-
Notifications
You must be signed in to change notification settings - Fork 1
/
RunAnalysis.R
1378 lines (1206 loc) · 53.2 KB
/
RunAnalysis.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
source(here("Functions", "Functions.R"))
# link to db tables -----
person_db<-tbl(db, sql(paste0("SELECT * FROM ",
cdm_database_schema,
".person")))
observation_period_db<-tbl(db, sql(paste0("SELECT * FROM ",
cdm_database_schema,
".observation_period")))
visit_occurrence_db<-tbl(db, sql(paste0("SELECT * FROM ",
cdm_database_schema,
".visit_occurrence")))
condition_era_db<-tbl(db, sql(paste0("SELECT * FROM ",
cdm_database_schema,
".condition_era")))
drug_era_db<-tbl(db, sql(paste0("SELECT * FROM ",
cdm_database_schema,
".drug_era")))
concept_ancestor_db<-tbl(db, sql(paste0("SELECT * FROM ",
cdm_database_schema,
".concept_ancestor")))
# specifications ----
years.of.interest<-c("all")
# years.of.interest<-c("all", 2017,2018,2019)
exposure.pop.defs<-c(1,2,3)
# 1 from start of calendar year
# 2 from visit
# 3 from visit, with TAR censored at 28 days post visit
# exposure.pop.defs<-c(1) # for now just the primary cohort of interest
prior.hist.req<-c(1,2)
# 1 no restriction
# 2 prior year required
# prior.hist.req<-1 # for now, just run for the former
cond.codes<-c("434621",
"4098292",
"4125650",
"317009",
"313217",
"443392",
"201820",
"433736",
"321588",
"316866",
"4030518",
"255573",
"4182210")
cond.names<-c("autoimmune_disease",
"antiphospholipid_syndrome",
"thrombophilia",
"asthma",
"atrial_fibrillation",
"malignant_neoplastic_disease",
"diabetes_mellitus",
"obesity",
"heart_disease",
"hypertensive_disorder",
"renal_impairment",
"copd",
"dementia")
# these are the conditions we´ll extract for our table 1
drug.codes<-c("21603933",
"21603991",
"21602722",
"21600961",
"21601853",
"21601386",
"21602472",
"21603831",
"21602471")
drug.names<-c("antiinflamatory_and_antirheumatic",
"coxibs",
"corticosteroids",
"antithrombotic",
"lipid_modifying",
"antineoplastic_immunomodulating",
"hormonal_contraceptives",
"tamoxifen",
"sex_hormones_modulators")
# these are the medications we´ll extract for our table 1
# instantiate outcome tables -----
cohort.sql<-list.files(here("OutcomeCohorts", "sql"))
cohort.sql<-cohort.sql[cohort.sql!="CreateCohortTable.sql"]
outcome.cohorts<-tibble(id=1:length(cohort.sql),
file=cohort.sql,
name=str_replace(cohort.sql, ".sql", ""))
if(create.outcome.cohorts=="FALSE"){
print(paste0("- Skipping creating outcome cohorts"))
} else {
print(paste0("- Getting outcomes"))
conn <- connect(connectionDetails)
# create empty cohorts table
print(paste0("Create empty cohort table"))
sql<-readSql(here("OutcomeCohorts", "sql","CreateCohortTable.sql"))
sql<-SqlRender::translate(sql, targetDialect = targetDialect)
renderTranslateExecuteSql(conn=conn,
sql,
cohort_database_schema = results_database_schema,
cohort_table = cohortTableOutcomes)
rm(sql)
for(cohort.i in 1:length(outcome.cohorts$id)){
working.id<-outcome.cohorts$id[cohort.i]
print(paste0("- Getting outcome: ",
outcome.cohorts$name[cohort.i],
" (", cohort.i, " of ", length(outcome.cohorts$name), ")"))
sql<-readSql(here("OutcomeCohorts", "sql",
outcome.cohorts$file[cohort.i]))
sql <- sub("BEGIN: Inclusion Impact Analysis - event.*END: Inclusion Impact Analysis - person", "", sql)
sql<-SqlRender::translate(sql, targetDialect = targetDialect)
renderTranslateExecuteSql(conn=conn,
sql,
cdm_database_schema = cdm_database_schema,
vocabulary_database_schema = vocabulary_database_schema,
target_database_schema = results_database_schema,
# results_database_schema = results_database_schema,
target_cohort_table = cohortTableOutcomes,
target_cohort_id = working.id)
}
disconnect(conn)
}
# link
outcome_db<-tbl(db, sql(paste0("SELECT * FROM ",
results_database_schema,
".", cohortTableOutcomes)))
# drop any outcome cohorts with less than 5 people
outcome.cohorts<-outcome.cohorts %>%
