253, …
## $ eligibility <dbl> 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, …
## $ numhh_list <fct> 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ ohp_all_ever_inperson <dbl> 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, …
## $ doc_num <dbl> 0, 6, 12, 0, 0, 5, 0, 5, 1, 12, 6, 0, 3, 0, 10, …
## $ ed_num <dbl> 0, 2, 1, 1, 0, 0, 0, 10, 2, 6, 0, 2, 0, 0, 0, 0,…
## $ hosp_num <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, …
## $ num_visit_pre_cens_ed <dbl> 0, 0, 1, NA, 2, 0, 0, 7, 0, NA, 0, 0, 1, 0, 0, 0…
## $ any_depres_pre_ed <dbl> 0, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0…
## $ charg_tot_pre_ed <dbl> 0.0, 0.0, 1888.2, NA, 1715.3, 0.0, 0.0, 5743.9, …
## $ charg_tot_ed <dbl> 0, 2751, 15233, NA, 0, 0, 0, 8436, 0, NA, 0, 0, …
## $ ed_charg_tot_pre_ed <dbl> 0.0, 0.0, 1888.2, NA, 1006.3, 0.0, 0.0, 4542.4, …
## $ ed_charg_tot_ed <dbl> 0.0, 2751.4, 7100.8, NA, 0.0, 0.0, 0.0, 7067.0, …
## $ lessHS <dbl> 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ HSorGED <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, …
## $ sbp_neg <dbl> -144, -134, -126, -168, -119, -98, -108, -125, -…
## $ dbp_neg <dbl> -81, -82, -94, -110, -79, -59, -63, -76, -77, -6…
## $ chl_level_neg <dbl> -241.0, -229.9, -229.9, -235.4, -177.7, -173.8, …
## $ hdl_level_neg <dbl> -48.33, -51.33, -38.58, -51.33, -28.08, -31.08, …
## $ a1c_neg <dbl> -5.037, -5.201, -5.854, -5.364, -5.527, -5.037, …
## $ bmi_neg <dbl> -26.66, -35.23, -37.12, -24.81, -27.02, -26.26, …
## $ phq_neg <dbl> -1, -9, -2, -13, -2, -3, -2, -14, -11, -8, -7, -…
## $ cvd_risk_neg <dbl> -0.1370, -0.1120, -0.0330, -0.2530, -0.1560, -0.…
## $ debt_neg <dbl> 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, …
## $ borrow_neg <dbl> 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, …
## $ catastrophic_neg <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, …
## person_id weight_total_inp gender_inp age_inp
## Min. : 5 Min. : 0.681 Min. :0.000 Min. :19.0
## 1st Qu.:18944 1st Qu.: 1.000 1st Qu.:0.000 1st Qu.:31.0
## Median :37494 Median : 1.068 Median :1.000 Median :41.0
## Mean :37500 Mean : 1.240 Mean :0.566 Mean :40.8
## 3rd Qu.:55947 3rd Qu.: 1.213 3rd Qu.:1.000 3rd Qu.:50.0
## Max. :74911 Max. :13.634 Max. :1.000 Max. :71.0
##
## ast_dx_pre_lottery dia_dx_pre_lottery hbp_dx_pre_lottery chl_dx_pre_lottery
## Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.0000 Median :0.000 Median :0.000
## Mean :0.193 Mean :0.0711 Mean :0.182 Mean :0.127
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.0000 Max. :1.000 Max. :1.000
##
## ami_dx_pre_lottery chf_dx_pre_lottery emp_dx_pre_lottery kid_dx_pre_lottery
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.0197 Mean :0.0111 Mean :0.022 Mean :0.0186
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
##
## cancer_dx_pre_lottery dep_dx_pre_lottery diabetes hypertension
## Min. :0.0000 Min. :0.000 Min. :0.00 Min. :0.00
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.00
## Median :0.0000 Median :0.000 Median :0.00 Median :0.00
## Mean :0.0428 Mean :0.341 Mean :0.02 Mean :0.06
## 3rd Qu.:0.0000 3rd Qu.:1.000 3rd Qu.:0.00 3rd Qu.:0.00
## Max. :1.0000 Max. :1.000 Max. :1.00 Max. :1.00
## NA's :41 NA's :281
## highcholesterol depression phq cvd_risk doc_any
## Min. :0.0 Min. :0.00 Min. : 0.00 Min. :0.0 Min. :0.00
## 1st Qu.:0.0 1st Qu.:0.00 1st Qu.: 2.00 1st Qu.:0.0 1st Qu.:0.00
## Median :0.0 Median :0.00 Median : 5.00 Median :0.1 Median :1.00
## Mean :0.1 Mean :0.05 Mean : 6.82 Mean :0.1 Mean :0.67
## 3rd Qu.:0.0 3rd Qu.:0.00 3rd Qu.:10.00 3rd Qu.:0.1 3rd Qu.:1.00
## Max. :1.0 Max. :1.00 Max. :24.00 Max. :0.3 Max. :1.00
## NA's :332 NA's :131 NA's :64 NA's :2801 NA's :17
## ed_any hosp_any oop_spend catastrophic debt
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.:0.00
## Median :0.000 Median :0.000 Median :1.000 Median :0 Median :1.00
## Mean :0.406 Mean :0.131 Mean :0.569 Mean :0 Mean :0.55
## 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:0 3rd Qu.:1.00
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1 Max. :1.00
