aline-propensity-score
Alistair Johnson, Jesse Raffa
May 15, 2017
Analysis of arterial line dataset
This notebook creates a propensity score using a dataset of patients with indwelling arterial catheters (IACs). The propensity score is built using physiology and administrative data to predict the need for an IAC. Patients are then matched, and we statistically compare the mortality rate in the two matched groups.
Load data
First, we load the data and convert some variables into factors. Note this code assumes that you have the csv file available in “~/git/mimic-code/notebooks/aline/”.
wdpath = paste(path.expand("~"),'git/mimic-code/notebooks/aline/',sep='/')
setwd(wdpath)
dataset = read.csv(file="aline_data.csv",head=TRUE,sep=",")If we did not remove any missing data above, we need to subselect complete cases for analysis.
## [1] "Removed 160 rows with missing data."Propensity score model
Now, we build a logistic regression, using all the features, to predict the need for an arterial line catheter from physiology and administrative data.
# fit GLM
glm_fitted = glm(aline_flag ~ ., data=dat, family="binomial", na.action = na.exclude)With our model fit, we now run step-wise AIC to remove features. We then plot the ROC curve, and calculate the area under the ROC curve.
# run step-wise AIC
library(MASS);
glm_fitted <- stepAIC(glm_fitted )## Start: AIC=3054.63
## aline_flag ~ age + gender + weight_first + sofa_first + service_surg +
## day_icu_intime + hour_icu_intime + chf_flag + afib_flag +
## renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag +
## malignancy_flag + respfail_flag + map_first + hr_first +
## temp_first + spo2_first + bun_first + chloride_first + creatinine_first +
## hgb_first + platelet_first + potassium_first + sodium_first +
## tco2_first + wbc_first
##
## Df Deviance AIC
## - hour_icu_intime 23 2977.9 3045.9
## - day_icu_intime 6 2949.4 3051.4
## - creatinine_first 1 2940.7 3052.7
## - hr_first 1 2940.8 3052.8
## - afib_flag 1 2940.8 3052.8
## - temp_first 1 2940.8 3052.8
## - cad_flag 1 2940.9 3052.9
## - age 1 2941.2 3053.2
## - chf_flag 1 2941.2 3053.2
## - malignancy_flag 1 2941.2 3053.2
## - hgb_first 1 2941.2 3053.2
## - spo2_first 1 2941.2 3053.2
## - gender 1 2941.3 3053.3
## - respfail_flag 1 2941.6 3053.6
## - bun_first 1 2941.8 3053.8
## - potassium_first 1 2942.6 3054.6
## <none> 2940.6 3054.6
## - platelet_first 1 2943.1 3055.1
## - weight_first 1 2943.9 3055.9
## - renal_flag 1 2946.0 3058.0
## - liver_flag 1 2946.1 3058.1
## - tco2_first 1 2946.5 3058.5
## - copd_flag 1 2948.3 3060.3
## - map_first 1 2950.6 3062.6
## - sodium_first 1 2962.5 3074.5
## - sofa_first 1 2966.3 3078.3
## - wbc_first 1 2969.6 3081.6
## - stroke_flag 1 2979.0 3091.0
## - chloride_first 1 2980.9 3092.9
## - service_surg 1 2993.1 3105.1
##
## Step: AIC=3045.91
## aline_flag ~ age + gender + weight_first + sofa_first + service_surg +
## day_icu_intime + chf_flag + afib_flag + renal_flag + liver_flag +
## copd_flag + cad_flag + stroke_flag + malignancy_flag + respfail_flag +
## map_first + hr_first + temp_first + spo2_first + bun_first +
## chloride_first + creatinine_first + hgb_first + platelet_first +
## potassium_first + sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - day_icu_intime 6 2984.5 3040.5
## - creatinine_first 