inner_join(outcome_db %>%
group_by(cohort_definition_id) %>%
tally() %>%
collect() %>%
filter(n>5) %>%
select(cohort_definition_id),
by=c("id"="cohort_definition_id"))
# all those instantiated from outcome diagnostics
# specify which events we´ll also combine with thrombocytopenia ------
outcome.cohorts.thromb.10_10<-outcome.cohorts %>%
filter(name %in%
c("all stroke","CVST","DIC",
"DVT broad","DVT narrow",
"VTE broad", "VTE narrow",
"PE", "hem stroke",
"hepatic vein" ,"intest infarc",
"isc stroke", "IVT", "MACE",
"MI isc stroke", "PE",
"portal vein","splenic infarc",
"SVT" , "visc venous","VTE broad" ,"VTE narrow",
"splenic vein", "splenic artery",
"splenic infarc", "hepatic artery",
"intest infarc",
"mesen vein", "CAT"))
outcome.cohorts.thromb.10_10$name<-paste0(outcome.cohorts.thromb.10_10$name, " (with thrombocytopenia 10 days pre to 10 days post)")
outcome.cohorts.thromb.42_14<-outcome.cohorts %>%
filter(name %in%
c("all stroke","CVST","DIC",
"DVT broad","DVT narrow",
"VTE broad", "VTE narrow",
"PE", "hem stroke",
"hepatic vein" ,"intest infarc",
"isc stroke", "IVT", "MACE",
"MI isc stroke", "PE",
"portal vein","splenic infarc",
"SVT" , "visc venous","VTE broad" ,"VTE narrow",
"splenic vein", "splenic artery",
"splenic infarc", "hepatic artery",
"intest infarc",
"mesen vein", "CAT"))
outcome.cohorts.thromb.42_14$name<-paste0(outcome.cohorts.thromb.42_14$name, " (with thrombocytopenia 42 days pre to 14 days post)")
# add to outcomes
outcome.cohorts<-bind_rows(outcome.cohorts,
outcome.cohorts.thromb.10_10,
outcome.cohorts.thromb.42_14)
# run analysis ----
# Initiate lists to store output
Patient.characteristcis<-list()
Patient.characteristcis.for.plotting<-list()
IR.summary<-list()
for(i in 1:length(years.of.interest)){
for(e in 1:length(exposure.pop.defs)){ # type of exposure pop
for(o in 1:length(prior.hist.req)){ # with and without requirement for year of prior history
print(paste0("- Getting year: ", years.of.interest[i]))
working.exposure.pop.def<- exposure.pop.defs[[e]]
if(working.exposure.pop.def==1){
pop.type<-"general.pop.all"
# i.e. we go from the start of the year for all
}
if(working.exposure.pop.def==2){
pop.type<-"general.pop.with.visit"
# here we go from the first observed visit
}
if(working.exposure.pop.def==3){
pop.type<-"general.pop.with.visit.28.days"
# here we go from the first observed visit, and then just follow up for 28 days
}
# set working.year
if(years.of.interest[i]=="all"){
# for all, we start in 2017
working.year<-2017
working.start.date<-as.Date(dmy(paste0("01-01-","2017")))
} else {
working.year<-as.numeric(years.of.interest[i])
working.start.date<-as.Date(dmy(paste0("01-01-",working.year)))
}
#set end date
if(years.of.interest[i]=="all"){
end.date<-as.Date(dmy(paste0("31-12-","2019")))
}
if(years.of.interest[i]=="2017"){
end.date<-as.Date(dmy(paste0("31-12-","2017")))
}
if(years.of.interest[i]=="2018"){
end.date<-as.Date(dmy(paste0("31-12-","2018")))
}
if(years.of.interest[i]=="2019"){
end.date<-as.Date(dmy(paste0("31-12-","2019")))
}
# study population -----
# define our study population using the sql in the Cohorts folder
# the accompanying folder has the jsons (which can be imported into Atlas)
# The population are identified and added to the cohortTableExposures table
# connect to database
conn <- connect(connectionDetails)
# create empty cohorts table
print(paste0("Create empty cohort table"))
sql<-readSql(here("ExposureCohorts", "sql","CreateCohortTable.sql"))
sql<-SqlRender::translate(sql, targetDialect = targetDialect)
renderTranslateExecuteSql(conn=conn,
sql,
cohort_database_schema = results_database_schema,
cohort_table = cohortTableExposures)
rm(sql)
if(working.year==2017){
if(working.exposure.pop.def==1){
sql<-readSql(here("ExposureCohorts", "sql","general_pop_2017.sql"))
}
if(working.exposure.pop.def %in% c(2,3) & years.of.interest[i]=="all"){