## NA's :18 NA's :17 NA's :28 NA's :424 NA's :114
## borrow hispanic_inp race_white_inp race_black_inp
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :1.000 Median :0.000
## Mean :0.225 Mean :0.18 Mean :0.687 Mean :0.103
## 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:1.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000
## NA's :10
## race_nwother_inp a1c hdl_level chl_level bmi
## Min. :0.000 Min. : 3.73 Min. : -1.92 Min. :117 Min. :14.3
## 1st Qu.:0.000 1st Qu.: 5.04 1st Qu.: 38.58 1st Qu.:182 1st Qu.:24.5
## Median :0.000 Median : 5.20 Median : 46.08 Median :203 Median :28.5
## Mean :0.144 Mean : 5.33 Mean : 47.70 Mean :205 Mean :29.9
## 3rd Qu.:0.000 3rd Qu.: 5.45 3rd Qu.: 55.08 3rd Qu.:226 3rd Qu.:33.7
## Max. :1.000 Max. :11.25 Max. :139.08 Max. :488 Max. :93.9
## NA's :89 NA's :57 NA's :55 NA's :54
## sbp dbp prescriptions_any prescriptions
## Min. : 72 Min. : 39.0 Min. :0.00 Min. : 0.0
## 1st Qu.:108 1st Qu.: 67.0 1st Qu.:0.00 1st Qu.: 0.0
## Median :117 Median : 75.0 Median :1.00 Median : 1.0
## Mean :119 Mean : 75.8 Mean :0.55 Mean : 1.9
## 3rd Qu.:128 3rd Qu.: 83.0 3rd Qu.:1.00 3rd Qu.: 3.0
## Max. :229 Max. :158.0 Max. :1.00 Max. :28.0
## NA's :41 NA's :41 NA's :2 NA's :311
## hypertension_med cholesterol_med diabetes_med depression_med
## Min. :0.00 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.00 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.14 Mean :0.0894 Mean :0.0709 Mean :0.175
## 3rd Qu.:0.00 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.00 Max. :1.0000 Max. :1.0000 Max. :1.000
##
## household_id eligibility numhh_list ohp_all_ever_inperson
## Min. :100005 Min. :0.000 1:9239 Min. :0.000
## 1st Qu.:122926 1st Qu.:0.000 2:2951 1st Qu.:0.000
## Median :141844 Median :1.000 3: 18 Median :0.000
## Mean :140510 Mean :0.522 Mean :0.311
## 3rd Qu.:159100 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :174911 Max. :1.000 Max. :1.000
##
## doc_num ed_num hosp_num num_visit_pre_cens_ed
## Min. : 0.00 Min. : 0.00 Min. :0.0 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:0.0 1st Qu.: 0.0
## Median : 2.00 Median : 0.00 Median :0.0 Median : 0.0
## Mean : 5.94 Mean : 0.99 Mean :0.2 Mean : 0.8
## 3rd Qu.: 6.00 3rd Qu.: 1.00 3rd Qu.:0.0 3rd Qu.: 1.0
## Max. :144.00 Max. :20.00 Max. :8.0 Max. :17.0
## NA's :64 NA's :47 NA's :47 NA's :2055
## any_depres_pre_ed charg_tot_pre_ed charg_tot_ed ed_charg_tot_pre_ed
## Min. :0 Min. : 0 Min. : 0 Min. : 0
## 1st Qu.:0 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0
## Median :0 Median : 0 Median : 0 Median : 0
## Mean :0 Mean : 2078 Mean : 3319 Mean : 880
## 3rd Qu.:0 3rd Qu.: 740 3rd Qu.: 1401 3rd Qu.: 595
## Max. :1 Max. :180055 Max. :266993 Max. :41246
## NA's :2050 NA's :2053 NA's :2055 NA's :2058
## ed_charg_tot_ed lessHS HSorGED sbp_neg
## Min. : 0 Min. :0.000 Min. :0.000 Min. :-229
## 1st Qu.: 0 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:-128
## Median : 0 Median :0.000 Median :0.000 Median :-117
## Mean : 1433 Mean :0.205 Mean :0.454 Mean :-119
## 3rd Qu.: 1128 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:-108
## Max. :67985 Max. :1.000 Max. :1.000 Max. : -72
## NA's :2056 NA's :41
## dbp_neg chl_level_neg hdl_level_neg a1c_neg
## Min. :-158.0 Min. :-488 Min. :-139.08 Min. :-11.25
## 1st Qu.: -83.0 1st Qu.:-226 1st Qu.: -55.08 1st Qu.: -5.45
## Median : -75.0 Median :-203 Median : -46.08 Median : -5.20
## Mean : -75.8 Mean :-205 Mean : -47.70 Mean : -5.33
## 3rd Qu.: -67.0 3rd Qu.:-182 3rd Qu.: -38.58 3rd Qu.: -5.04
## Max. : -39.0 Max. :-117 Max. : 1.92 Max. : -3.73
## NA's :41 NA's :55 NA's :57 NA's :89
## bmi_neg phq_neg cvd_risk_neg debt_neg borrow_neg
## Min. :-93.9 Min. :-24.00 Min. :-0.3 Min. :0.00 Min. :0.000
## 1st Qu.:-33.7 1st Qu.:-10.00 1st Qu.:-0.1 1st Qu.:0.00 1st Qu.:1.000
## Median :-28.5 Median : -5.00 Median :-0.1 Median :0.00 Median :1.000
## Mean :-29.9 Mean : -6.82 Mean :-0.1 Mean :0.45 Mean :0.775
## 3rd Qu.:-24.5 3rd Qu.: -2.00 3rd Qu.: 0.0 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :-14.3 Max. : 0.00 Max. : 0.0 Max. :1.00 Max. :1.000
## NA's :54 NA's :64 NA's :2801 NA's :114 NA's :10
## catastrophic_neg
## Min. :0
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :424