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cox.py
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cox.py
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import numpy as np
from datetime import timedelta
import pandas as pd
import os
from tqdm.auto import tqdm
from naming_scheme import type_and_suffix
import icd_data_process
def interval_process(df,total):
df['interval']=df['HR'].apply(lambda x: "{:.2f}".format(x))+'('+\
df['lower95'].apply(lambda x: "{:.2f}".format(x))+'-'+\
df['upper95'].apply(lambda x: "{:.2f}".format(x))+')'
if total==True:
df['frequency']=df['frequency'].fillna(0).astype(int).astype(str)+'/'+\
df['total'].fillna(0).astype(int).astype(str)
else:df['frequency']=df['frequency'].fillna(0).astype(int).astype(str)
def generate_table(df_main,df_0,df_1,total=False):
interval_process(df_main,total)
interval_process(df_0,total)
interval_process(df_1,total)
return df_main[['frequency','interval']]\
.join(df_0[['frequency','interval']],rsuffix='_0')\
.join(df_1[['frequency','interval']],rsuffix='_1')
def insert(df,i,df_add):
df1 = df.iloc[:i, :]
df2 = df.iloc[i:, :]
df = pd.concat([df1, df_add, df2])
return df
def get_disease_days(days, disease_name, base,frequency_count,fill_value,cohort,frequency):
patients=days.dt.days>0
fill=days.isna()
if((patients&cohort).sum() > frequency):
frequency_count.loc[disease_name,'frequency']=(patients&cohort).sum()
base[disease_name+'_in']=(patients|fill).astype(int)
base[disease_name+'_ICD']=patients.astype(int)
days[fill]=fill_value[fill]
base[disease_name+'_days']=days.dt.days
def for_cox(obj:type_and_suffix,frequency_disease=None,frequency_death=None,):
import warnings
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
os.makedirs('cox',exist_ok=True)
if obj.subgroup!='all':
return
if os.path.exists('data_target/for_cox.pkl'):
return
group_size,(cohort,target_disease)=obj.get_cohort(range='all')
icd_data=icd_data_process.process_relative_data()
icd_data=icd_data.loc[cohort]
fill_value=icd_data['fillin']
D_death,D_disease=obj.disease_dict()
for_cox=pd.DataFrame()
if frequency_death==None:
frequency_death=int(sum(target_disease==1)*0.005)
print(f'freqency death:{frequency_death}')
disease,disease_time=\
icd_data_process.predict_illness(icd_data,disease_type='death')
frequency_count=pd.DataFrame(columns=['frequency'])
frequency_count.index.name = 'ICD'
disease_time=disease_time['40000-0.0']
for temp_disease in tqdm(D_death):
days=disease_time.where((disease==temp_disease).any(axis=1),pd.NaT)
get_disease_days(
days, temp_disease, for_cox, frequency_count, fill_value,cohort=target_disease, frequency=frequency_death)
frequency_count.sort_index().to_csv('cox/label_death_all.csv')
if frequency_disease==None:
frequency_disease=int(sum(target_disease==1)*0.01)
print(f'freqency disease:{frequency_disease}')
disease,disease_time=\
icd_data_process.predict_illness(icd_data,disease_type='disease')
frequency_count=pd.DataFrame(columns=['frequency'])
frequency_count.index.name = 'ICD'
for temp_disease in tqdm(D_disease):
days=disease_time[disease==temp_disease].min(axis=1)
get_disease_days(
days, temp_disease, for_cox, frequency_count, fill_value,cohort=target_disease, frequency=frequency_disease)
frequency_count.sort_index().to_csv('cox/label_disease_all.csv')
for_cox=for_cox.join(target_disease)
for_cox.to_pickle('data_target/for_cox.pkl')
def for_cox_sens(obj:type_and_suffix,):
import warnings
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