sql<-readSql(here("ExposureCohorts", "sql","general_pop_visit_2017_to_2019.sql")) # to fix- max 2019
}
if(working.exposure.pop.def %in% c(2,3) & years.of.interest[i]=="2017"){
sql<-readSql(here("ExposureCohorts", "sql","general_pop_visit_2017_only.sql"))
}
sql<-SqlRender::translate(sql, targetDialect = targetDialect)
renderTranslateExecuteSql(conn=conn,
sql,
cdm_database_schema = cdm_database_schema,
target_database_schema = results_database_schema,
target_cohort_table = cohortTableExposures,
target_cohort_id = 1)
}
if(working.year==2018){
if(working.exposure.pop.def==1){
sql<-readSql(here("ExposureCohorts", "sql","general_pop_2018.sql"))
}
if(working.exposure.pop.def %in% c(2,3)){
sql<-readSql(here("ExposureCohorts", "sql","general_pop_visit_2018_only.sql"))
}
sql<-SqlRender::translate(sql, targetDialect = targetDialect)
renderTranslateExecuteSql(conn=conn,
sql,
cdm_database_schema = cdm_database_schema,
target_database_schema = results_database_schema,
target_cohort_table = cohortTableExposures,
target_cohort_id = 1)
}
if(working.year==2019){
if(working.exposure.pop.def==1){
sql<-readSql(here("ExposureCohorts", "sql","general_pop_2019.sql"))
}
if(working.exposure.pop.def %in% c(2,3)){
sql<-readSql(here("ExposureCohorts", "sql","general_pop_visit_2019_only.sql"))
}
sql<-SqlRender::translate(sql, targetDialect = targetDialect)
renderTranslateExecuteSql(conn=conn,
sql,
cdm_database_schema = cdm_database_schema,
target_database_schema = results_database_schema,
target_cohort_table = cohortTableExposures,
target_cohort_id = 1)
}
disconnect(conn)
cohortTableExposures_db<-tbl(db, sql(paste0("SELECT * FROM ",
results_database_schema,
".", cohortTableExposures)))
# Create Pop df ----
Pop<-person_db %>%
inner_join(cohortTableExposures_db %>%
select(subject_id,cohort_start_date) %>%
rename("person_id"="subject_id")) %>%
select(person_id,gender_concept_id,
year_of_birth, month_of_birth, day_of_birth,
cohort_start_date) %>%
left_join(observation_period_db %>%
select("person_id", "observation_period_start_date", "observation_period_end_date")) %>%
collect() %>%
mutate(cohort_start_date=as.Date(cohort_start_date)) %>%
mutate(observation_period_start_date=as.Date(observation_period_start_date)) %>%
mutate(observation_period_end_date=as.Date(observation_period_end_date))
# add age and gender -----
Pop$age<- NA
if(sum(is.na(Pop$day_of_birth))==0 & sum(is.na(Pop$month_of_birth))==0){
# if we have day and month
Pop<-Pop %>%
mutate(age=floor(as.numeric((ymd(cohort_start_date)-
ymd(paste(year_of_birth,
month_of_birth,
day_of_birth, sep="-"))))/365.25))
} else {
Pop<-Pop %>%
mutate(age= year(cohort_start_date)-year_of_birth)
}
Pop<-Pop %>%
mutate(age_gr=ifelse(age<20, "<20",
ifelse(age>=20 & age<=44, "20-44",
ifelse(age>=45 & age<=54, "45-54",
ifelse(age>=55 & age<=64, "55-64",
ifelse(age>=65 & age<=74, "65-74",
ifelse(age>=75 & age<=84, "75-84",
ifelse(age>=85, ">=85",
NA)))))))) %>%
mutate(age_gr= factor(age_gr,
levels = c("<20", "20-44","45-54", "55-64",
"65-74", "75-84",">=85")))
# wider age groups
Pop<-Pop %>%
mutate(age_gr2=ifelse(age<=44, "<=44",
ifelse(age>=45 & age<=64, "45-64",
ifelse(age>=55, ">=65",
NA)))) %>%
mutate(age_gr2= factor(age_gr2,
levels = c("<=44", "45-64",">=65")))
# another alternative set of age groups
Pop<-Pop %>%
mutate(age_gr3=ifelse(age<20, "<20",
ifelse(age>=20 & age<=29, "20-29",
ifelse(age>=30 & age<=39, "30-39",
ifelse(age>=40 & age<=49, "40-49",
ifelse(age>=50 & age<=59, "50-59",
ifelse(age>=60 & age<=69, "60-69",
ifelse(age>=70 & age<=79, "70-79",
ifelse(age>=80, ">=80",
NA))))))))) %>%
mutate(age_gr3= factor(age_gr3,
levels = c("<20", "20-29","30-39","40-49", "50-59",
"60-69", "70-79",">=80")))
# gender
#8507 male
#8532 female
Pop<-Pop %>%
mutate(gender= ifelse(gender_concept_id==8507, "Male",
ifelse(gender_concept_id==8532, "Female", NA ))) %>%
mutate(gender= factor(gender,
levels = c("Male", "Female")))