1 2977.9 3043.9
## - hr_first 1 2978.0 3044.0
## - afib_flag 1 2978.1 3044.1
## - age 1 2978.2 3044.2
## - spo2_first 1 2978.2 3044.2
## - gender 1 2978.3 3044.3
## - temp_first 1 2978.4 3044.4
## - cad_flag 1 2978.4 3044.4
## - malignancy_flag 1 2978.4 3044.4
## - chf_flag 1 2978.6 3044.6
## - hgb_first 1 2978.6 3044.6
## - respfail_flag 1 2978.7 3044.7
## - bun_first 1 2979.1 3045.1
## - potassium_first 1 2979.1 3045.1
## <none> 2977.9 3045.9
## - platelet_first 1 2980.8 3046.8
## - weight_first 1 2980.9 3046.9
## - renal_flag 1 2983.8 3049.8
## - liver_flag 1 2984.0 3050.0
## - tco2_first 1 2984.6 3050.6
## - copd_flag 1 2987.2 3053.2
## - map_first 1 2988.2 3054.2
## - sodium_first 1 3000.7 3066.7
## - sofa_first 1 3004.0 3070.0
## - wbc_first 1 3006.3 3072.3
## - stroke_flag 1 3016.5 3082.5
## - chloride_first 1 3020.5 3086.5
## - service_surg 1 3034.2 3100.2
##
## Step: AIC=3040.52
## aline_flag ~ age + gender + weight_first + sofa_first + service_surg +
## chf_flag + afib_flag + renal_flag + liver_flag + copd_flag +
## cad_flag + stroke_flag + malignancy_flag + respfail_flag +
## map_first + hr_first + temp_first + spo2_first + bun_first +
## chloride_first + creatinine_first + hgb_first + platelet_first +
## potassium_first + sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - creatinine_first 1 2984.5 3038.5
## - hr_first 1 2984.6 3038.6
## - afib_flag 1 2984.7 3038.7
## - age 1 2984.8 3038.8
## - spo2_first 1 2984.8 3038.8
## - temp_first 1 2984.9 3038.9
## - cad_flag 1 2985.0 3039.0
## - gender 1 2985.1 3039.1
## - malignancy_flag 1 2985.1 3039.1
## - hgb_first 1 2985.1 3039.1
## - chf_flag 1 2985.2 3039.2
## - respfail_flag 1 2985.3 3039.3
## - bun_first 1 2985.8 3039.8
## - potassium_first 1 2985.9 3039.9
## <none> 2984.5 3040.5
## - platelet_first 1 2987.2 3041.2
## - weight_first 1 2987.3 3041.3
## - renal_flag 1 2990.4 3044.4
## - liver_flag 1 2990.6 3044.6
## - tco2_first 1 2991.0 3045.0
## - copd_flag 1 2993.8 3047.8
## - map_first 1 2994.6 3048.6
## - sodium_first 1 3007.9 3061.9
## - sofa_first 1 3011.4 3065.4
## - wbc_first 1 3013.2 3067.2
## - stroke_flag 1 3023.1 3077.1
## - chloride_first 1 3027.6 3081.6
## - service_surg 1 3041.1 3095.1
##
## Step: AIC=3038.52
## aline_flag ~ age + gender + weight_first + sofa_first + service_surg +
## chf_flag + afib_flag + renal_flag + liver_flag + copd_flag +
## cad_flag + stroke_flag + malignancy_flag + respfail_flag +
## map_first + hr_first + temp_first + spo2_first + bun_first +
## chloride_first + hgb_first + platelet_first + potassium_first +
## sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - hr_first 1 2984.6 3036.6
## - afib_flag 1 2984.7 3036.7
## - age 1 2984.8 3036.8
## - spo2_first 1 2984.8 3036.8
## - temp_first 1 2984.9 3036.9
## - cad_flag 1 2985.1 3037.1
## - gender 1 2985.1 3037.1
## - malignancy_flag 1 2985.1 3037.1
## - hgb_first 1 2985.1 3037.1
## - chf_flag 1 2985.2 3037.2
## - respfail_flag 1 2985.3 3037.3
## - potassium_first 1 2985.9 3037.9
## - bun_first 1 2986.4 3038.4
## <none> 2984.5 3038.5
## - platelet_first 1 2987.2 3039.2
## - weight_first 1 2987.3 3039.3
## - liver_flag 1 2990.6 3042.6
## - renal_flag 1 2990.9 3042.9
## - tco2_first 1 2991.1 3043.1
## - copd_flag 1 2993.8 3045.8
## - map_first 1 2994.6 3046.6
## - sodium_first 1 3008.1 3060.1
## - sofa_first 1 3012.7 3064.7
## - wbc_first 1 3013.2 3065.2
## - stroke_flag 1 3023.1 3075.1
## - chloride_first 1 3028.7 3080.7
## - service_surg 1 3041.1 3093.1
##
## Step: AIC=3036.62
## aline_flag ~ age + gender + weight_first + sofa_first + service_surg +
## chf_flag + afib_flag + renal_flag + liver_flag + copd_flag +
## cad_flag + stroke_flag + malignancy_flag + respfail_flag +
## map_first + temp_first + spo2_first + bun_first + chloride_first +
## hgb_first + platelet_first + potassium_first + sodium_first +
## tco2_first + wbc_first
##
## Df Deviance AIC
## - afib_flag 1 2984.8 3034.8
## - age 1 2984.8 3034.8
## - temp_first 1 2985.0 3035.0
## - spo2_first 1 2985.0 3035.0
## - cad_flag 1 2985.2 3035.2
## - gender 1 2985.2 3035.2
## - malignancy_flag 1 2985.2 3035.2
## - hgb_first 1 2985.2 3035.2
## - chf_flag 1 2985.3 3035.3
## - respfail_flag 1 2985.5 3035.5
## - potassium_first 1 2986.0 3036.0
## - bun_first 1 2986.5 3036.5
## <none> 2984.6 3036.6
## - platelet_first 1 2987.3 3037.3
## - weight_first 1 2987.4 3037.4
## - liver_flag 1 2990.9 3040.9
## - renal_flag 1 2990.9 3040.9
## - tco2_first 1 2991.3 3041.3
## - copd_flag 1 2994.1 3044.1
## - map_first 1 2994.7 3044.7
## - sodium_first 1 3008.5 3058.5
## - sofa_first 1 3012.8 3062.8
## - wbc_first 1 3013.3 3063.3
## - stroke_flag 1 3023.8 3073.8
## - chloride_first 1 3029.1 3079.1
## - service_surg 1 3041.1 3091.1
##
## Step: AIC=3034.76
## aline_flag ~ age + gender + weight_first + sofa_first + service_surg +
## chf_flag + renal_flag + liver_flag + copd_flag + cad_flag +
## stroke_flag + malignancy_flag + respfail_flag + map_first +
## temp_first + spo2_first + bun_first + chloride_first + hgb_first +
## platelet_first + potassium_first + sodium_first + tco2_first +
## wbc_first
##
## Df Deviance AIC
## - age 1 2984.9 3032.9
## - temp_first 1 2985.1 3033.1
## - spo2_first 1 2985.1 3033.1
## - cad_flag 1 2985.3 3033.3
## - gender 1 2985.3 3033.3
## - malignancy_flag 1 2985.4 3033.4
## - hgb_first 1 2985.4 3033.4
## - chf_flag 1 2985.5 3033.5
## - respfail_flag 1 2985.6 3033.6
## - potassium_first 1 2986.2 3034.2
## - bun_first 1 2986.7 3034.7
## <none> 2984.8 3034.8
## - platelet_first 1 2987.5 3035.5
## - weight_first 1 2987.6 3035.6
## - renal_flag 1 2991.0 3039.0
## - liver_flag 1 2991.2 3039.2
## - tco2_first 1 2991.6 3039.6
## - copd_flag 1 2994.2 3042.2
## - map_first 1 2994.8 3042.8
## - sodium_first 1 3008.6 3056.6
## - sofa_first 1 3012.8 3060.8
## - wbc_first 1 3013.5 3061.5
## - stroke_flag 1 3024.6 3072.6
## - chloride_first 1 3029.2 3077.2
## - service_surg 1 3041.1 3089.1
##
## Step: AIC=3032.89
## aline_flag ~ gender + weight_first + sofa_first + service_surg +
## chf_flag + renal_flag + liver_flag + copd_flag + cad_flag +
## stroke_flag + malignancy_flag + respfail_flag + map_first +
## temp_first + spo2_first + bun_first + chloride_first + hgb_first +
## platelet_first + potassium_first + sodium_first + tco2_first +
## wbc_first
##
## Df Deviance AIC
## - temp_first 1 2985.2 3031.2
## - spo2_first 1 2985.3 3031.3
## - hgb_first 1 2985.4 3031.4
## - gender 1 2985.5 3031.5
## - cad_flag 1 2985.5 3031.5
## - malignancy_flag 1 2985.6 3031.6
## - chf_flag 1 2985.6 3031.6
## - respfail_flag 1 2985.8 3031.8
## - potassium_first 1 2986.4 3032.4
## - bun_first 