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simple_validation_script_16_sus.py
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simple_validation_script_16_sus.py
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from lib_phenotype_validation_sus import *
############################ CONFIGURE OPTIONS HERE ################################
# Import file
input_path = "output/extract_16/input_16.feather"
# Definitions
definitions = ["ethnicity_new_16", "ethnicity_sus_16"]
# Code dictionary
code_dict = {
"imd": {
0: "Unknown",
1: "1 Most deprived",
2: "2",
3: "3",
4: "4",
5: "5 Least deprived",
},
"ethnicity_sus_16": {
1: "White_British",
2: "White_Irish",
3: "Other_White",
4: "White_and_Black_Caribbean",
5: "White_and_Black_African",
6: "White_and_Asian",
7: "Other_Mixed",
8: "Indian",
9: "Pakistani",
10: "Bangladeshi",
11: "Other_Asian",
12: "Caribbean",
13: "African",
14: "Other_Black",
15: "Chinese",
16: "Any_other_ethnic_group",
},
"ethnicity_new_16": {
1: "White_British",
2: "White_Irish",
3: "Other_White",
4: "White_and_Black_Caribbean",
5: "White_and_Black_African",
6: "White_and_Asian",
7: "Other_Mixed",
8: "Indian",
9: "Pakistani",
10: "Bangladeshi",
11: "Other_Asian",
12: "Caribbean",
13: "African",
14: "Other_Black",
15: "Chinese",
16: "Any_other_ethnic_group",
},
}
# Other variables to include
other_vars = [
"White_British",
"White_Irish",
"Other_White",
"White_and_Black_Caribbean",
"White_and_Black_African",
"White_and_Asian",
"Other_Mixed",
"Indian",
"Pakistani",
"Bangladeshi",
"Other_Asian",
"Caribbean",
"African",
"Other_Black",
"Chinese",
"Any_other_ethnic_group",
]
other_vars_combined = [x + "_" + y for x in definitions for y in other_vars]
# Restrict to registered as of index date
registered = True
reg = "fullset"
if registered == True:
reg = "registered"
# Dates
dates = False
date_min = ""
date_max = ""
time_delta = ""
# Min/max range
min_range = 4
max_range = 200
# Null value – could be multiple values in a list [0,'0',NA]
null = [0, "0"]
# Covariates
demographic_covariates = ["age_band", "sex", "region", "imd"]
clinical_covariates = ["dementia", "diabetes", "hypertension", "learning_disability"]
# Output filepath
output_path = "sus/simplified_output"
grouping = "16_group"
########################## SPECIFY ANALYSES TO RUN HERE ##############################
def main():
# combine defintions and other_vars
df_clean = import_clean(
input_path,
definitions,
other_vars_combined,
demographic_covariates,
clinical_covariates,
reg,
null,
date_min,
date_max,
time_delta,
output_path,
grouping,
code_dict,
dates=False,
registered = registered,
dates_check=False,
)
# Count patients with records
simple_patient_counts(
df_clean, definitions,reg, demographic_covariates, clinical_covariates, output_path,grouping,
)
# Count patients by categories
simple_patient_counts(
df_clean,
definitions,
reg,
demographic_covariates,
clinical_covariates,
output_path,
grouping,
categories=True,
)
# Latest v most common
simple_latest_common_comparison(
df_clean, definitions,reg, other_vars_combined, output_path,grouping,
)
simple_latest_common_comparison(
df_clean, definitions,reg, other_vars_combined, output_path,grouping, missing_check=False,
)
# State change
simple_state_change(df_clean, definitions,reg, other_vars_combined, output_path,grouping,)
# # records over time
# records_over_time_perc(
# df_clean, definitions, demographic_covariates, clinical_covariates, output_path, "",grouping,reg
# )
########################## DO NOT EDIT – RUNS SCRIPT ##############################
if __name__ == "__main__":
main()
# registered = False
# reg = "fullset"
# if __name__ == "__main__":
# main()