# if missing age or gender, drop
Pop<-Pop %>%
filter(!is.na(age))
Pop<-Pop %>%
filter(!is.na(gender))
# add prior observation time -----
Pop<-Pop %>%
mutate(prior_obs_days=as.numeric(difftime(cohort_start_date,
observation_period_start_date,
units="days"))) %>%
mutate(prior_obs_years=prior_obs_days/365.25)
if(o==1){
print(paste0("- Without requirement for prior observation time"))
} else {
print(paste0("- With requirement for prior observation time"))
Pop<-Pop %>%
filter(prior_obs_years>=1)
}
# condition history ------
# add the concept ids of interest to the cohortTableProfiles table in the results
# schema in the cdm
# these will be the code of interest and all of its descendants
# get any instances over prior history
# for everyone in the database (everyone in the condition_occurrence table),
# left_join to population of interest we´ve established
print(paste0("-- Getting codes for conditions"))
conn<-connect(connectionDetails)
# table with all the concept ids of interest (code and descendants)
insertTable(connection=conn,
tableName=paste0(results_database_schema, ".",cohortTableProfiles),
data=data.frame(condition_id=integer(), concept_id =integer()),
createTable = TRUE,
progressBar=FALSE)
for(n in 1:length(cond.codes)){ # add codes for each condition
working.code<-cond.codes[n]
working.name<-cond.names[n]
sql<-paste0("INSERT INTO ", results_database_schema, ".",cohortTableProfiles, " (condition_id, concept_id) SELECT DISTINCT ", n ,", descendant_concept_id FROM ", cdm_database_schema, ".concept_ancestor WHERE ancestor_concept_id IN (",working.code, ");")
suppressMessages(executeSql(conn, sql, progressBar = FALSE))
}
#link to table
cohortTableProfiles_db<-tbl(db, sql(paste0("SELECT * FROM ",
results_database_schema,
".", cohortTableProfiles)))
print(paste0("-- Getting conditions for study population"))
#people with at least one of the conditions
cond.persons <- condition_era_db %>%
select(person_id, condition_concept_id, condition_era_start_date) %>%
inner_join(cohortTableProfiles_db ,
by=c("condition_concept_id"="concept_id")) %>%
inner_join(cohortTableExposures_db %>%
select(subject_id, cohort_start_date) %>%
rename("person_id"="subject_id"),
by = "person_id") %>%
filter(condition_era_start_date < cohort_start_date) %>%
select(person_id, condition_id) %>%
distinct() %>%
collect()
# add each condition to pop
for(n in 1:length(cond.codes)){# add each to Pop
working.code<-cond.codes[n]
working.name<-cond.names[n]
working.persons <- cond.persons %>%
filter(condition_id==n) %>%
select(person_id) %>%
mutate(working.cond=1)
if(nrow(working.persons)>0){
Pop<-Pop %>%
left_join(working.persons,
by = "person_id") %>%
rename(!!working.name:="working.cond")
} else {
Pop$working.cond<-0
Pop<-Pop %>%
rename(!!working.name:="working.cond")
}
}
disconnect(conn)
#to zero if absent
Pop<-Pop %>%
mutate(across(all_of(cond.names), ~ replace_na(.x, 0)))
# medication history ----
# 183 days prior to four days prior index date
print(paste0("-- Getting codes for medications"))
conn<-connect(connectionDetails)
# table with all the concept ids of interest (code and descendants)
insertTable(connection=conn,
tableName=paste0(results_database_schema, ".",cohortTableProfiles),
data=data.frame(drug_id=integer(), concept_id =integer()),
createTable = TRUE,
progressBar=FALSE)
for(n in 1:length(drug.codes)){ # add codes for each condition
working.code<-drug.codes[n]
working.name<-drug.names[n]
sql<-paste0("INSERT INTO ", results_database_schema, ".",cohortTableProfiles, " (drug_id, concept_id) SELECT DISTINCT ", n ,", descendant_concept_id FROM ", cdm_database_schema, ".concept_ancestor WHERE ancestor_concept_id IN (",working.code, ");")
suppressMessages(executeSql(conn, sql, progressBar = FALSE))
}
#link to table
cohortTableProfiles_db<-tbl(db, sql(paste0("SELECT * FROM ",
results_database_schema,
".", cohortTableProfiles)))
print(paste0("-- Getting medications for study population"))