1 2986.7 3032.7
## <none> 2984.9 3032.9
## - platelet_first 1 2987.6 3033.6
## - weight_first 1 2987.9 3033.9
## - renal_flag 1 2991.1 3037.1
## - liver_flag 1 2991.2 3037.2
## - tco2_first 1 2991.6 3037.6
## - copd_flag 1 2994.5 3040.5
## - map_first 1 2995.0 3041.0
## - sodium_first 1 3008.8 3054.8
## - sofa_first 1 3012.9 3058.9
## - wbc_first 1 3014.5 3060.5
## - stroke_flag 1 3027.3 3073.3
## - chloride_first 1 3030.0 3076.0
## - service_surg 1 3041.1 3087.1
##
## Step: AIC=3031.24
## aline_flag ~ gender + weight_first + sofa_first + service_surg +
## chf_flag + renal_flag + liver_flag + copd_flag + cad_flag +
## stroke_flag + malignancy_flag + respfail_flag + map_first +
## spo2_first + bun_first + chloride_first + hgb_first + platelet_first +
## potassium_first + sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - spo2_first 1 2985.6 3029.6
## - hgb_first 1 2985.8 3029.8
## - malignancy_flag 1 2985.9 3029.9
## - gender 1 2985.9 3029.9
## - chf_flag 1 2985.9 3029.9
## - cad_flag 1 2986.0 3030.0
## - respfail_flag 1 2986.1 3030.1
## - potassium_first 1 2986.8 3030.8
## - bun_first 1 2987.0 3031.0
## <none> 2985.2 3031.2
## - platelet_first 1 2987.9 3031.9
## - weight_first 1 2988.3 3032.3
## - renal_flag 1 2991.4 3035.4
## - liver_flag 1 2991.5 3035.5
## - tco2_first 1 2991.9 3035.9
## - copd_flag 1 2995.0 3039.0
## - map_first 1 2995.2 3039.2
## - sodium_first 1 3009.0 3053.0
## - sofa_first 1 3013.2 3057.2
## - wbc_first 1 3015.5 3059.5
## - stroke_flag 1 3027.9 3071.9
## - chloride_first 1 3030.1 3074.1
## - service_surg 1 3041.9 3085.9
##
## Step: AIC=3029.62
## aline_flag ~ gender + weight_first + sofa_first + service_surg +
## chf_flag + renal_flag + liver_flag + copd_flag + cad_flag +
## stroke_flag + malignancy_flag + respfail_flag + map_first +
## bun_first + chloride_first + hgb_first + platelet_first +
## potassium_first + sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - hgb_first 1 2986.2 3028.2
## - chf_flag 1 2986.3 3028.3
## - gender 1 2986.3 3028.3
## - malignancy_flag 1 2986.3 3028.3
## - cad_flag 1 2986.4 3028.4
## - respfail_flag 1 2986.6 3028.6
## - potassium_first 1 2987.2 3029.2
## - bun_first 1 2987.3 3029.3
## <none> 2985.6 3029.6
## - platelet_first 1 2988.3 3030.3
## - weight_first 1 2988.6 3030.6
## - renal_flag 1 2991.8 3033.8
## - liver_flag 1 2992.0 3034.0
## - tco2_first 1 2992.1 3034.1
## - map_first 1 2995.5 3037.5
## - copd_flag 1 2995.8 3037.8
## - sodium_first 1 3009.2 3051.2
## - sofa_first 1 3013.9 3055.9
## - wbc_first 1 3015.5 3057.5
## - stroke_flag 1 3028.4 3070.4
## - chloride_first 1 3030.3 3072.3
## - service_surg 1 3042.0 3084.0
##
## Step: AIC=3028.18
## aline_flag ~ gender + weight_first + sofa_first + service_surg +
## chf_flag + renal_flag + liver_flag + copd_flag + cad_flag +
## stroke_flag + malignancy_flag + respfail_flag + map_first +
## bun_first + chloride_first + platelet_first + potassium_first +
## sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - gender 1 2986.6 3026.6
## - malignancy_flag 1 2986.7 3026.7
## - chf_flag 1 2986.9 3026.9
## - cad_flag 1 2986.9 3026.9
## - respfail_flag 1 2987.1 3027.1
## - potassium_first 1 2987.9 3027.9
## <none> 2986.2 3028.2
## - bun_first 1 2988.4 3028.4
## - platelet_first 1 2988.8 3028.8
## - weight_first 1 2989.0 3029.0