os.makedirs('cox',exist_ok=True)
if os.path.exists('data_target/for_cox_sens.pkl'):
return
group_size,(cohort,target_disease)=obj.get_cohort(range='all')
icd_data=icd_data_process.process_relative_data()
icd_data=icd_data.loc[cohort]
fill_value=icd_data['fillin']
D_disease=pd.read_csv('cox/cox_result'+obj.get_cox_suffix()+'.csv',index_col=0).index
for_cox=pd.DataFrame()
disease,disease_time=\
icd_data_process.predict_illness(icd_data,disease_type='disease')
disease_death_dict=obj.disease_death_dict()
disease_chapters=disease.copy(deep=True)
disease_chapters=disease_chapters.applymap(lambda x: disease_death_dict[x] if x in disease_death_dict else np.nan)
frequency_count=pd.DataFrame(columns=['frequency'])
frequency_count.index.name = 'ICD'
for temp_disease in tqdm(D_disease):
days=disease_time[disease==temp_disease].min(axis=1)
days_exclude=disease_time[disease_chapters==disease_death_dict[temp_disease]].min(axis=1)
days=days.mask(days_exclude.dt.days<=0, days_exclude)
get_disease_days(
days, temp_disease, for_cox, frequency_count, fill_value,cohort=target_disease, frequency=0)
Death=pd.read_csv('cox/cox_result_death_all.csv',index_col=0).index
death,death_time=\
icd_data_process.predict_illness(icd_data,disease_type='death')
death_time=death_time['40000-0.0']
for D in Death:
dead=death_time.where((death==D).any(axis=1),pd.NaT)
days_exclude=disease_time[disease_chapters==D].min(axis=1)
dead=dead.mask(days_exclude.dt.days<=0, days_exclude)
get_disease_days(
dead, D, for_cox, frequency_count, fill_value,cohort=target_disease, frequency=0)
for_cox=for_cox.join(target_disease)
for_cox.to_pickle('data_target/for_cox_sens.pkl')
def cox_regression(obj:type_and_suffix,begin=None,end=None, target_disease='MAFLD',sens=False,custom=''):
from lifelines import CoxPHFitter
if sens:sens='_sens'
else: sens=''
group_size,(cohort,disease_label)=obj.get_cohort(range='all',label=target_disease)
data = pd.read_pickle('data_target/for_cox'+sens+'.pkl')
data=data.loc[cohort]
print(f'Cohort size:{group_size}')
Diseases=pd.read_csv('cox/label'+obj.get_cox_suffix()+'.csv',index_col=0)
result=pd.DataFrame(columns=['frequency','total','HR','lower95','upper95','p_value'])
result.index.name='ICD'
cph = CoxPHFitter()
for D in tqdm(Diseases.index):
try:
index=data[data[D+'_in']==True].index
cox_data=data.loc[index,[D+'_days',D+'_ICD']]
cox_data[target_disease]=disease_label.astype(bool)
if begin!=None:
cox_data.loc[:,D+'_ICD']=(cox_data[D+'_days']>begin*360)&(cox_data[D+'_ICD'])
if end != None:
cox_data.loc[:,D+'_ICD']=(cox_data[D+'_days']<= end*360)&(cox_data[D+'_ICD'])
frequency=sum(cox_data[D+'_ICD']&cox_data[target_disease])
total=sum(cox_data[target_disease])
cox=cph.fit(cox_data, duration_col=D+'_days', event_col=D+'_ICD', formula=target_disease).summary
HR=cox.loc[target_disease,'exp(coef)']
lower95=cox.loc[target_disease,'exp(coef) lower 95%']
upper95=cox.loc[target_disease,'exp(coef) upper 95%']
p_value=cox.loc[target_disease,'p']
result.loc[D]=[frequency,total,HR,lower95,upper95,p_value]
except: continue
_,D1=obj.disease_descrpition_dict()
Diseases=Diseases.drop(columns=['frequency'])
Diseases=Diseases.join(result)
Diseases['DESCRIPTION']=Diseases.index.map(lambda x : D1[x])
if (begin==None)&(end==None)&(sens=='')&(custom==''):
if (obj.subgroup=='all'):
threshold=0.05/Diseases['frequency']
Diseases=Diseases.loc[(Diseases['HR'] >= 1) & (Diseases['p_value'] < threshold), :]
Diseases.to_csv('cox/cox_result'+obj.get_suffix()+sens+custom+'.csv')