med.persons <- drug_era_db %>%
filter(drug_era_start_date>as.Date("2015-01-01")) %>%
select(person_id, drug_concept_id, drug_era_start_date, drug_era_end_date) %>%
inner_join(cohortTableProfiles_db ,
by=c("drug_concept_id"="concept_id")) %>%
inner_join(cohortTableExposures_db %>%
select(subject_id, cohort_start_date) %>%
rename("person_id"="subject_id"),
by = "person_id") %>%
filter(drug_era_start_date < cohort_start_date) %>%
select(person_id, drug_id, drug_era_start_date, drug_era_end_date,
cohort_start_date) %>%
distinct() %>%
collect() %>%
filter(as.Date(drug_era_start_date)<=(as.Date(cohort_start_date)-days(4))
& as.Date(drug_era_start_date)>=(as.Date(cohort_start_date)-days(183)) |
as.Date(drug_era_end_date)<=(as.Date(cohort_start_date)-days(4))
& as.Date(drug_era_end_date)>=(as.Date(cohort_start_date)-days(183))) %>%
select(person_id, drug_id) %>%
distinct()
for(n in 1:length(drug.codes)){# extract condition info and add to Pop
working.code<-drug.codes[n]
working.name<-drug.names[n]
working.persons <- med.persons %>%
filter(drug_id==n) %>%
select(person_id) %>%
mutate(working.drug=1)
if(nrow(working.persons)>0){
Pop<-Pop %>%
left_join(working.persons,
by = "person_id") %>%
rename(!!working.name:="working.drug")
} else {
Pop$working.drug<-0
Pop<-Pop %>%
rename(!!working.name:="working.drug")
}
}
disconnect(conn)
#to zero if absent
Pop<-Pop %>%
mutate(across(all_of(drug.names), ~ replace_na(.x, 0)))
# summarise characteristics -----
summary.characteristics<-bind_rows(
data.frame(Overall=t(Pop %>%
mutate(index_year=year(cohort_start_date)) %>%
summarise(n=nice.num.count(length(person_id)),
age=paste0(nice.num.count(median(age)), " [",
nice.num.count(quantile(age,probs=0.25)), " to ",
nice.num.count(quantile(age,probs=0.75)), "]" ),
sex.male=paste0(nice.num.count(sum(gender=="Male")),
" (", nice.num((sum(gender=="Male")/length(person_id))*100),
"%)"),
prior_obs_years=paste0(nice.num(median(prior_obs_years)),
" [", nice.num(quantile(prior_obs_years,probs=0.25)),
" to ", nice.num(quantile(prior_obs_years,probs=0.75)), "]" ),
index.year_2017= paste0(nice.num.count(sum(index_year==2017)),
" (", nice.num((sum(index_year==2017)/length(person_id))*100), "%)"),
index.year_2018= paste0(nice.num.count(sum(index_year==2018)),
" (", nice.num((sum(index_year==2018)/length(person_id))*100), "%)"),
index.year_2019= paste0(nice.num.count(sum(index_year==2019)),
" (", nice.num((sum(index_year==2019)/length(person_id))*100), "%)"))
)),
# and all the conds and medications
data.frame(Overall=t(Pop %>%
summarise_at(.vars = all_of(c(cond.names, drug.names)),
.funs = function(x, tot){
paste0(nice.num.count(sum(x)), " (", nice.num((sum(x)/tot)*100), "%)")
} , tot=nrow(Pop))) ))
summary.characteristics$Overall<-as.character(summary.characteristics$Overall)
rownames(summary.characteristics)<-str_to_sentence(rownames(summary.characteristics))
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics),
"Sex.male", "Sex: Male")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics),
"Prior_obs_years", "Years of prior observation time")
rownames(summary.characteristics)<-str_replace(rownames(summary.characteristics),
"Index.year_", "Index year: ")
rownames(summary.characteristics)<-str_replace_all(rownames(summary.characteristics) , "_", " ")
#obscure any counts less than 5
summary.characteristics$Overall<-
ifelse(str_sub(summary.characteristics$Overall, 1, 2) %in% c("1 ","2 ", "3 ","4 "),
"<5",summary.characteristics$Overall)
summary.characteristics$var<-row.names(summary.characteristics)
row.names(summary.characteristics)<-1:nrow(summary.characteristics)
Pop.summary.characteristics<-summary.characteristics
Patient.characteristcis.for.plotting[[paste0("exposure population",";",years.of.interest[i],";",o,";",pop.type,";","age_gr")]]<- Pop %>%
group_by(age_gr) %>%
tally() %>%
mutate(group="exposure population") %>%