## - renal_flag 1 2992.2 3032.2
## - liver_flag 1 2992.2 3032.2
## - tco2_first 1 2994.0 3034.0
## - map_first 1 2995.9 3035.9
## - copd_flag 1 2996.2 3036.2
## - sofa_first 1 3015.0 3055.0
## - wbc_first 1 3015.6 3055.6
## - sodium_first 1 3016.9 3056.9
## - stroke_flag 1 3028.4 3068.4
## - service_surg 1 3043.1 3083.1
## - chloride_first 1 3044.2 3084.2
##
## Step: AIC=3026.62
## aline_flag ~ weight_first + sofa_first + service_surg + chf_flag +
## renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag +
## malignancy_flag + respfail_flag + map_first + bun_first +
## chloride_first + platelet_first + potassium_first + sodium_first +
## tco2_first + wbc_first
##
## Df Deviance AIC
## - malignancy_flag 1 2987.2 3025.2
## - chf_flag 1 2987.2 3025.2
## - cad_flag 1 2987.3 3025.3
## - respfail_flag 1 2987.6 3025.6
## - potassium_first 1 2988.2 3026.2
## <none> 2986.6 3026.6
## - bun_first 1 2988.8 3026.8
## - platelet_first 1 2989.5 3027.5
## - weight_first 1 2990.3 3028.3
## - renal_flag 1 2992.7 3030.7
## - liver_flag 1 2992.7 3030.7
## - tco2_first 1 2994.3 3032.3
## - map_first 1 2996.2 3034.2
## - copd_flag 1 2996.7 3034.7
## - wbc_first 1 3016.5 3054.5
## - sofa_first 1 3016.6 3054.6
## - sodium_first 1 3016.9 3054.9
## - stroke_flag 1 3028.4 3066.4
## - service_surg 1 3043.2 3081.2
## - chloride_first 1 3044.3 3082.3
##
## Step: AIC=3025.15
## aline_flag ~ weight_first + sofa_first + service_surg + chf_flag +
## renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag +
## respfail_flag + map_first + bun_first + chloride_first +
## platelet_first + potassium_first + sodium_first + tco2_first +
## wbc_first
##
## Df Deviance AIC
## - chf_flag 1 2987.8 3023.8
## - cad_flag 1 2987.8 3023.8
## - respfail_flag 1 2988.2 3024.2
## - potassium_first 1 2988.8 3024.8
## <none> 2987.2 3025.2
## - bun_first 1 2989.2 3025.2
## - platelet_first 1 2990.2 3026.2
## - weight_first 1 2991.0 3027.0
## - renal_flag 1 2993.1 3029.1
## - liver_flag 1 2993.3 3029.3
## - tco2_first 1 2994.6 3030.6
## - map_first 1 2996.9 3032.9
## - copd_flag 1 2997.3 3033.3
## - sofa_first 1 3016.8 3052.8
## - sodium_first 1 3016.9 3052.9
## - wbc_first 1 3017.7 3053.7
## - stroke_flag 1 3029.6 3065.6
## - service_surg 1 3043.3 3079.3
## - chloride_first 1 3044.4 3080.4
##
## Step: AIC=3023.75
## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag +
## liver_flag + copd_flag + cad_flag + stroke_flag + respfail_flag +
## map_first + bun_first + chloride_first + platelet_first +
## potassium_first + sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - cad_flag 1 2988.2 3022.2
## - respfail_flag 1 2988.7 3022.7
## - potassium_first 1 2989.3 3023.3
## <none> 2987.8 3023.8
## - bun_first 1 2990.2 3024.2
## - platelet_first 1 2990.8 3024.8
## - weight_first 1 2991.7 3025.7
## - renal_flag 1 2993.4 3027.4
## - liver_flag 1 2993.9 3027.9
## - tco2_first 1 2995.9 3029.9
## - map_first 1 2997.4 3031.4
## - copd_flag 1 2997.4 3031.4
## - sofa_first 1 3017.1 3051.1
## - sodium_first 1 3017.2 3051.2
## - wbc_first 1 3018.2 3052.2
## - stroke_flag 1 3029.7 3063.7
## - service_surg 1 3043.7 3077.7
## - chloride_first 1 3044.5 3078.5
##
## Step: AIC=3022.25
## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag +
## liver_flag + copd_flag + stroke_flag + respfail_flag + map_first +
## bun_first + chloride_first + platelet_first + potassium_first +
## sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## - respfail_flag 1 2989.1 3021.1
## - potassium_first 1 2989.8 3021.8
## <none> 2988.2 3022.2
## - bun_first 1 2990.5 3022.5
## - platelet_first 1 2991.3 3023.3
## - weight_first 1 2992.1 3024.1
## - liver_flag 1 2994.3 3026.3
## - renal_flag 1 2994.7 3026.7
## - tco2_first 1 2996.4 3028.4
## - copd_flag 1 2998.0 3030.0
## - map_first 1 2998.0 3030.0
## - sodium_first 1 3017.5 3049.5
## - sofa_first 1 3018.1 3050.1
## - wbc_first 1 3018.9 3050.9
## - stroke_flag 1 3029.7 3061.7
## - service_surg 1 3044.0 3076.0
## - chloride_first 1 3044.9 3076.9
##
## Step: AIC=3021.13
## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag +
## liver_flag + copd_flag + stroke_flag + map_first + bun_first +
## chloride_first + platelet_first + potassium_first + sodium_first +
## tco2_first + wbc_first
##
## Df Deviance AIC
## - potassium_first 1 2990.7 3020.7
## - bun_first 1 2991.1 3021.1
## <none> 2989.1 3021.1
## - platelet_first 1 2992.2 3022.2
## - weight_first 1 2992.9 3022.9
## - liver_flag 1 2995.2 3025.2
## - renal_flag 1 2995.6 3025.6
## - tco2_first 1 2996.7 3026.7
## - map_first 1 2998.8 3028.8
## - copd_flag 1 2999.1 3029.1
## - sofa_first 1 3018.3 3048.3
## - sodium_first 1 3018.4 3048.4
## - wbc_first 1 3019.5 3049.5
## - stroke_flag 1 3032.1 3062.1
## - chloride_first 1 3046.0 3076.0
## - service_surg 1 3046.2 3076.2
##
## Step: AIC=3020.68
## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag +
## liver_flag + copd_flag + stroke_flag + map_first + bun_first +
## chloride_first + platelet_first + sodium_first + tco2_first +
## wbc_first
##
## Df Deviance AIC
## - bun_first 1 2992.0 3020.0
## <none> 2990.7 3020.7
## - weight_first 1 2994.1 3022.1
## - platelet_first 1 2994.2 3022.2
## - liver_flag 1 2996.9 3024.9
## - renal_flag 1 2997.9 3025.9
## - tco2_first 1 2998.0 3026.0
## - map_first 1 3000.2 3028.2
## - copd_flag 1 3001.0 3029.0
## - sodium_first 1 3018.7 3046.7
## - sofa_first 1 3019.3 3047.3
## - wbc_first 1 3020.6 3048.6
## - stroke_flag 1 3034.3 3062.3
## - chloride_first 1 3046.4 3074.4
## - service_surg 1 3049.0 3077.0
##
## Step: AIC=3020
## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag +
## liver_flag + copd_flag + stroke_flag + map_first + chloride_first +
## platelet_first + sodium_first + tco2_first + wbc_first
##
## Df Deviance AIC
## <none> 2992.0 3020.0
## - weight_first 1 2995.5 3021.5
## - platelet_first 1 2995.7 3021.7
## - renal_flag 1 2998.0 3024.0
## - liver_flag 1 2998.3 3024.3
## - tco2_first 1 2999.3 3025.3
## - map_first 1 3001.6 3027.6
## - copd_flag 1 3002.1 3028.1
## - sodium_first 1 3019.8 3045.8
## - wbc_first 1 3022.8 3048.8
## - sofa_first 1 3026.8 3052.8
## - stroke_flag 1 3035.9 3061.9
## - chloride_first 1 3047.2 3073.2
## - service_surg 1 3049.9 3075.9X <- fitted(glm_fitted, type="response")
Tr <- dat$aline_flag
library("pROC") ## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, varroccurve <- roc(Tr ~ X)
plot(roccurve, col=rainbow(7), main="ROC curve", xlab="Specificity", ylab="Sensitivity")
auc(roccurve)## Area under the curve: 0.6945Our final model has a subset of features and OK AUROC. Let’s plot the predictions it makes using a stacked bar chart.