def death_cox_regression(obj:type_and_suffix,custome=''):
from lifelines import CoxPHFitter
import warnings
warnings.simplefilter(action='ignore', category=RuntimeWarning)
Death_ICD=obj.Death_cause
data = pd.read_pickle('data_target/for_cox.pkl')
group_size,(cohort,MAFLD)=obj.get_cohort(range='all')
print(f'Cohort size:{group_size}')
data=data.loc[cohort]
Diseases=pd.read_csv('cox/cox_result_disease_all.csv',index_col=0)
result=pd.DataFrame(columns=['frequency','HR','lower95','upper95','p_value'])
result.index.name='ICD'
cph = CoxPHFitter()
for D in tqdm(Diseases.index):
try:
index=data[data[D+'_in']==True].index
cox_data=data.loc[index,[Death_ICD+'_days',Death_ICD+'_ICD',D+'_ICD']]
frequency=sum(cox_data[D+'_ICD']&cox_data[Death_ICD+'_ICD']&data['MAFLD'])
cox=cph.fit(cox_data, duration_col=Death_ICD+'_days', event_col=Death_ICD+'_ICD', formula=D+'_ICD').summary
HR=cox.loc[D+'_ICD','exp(coef)']
lower95=cox.loc[D+'_ICD','exp(coef) lower 95%']
upper95=cox.loc[D+'_ICD','exp(coef) upper 95%']
p_value=cox.loc[D+'_ICD','p']
result.loc[D]=[frequency,HR,lower95,upper95,p_value]
except: result.loc[D]=[0,np.nan,np.nan,np.nan,np.nan]
D1,_=obj.disease_descrpition_dict()
Diseases=Diseases[['frequency']].drop(columns=['frequency'])
Diseases=Diseases.join(result)
Diseases['DESCRIPTION']=Diseases.index.map(lambda x : D1[x])
if obj.subgroup=='all':
threshold=0.05/Diseases['frequency']
Diseases=Diseases.loc[(Diseases['HR'] >= 1) & (Diseases['p_value'] < threshold), :]
Diseases.to_csv('cox/cox_result'+obj.get_suffix()+'.csv')
def create_table_subgroup(obj:type_and_suffix):
os.makedirs('cox_subgroup/',exist_ok=True)
writer=pd.ExcelWriter('cox_subgroup/'+obj.disease_type+'.xlsx',engine='xlsxwriter')
D1,D2=obj.subgroup_dict()
for subtask in obj.subtasks:
label=obj.subtasks[subtask]
dfs=[type_and_suffix(obj.disease_type,obj.subgroup),
type_and_suffix(obj.disease_type,subtask,0),
type_and_suffix(obj.disease_type,subtask,1)]
df_main=pd.read_csv('cox/cox_result'+dfs[0].get_cox_suffix()+'.csv',index_col=0)
df_0=pd.read_csv('cox/cox_result'+dfs[1].get_suffix()+'.csv',index_col=0)
df_1=pd.read_csv('cox/cox_result'+dfs[2].get_suffix()+'.csv',index_col=0)
overlapping=(~((df_0['lower95'] > df_1['upper95']) | (df_1['lower95'] > df_0['upper95']))).astype(int)
result=generate_table(df_main,df_0,df_1)
title= ['All (N = '+dfs[0].get_cohort()[0]+')',
label[0]+' (N = '+dfs[1].get_cohort()[0]+')',
label[1]+' (N = '+dfs[2].get_cohort()[0]+')',]
result.columns=pd.MultiIndex.from_product([title,['No.','HR (95% CI)']])
result.insert(0,'Code',result.index)
result['Medical conditions']=result.index.map(lambda x : D1[x])
result['overlapping']=overlapping
result=result.set_index('Medical conditions')
cols=result.columns
if obj.disease_type=='disease':
tag=''
offset=0
for i,(_,row) in enumerate(result.iterrows()):
i+=offset
if D2[row[('Code','')]]!=tag:
tag=D2[row[('Code','')]]
blank=pd.DataFrame([pd.Series([[''],[''],[''],[''],[''],[''],['']],)],columns=cols,index=['*'+tag+'*'])
result=insert(result,i,blank)
offset+=1
result.index.name='Medical conditions'
result.to_excel(writer, sheet_name=subtask)
worksheet = writer.sheets[subtask]
max_len =result.index.astype(str).map(len).max()
worksheet.set_column(0, 0, max_len)
for idx, col in enumerate(result):
series = result[col]
max_len =series.astype(str).map(len).max()
worksheet.set_column(idx+1, idx+1, max(max_len+1,5))
writer.close()
def create_table_nfs(obj:type_and_suffix):
os.makedirs('cox_subgroup/',exist_ok=True)