mutate(type="age_gr") %>%
mutate(study.year=years.of.interest[i]) %>%
mutate(prior.obs.required=ifelse(o==1, "No", "Yes")) %>%
mutate(pop.type=pop.type)
Patient.characteristcis.for.plotting[[paste0("exposure population",";",years.of.interest[i],";",o,";",pop.type,";","age_gr_gender")]]<- Pop %>%
group_by(age_gr,gender) %>%
tally() %>%
mutate(group="exposure population") %>%
mutate(type="age_gr_gender") %>%
mutate(study.year=years.of.interest[i]) %>%
mutate(prior.obs.required=ifelse(o==1, "No", "Yes")) %>%
mutate(pop.type=pop.type)
Patient.characteristcis.for.plotting[[paste0("exposure population",";",years.of.interest[i],";",o,";",pop.type,";","age_gr2")]]<- Pop %>%
group_by(age_gr2) %>%
tally() %>%
mutate(group="exposure population") %>%
mutate(type="age_gr2") %>%
mutate(study.year=years.of.interest[i]) %>%
mutate(prior.obs.required=ifelse(o==1, "No", "Yes")) %>%
mutate(pop.type=pop.type)
Patient.characteristcis.for.plotting[[paste0("exposure population",";",years.of.interest[i],";",o,";",pop.type,";","age_gr2_gender")]]<- Pop %>%
group_by(age_gr2,gender) %>%
tally() %>%
mutate(group="exposure population") %>%
mutate(type="age_gr2_gender") %>%
mutate(study.year=years.of.interest[i]) %>%
mutate(prior.obs.required=ifelse(o==1, "No", "Yes")) %>%
mutate(pop.type=pop.type)
Patient.characteristcis.for.plotting[[paste0("exposure population",";",years.of.interest[i],";",o,";",pop.type,";","age_gr3")]]<- Pop %>%
group_by(age_gr3) %>%
tally() %>%
mutate(group="exposure population") %>%
mutate(type="age_gr3") %>%
mutate(study.year=years.of.interest[i]) %>%
mutate(prior.obs.required=ifelse(o==1, "No", "Yes")) %>%
mutate(pop.type=pop.type)
Patient.characteristcis.for.plotting[[paste0("exposure population",";",years.of.interest[i],";",o,";",pop.type,";","age_gr3_gender")]]<- Pop %>%
group_by(age_gr3,gender) %>%
tally() %>%
mutate(group="exposure population") %>%
mutate(type="age_gr3_gender") %>%
mutate(study.year=years.of.interest[i]) %>%
mutate(prior.obs.required=ifelse(o==1, "No", "Yes")) %>%
mutate(pop.type=pop.type)
Patient.characteristcis.for.plotting[[paste0("exposure population",";",years.of.interest[i],";",o,";",pop.type,";","gender")]]<- Pop %>%
group_by(gender) %>%
tally() %>%
mutate(group="exposure population") %>%
mutate(type="gender") %>%
mutate(study.year=years.of.interest[i]) %>%
mutate(prior.obs.required=ifelse(o==1, "No", "Yes")) %>%
mutate(pop.type=pop.type)
# working outcome -----
for(j in 1:length(outcome.cohorts$id)){ # for each outcome of interest
working.outcome<-outcome.cohorts$id[j]
working.outcome.name<-outcome.cohorts$name[j]
print(paste0("- Getting ", working.outcome.name,
" (", j, " of ", length(outcome.cohorts$id), ")"))
working.Pop<-Pop
# drop time-varying covariates, we will get them again for the index date of the event
working.Pop<-working.Pop %>%
select(-age, age_gr, age_gr2, age_gr2, prior_obs_years) %>%
select(-all_of(cond.names)) %>%
select(-all_of(drug.names))
# event of interest ------
if(working.outcome.name=="imm throm"){
# for imm throm, either this specific cohort based on diagnosis codes or HIT (which included drug eras)
ids<-c(outcome.cohorts %>%
filter(name=="HIT") %>%
select(id) %>% pull(),
working.outcome)
working.outcomes<-outcome_db %>%
filter(cohort_definition_id %in%
ids) %>%
select(subject_id, cohort_start_date) %>%
collect() %>%
mutate(cohort_start_date=as.Date(cohort_start_date))
} else {
working.outcomes<-outcome_db %>%
filter(cohort_definition_id %in% working.outcome) %>%
select(subject_id, cohort_start_date) %>%
collect() %>%
mutate(cohort_start_date=as.Date(cohort_start_date))
}
# thrombocytopenia window ----
if(str_detect(working.outcome.name, "(with thrombocytopenia 10 days pre to 10 days post)")){
thrombocyt.id<-outcome.cohorts %>%
filter(name=="thrombocyt") %>%
select(id) %>% pull()
thromb.outcomes<-outcome_db %>%
filter(cohort_definition_id ==thrombocyt.id) %>%