# plot stacked histogram of the predictions
xrange = seq(0,1,0.01)
# 3) subset your vectors to be inside xrange
g1 = subset(X,Tr==0)
g2 = subset(X,Tr==1)
# 4) Now, use hist to compute the counts per interval
h1 = hist(g1,breaks=xrange,plot=F)$counts
h2 = hist(g2,breaks=xrange,plot=F)$counts
barplot(rbind(h1,h2),col=3:2,names.arg=xrange[-1],
legend.text=c("No aline","Aline"),space=0,las=1,main="Stacked histogram of X")
We can see we have little support between 0-0.2, and above 0.9. We’ll carry on with the knowledge that we’ll have few pairs in these probability ranges.
We have built the propensity score using logistic regression in the previous block. We now use the Matching package to match patients with a caliper size of 0.1. After matching, we’ll apply McNemar’s test for paired samples to determine if patients with and without an a-line have a difference in mortality.
library(Matching)## ##
## ## Matching (Version 4.9-2, Build Date: 2015-12-25)
## ## See http://sekhon.berkeley.edu/matching for additional documentation.
## ## Please cite software as:
## ## Jasjeet S. Sekhon. 2011. ``Multivariate and Propensity Score Matching
## ## Software with Automated Balance Optimization: The Matching package for R.''
## ## Journal of Statistical Software, 42(7): 1-52.
## ##set.seed(43770)
ps <- Match(Y=NULL, Tr=Tr, X=X, M=1, estimand='ATT', caliper=0.1, exact=FALSE, replace=FALSE);## Warning in Match(Y = NULL, Tr = Tr, X = X, M = 1, estimand = "ATT", caliper
## = 0.1, : replace==FALSE, but there are more (weighted) treated obs than
## control obs. Some treated obs will not be matched. You may want to estimate
## ATC instead.# get pairs with treatment/outcome as cols
outcome <- data.frame(aline_pt=y[ps$index.treated], match_pt=y[ps$index.control])
head(outcome)## aline_pt match_pt
## 1 0 0
## 2 0 1
## 3 0 0
## 4 0 1
## 5 1 0
## 6 0 0# mcnemar's test to see if iac related to mort (test should use matched pairs)
tab.match1 <- table(outcome$aline_pt,outcome$match_pt,dnn=c("Aline","Matched Control"))
tab.match1## Matched Control
## Aline 0 1
## 0 582 118
## 1 104 19tab.match1[1,2]/tab.match1[2,1]## [1] 1.134615paste("95% Confint", round(exp(c(log(tab.match1[2,1]/tab.match1[1,2]) - qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1]),log(tab.match1[2,1]/tab.match1[1,2]) + qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1])) ),2))## [1] "95% Confint 0.68" "95% Confint 1.15"mcnemar.test(tab.match1) # for 1-1 pairs##
## McNemar's Chi-squared test with continuity correction
##
## data: tab.match1
## McNemar's chi-squared = 0.76126, df = 1, p-value = 0.3829The above p-value, which is > 0.05, tells us that we cannot reject the null hypothesis of the aline/non-aline groups having the same mortality rate. Assuming all assumptions of our modelling process are correct, we can infer from this that the use of an indwelling arterial catheter is not associated with a mortality benefit in these patients.