writer=pd.ExcelWriter('cox_subgroup/nfs_'+obj.disease_type+'.xlsx',engine='xlsxwriter')
D1,D2=obj.subgroup_dict()
for subtask in obj.subtasks:
df_main=pd.read_csv('cox/cox_result'+obj.get_suffix()+'.csv',index_col=0)
df_0=pd.read_csv('cox/cox_result'+obj.get_suffix()+'_NFS_below.csv',index_col=0)
df_1=pd.read_csv('cox/cox_result'+obj.get_suffix()+'_NFS_above.csv',index_col=0)
overlapping=(~((df_0['lower95'] > df_1['upper95']) | (df_1['lower95'] > df_0['upper95']))).astype(int)
result=generate_table(df_main,df_0,df_1)
title= ['All (N = '+obj.get_cohort()[0]+')',
'NFS_below'+' (N = '+obj.get_cohort(label='NFS_below')[0]+')',
'NFS_above'+' (N = '+obj.get_cohort(label='NFS_above')[0]+')',]
result.columns=pd.MultiIndex.from_product([title,['No.','HR (95% CI)']])
result.insert(0,'Code',result.index)
result['Medical conditions']=result.index.map(lambda x : D1[x])
result['overlapping']=overlapping
result=result.set_index('Medical conditions')
cols=result.columns
if obj.disease_type=='disease':
tag=''
offset=0
for i,(_,row) in enumerate(result.iterrows()):
i+=offset
if D2[row[('Code','')]]!=tag:
tag=D2[row[('Code','')]]
blank=pd.DataFrame([pd.Series([[''],[''],[''],[''],[''],[''],['']],)],columns=cols,index=['*'+tag+'*'])
result=insert(result,i,blank)
offset+=1
result.index.name='Medical conditions'
result.to_excel(writer, sheet_name=subtask)
worksheet = writer.sheets[subtask]
max_len =result.index.astype(str).map(len).max()
worksheet.set_column(0, 0, max_len)
for idx, col in enumerate(result):
series = result[col]
max_len =series.astype(str).map(len).max()
worksheet.set_column(idx+1, idx+1, max(max_len+1,5))
writer.close()
def create_table_sens(obj:type_and_suffix,sens='_sens'):
os.makedirs('cox_subgroup/',exist_ok=True)
D1,D2=obj.subgroup_dict()
df_main=pd.read_csv('cox/cox_result'+obj.get_suffix()+'.csv',index_col=0)
df_0=pd.read_csv('cox/cox_result'+obj.get_suffix()+sens+'.csv',index_col=0)
df_1=pd.read_csv('cox/cox_result'+obj.get_suffix()+sens+'.csv',index_col=0)
df_0=df_0.loc[df_main.index]
overlapping=(~((df_main['lower95'] > df_0['upper95']) | (df_0['lower95'] > df_main['upper95']))).astype(int)
result=generate_table(df_main,df_0,df_1,total=True)
title= ['Main analysis',
'Sensitivity analysis',
'Sensitivity analysis(Duplicated)',]
result.columns=pd.MultiIndex.from_product([title,['No./ N','HR (95% CI)']])
result.insert(0,'Code',result.index)
result['overlapping']=overlapping
result['Medical conditions']=result.index.map(lambda x : D1[x])
result=result.set_index('Medical conditions')
cols=result.columns
tag=''
offset=0
if obj.disease_type=='disease':
for i,(_,row) in enumerate(result.iterrows()):
i+=offset
if D2[row[('Code','')]]!=tag:
tag=D2[row[('Code','')]]
blank=pd.DataFrame([pd.Series([[''],[''],[''],[''],[''],[''],['']],)],columns=cols,index=['*'+tag+'*'])
result=insert(result,i,blank)
offset+=1
result.index.name='Medical conditions'
result=result.drop(columns=[('Sensitivity analysis(Duplicated)','No./ N'),('Sensitivity analysis(Duplicated)','HR (95% CI)')])
writer=pd.ExcelWriter('cox_subgroup/T10_'+obj.disease_type+sens+'.xlsx',engine='xlsxwriter')
result.to_excel(writer, sheet_name='Sensitivity')
worksheet = writer.sheets['Sensitivity']
max_len =result.index.astype(str).map(len).max()
worksheet.set_column(0, 0, max_len)
for idx, col in enumerate(result):
series = result[col]
max_len =series.astype(str).map(len).max()
worksheet.set_column(idx+1, idx+1, max(max_len+1,5))
writer.close()