select(subject_id, cohort_start_date) %>%
rename("thromb.date"="cohort_start_date") %>%
collect()
# find any outcomes with thrombocytopenia also observed
working.outcomes<-working.outcomes %>%
inner_join(thromb.outcomes)
# find any outcomes with thrombocytopenia in the time window
working.outcomes$dtime<-as.numeric(difftime(as.Date(working.outcomes$thromb.date),
as.Date(working.outcomes$cohort_start_date), units="days"))
working.outcomes<-working.outcomes %>%
filter(dtime>=(-10)) %>%
filter(dtime<=10)
working.outcomes<-working.outcomes %>%
select(-dtime) %>%
select(-thromb.date)
}
if(str_detect(working.outcome.name, "(with thrombocytopenia 42 days pre to 14 days post)")){
thrombocyt.id<-outcome.cohorts %>%
filter(name=="thrombocyt") %>%
select(id) %>% pull()
thromb.outcomes<-outcome_db %>%
filter(cohort_definition_id ==thrombocyt.id) %>%
select(subject_id, cohort_start_date) %>%
rename("thromb.date"="cohort_start_date") %>%
collect()
# find any outcomes with thrombocytopenia also observed
working.outcomes<-working.outcomes %>%
inner_join(thromb.outcomes)
# find any outcomes with thrombocytopenia in the time window
working.outcomes$dtime<-as.numeric(difftime(as.Date(working.outcomes$thromb.date),
as.Date(working.outcomes$cohort_start_date), units="days"))
working.outcomes<-working.outcomes %>%
filter(dtime>=(-42)) %>%
filter(dtime<=14)
working.outcomes<-working.outcomes %>%
select(-dtime) %>%
select(-thromb.date)
}
# drop anyone with the outcome in the year prior to the index ------
washout.outcomes<-working.outcomes %>%
inner_join(working.Pop %>%
select(person_id,cohort_start_date) %>%
rename("subject_id"="person_id") %>%
rename("Pop_cohort_start_date"="cohort_start_date")) %>%
filter(as.Date(cohort_start_date)>= (as.Date(Pop_cohort_start_date)-years(1))) %>%
filter(as.Date(cohort_start_date)<= as.Date(Pop_cohort_start_date))
working.Pop<-working.Pop %>%
anti_join(washout.outcomes %>%
select(subject_id) %>%
distinct(),
by=c("person_id"="subject_id"))
# history of outcome event -----
history.outcomes<-working.outcomes %>%
inner_join(working.Pop %>%
select(person_id,cohort_start_date) %>%
rename("subject_id"="person_id") %>%
rename("Pop_cohort_start_date"="cohort_start_date")) %>%
filter(as.Date(cohort_start_date)< (as.Date(Pop_cohort_start_date)-years(1)))
working.Pop<-working.Pop %>%
left_join(history.outcomes %>%
select(subject_id) %>%
distinct() %>%
mutate(history_outcome=1),
by=c("person_id"="subject_id"))
working.Pop<-working.Pop %>%
mutate(history_outcome=ifelse(is.na(history_outcome),0,1))
# first event after index date up to -----
# where e==1 or e==2
# where years.of.interest is "all", we want all four years of potential follow
# where it is a specific year, just that year of follow up
# see end.date defined above
if(working.exposure.pop.def==1 | working.exposure.pop.def==2 ){
f_u.outcome<-working.outcomes %>%
inner_join(working.Pop %>%
select(person_id,cohort_start_date) %>%
rename("subject_id"="person_id") %>%
rename("Pop_cohort_start_date"="cohort_start_date")) %>%
filter(as.Date(cohort_start_date)> as.Date(Pop_cohort_start_date)) %>%
filter(as.Date(cohort_start_date)<= end.date)}
# where e==3
# just up to 28 days
if(working.exposure.pop.def==3){
f_u.outcome<-working.outcomes %>%
inner_join(working.Pop %>%
select(person_id,cohort_start_date) %>%
rename("subject_id"="person_id") %>%
rename("Pop_cohort_start_date"="cohort_start_date")) %>%
filter(as.Date(cohort_start_date)> as.Date(Pop_cohort_start_date)) %>%
filter(as.Date(cohort_start_date)<= (as.Date(Pop_cohort_start_date)+days(28)))
}
if(nrow(f_u.outcome)>=5){ # 5 or more cases
f_u.outcome<-f_u.outcome %>%
group_by(subject_id) %>%
arrange(cohort_start_date) %>%
mutate(seq=1:length(subject_id)) %>%
filter(seq==1) %>%
select(subject_id,cohort_start_date) %>%
rename("f_u.outcome_date"="cohort_start_date") %>%
mutate(f_u.outcome=1)
working.Pop<-working.Pop %>%
left_join(f_u.outcome,
by=c("person_id"="subject_id"))
working.Pop<-working.Pop %>%
mutate(f_u.outcome=ifelse(is.na(f_u.outcome),0,1))
# TAR -----
# censor at first of outcome, end of observation period, one year
# if event, date of event
# if no event, censor at tar.end.date or end of observation period, whichever comes first
if(working.exposure.pop.def==1 | working.exposure.pop.def==2 ){
working.Pop<-working.Pop %>%
mutate(f_u.outcome_date=if_else(f_u.outcome==1,
f_u.outcome_date,
if_else(observation_period_end_date < end.date,
observation_period_end_date,
end.date )))
working.Pop<-working.Pop %>%
mutate(f_u.outcome.days=as.numeric(difftime(f_u.outcome_date,
working.start.date,
units="days")))
}
if(working.exposure.pop.def==3){
working.Pop<-working.Pop %>%
mutate(f_u.outcome_date=if_else(f_u.outcome==1,
f_u.outcome_date,
if_else(observation_period_end_date < (cohort_start_date+days(28)),
observation_period_end_date,
(cohort_start_date+days(28)) )))
working.Pop<-working.Pop %>%
mutate(f_u.outcome.days=as.numeric(difftime(f_u.outcome_date,
cohort_start_date,
units="days")))
}
# characteristics ----
working.Pop.w.outcome<-working.Pop %>% filter(f_u.outcome==1)
# add age and gender -----
working.Pop.w.outcome$age<- NA
if(sum(is.na(working.Pop.w.outcome$day_of_birth))==0 & sum(is.na(working.Pop.w.outcome$month_of_birth))==0){
# if we have day and month
working.Pop.w.outcome<-working.Pop.w.outcome %>%
mutate(age=floor(as.numeric((ymd(cohort_start_date)-
ymd(paste(year_of_birth,
month_of_birth,
day_of_birth, sep="-"))))/365.25))
} else {
working.Pop.w.outcome<-working.Pop.w.outcome %>%
mutate(age= year(cohort_start_date)-year_of_birth)
}
working.Pop.w.outcome<-working.Pop.w.outcome %>%
mutate(age_gr=ifelse(age<20, "<20",
ifelse(age>=20 & age<=44, "20-44",
ifelse(age>=45 & age<=54, "45-54",
ifelse(age>=55 & age<=64, "55-64",
ifelse(age>=65 & age<=74, "65-74",
ifelse(age>=75 & age<=84, "75-84",
ifelse(age>=85, ">=85",
NA)))))))) %>%
mutate(age_gr= factor(age_gr,
levels = c("<20", "20-44","45-54", "55-64",
"65-74", "75-84",">=85")))
# wider age groups
working.Pop.w.outcome<-working.Pop.w.outcome %>%
mutate(age_gr2=ifelse(age<=44, "<=44",
ifelse(age>=45 & age<=64, "45-64",
ifelse(age>=55, ">=65",
NA)))) %>%
mutate(age_gr2= factor(age_gr2,
levels = c("<=44", "45-64",">=65")))
# another alternative set of age groups
working.Pop.w.outcome<-working.Pop.w.outcome %>%
mutate(age_gr3=ifelse(age<20, "<20",
ifelse(age>=20 & age<=29, "20-29",
ifelse(age>=30 & age<=39, "30-39",
ifelse(age>=40 & age<=49, "40-49",
ifelse(age>=50 & age<=59, "50-59",
ifelse(age>=60 & age<=69, "60-69",
ifelse(age>=70 & age<=79, "70-79",
ifelse(age>=80, ">=80",
NA))))))))) %>%
mutate(age_gr3= factor(age_gr3,
levels = c("<20", "20-29","30-39","40-49", "50-59",
"60-69", "70-79",">=80")))
# add prior observation time -----
working.Pop.w.outcome<-working.Pop.w.outcome %>%
mutate(prior_obs_days=as.numeric(difftime(f_u.outcome_date,
observation_period_start_date,
units="days"))) %>%
mutate(prior_obs_years=prior_obs_days/365.25)
# condition history -----
print(paste0("-- Getting codes for conditions"))
conn<-connect(connectionDetails)
# table with all the concept ids of interest (code and descendants)
insertTable(connection=conn,
tableName=paste0(results_database_schema, ".",cohortTableProfiles),
data=data.frame(condition_id=integer(), concept_id =integer()),
createTable = TRUE,
progressBar=FALSE)
for(n in 1:length(cond.codes)){ # add codes for each condition
working.code<-cond.codes[n]
working.name<-cond.names[n]
sql<-paste0("INSERT INTO ", results_database_schema, ".",cohortTableProfiles, " (condition_id, concept_id) SELECT DISTINCT ", n ,", descendant_concept_id FROM ", cdm_database_schema, ".concept_ancestor WHERE ancestor_concept_id IN (",working.code, ");")
suppressMessages(executeSql(conn, sql, progressBar = FALSE))
}
#link to table
cohortTableProfiles_db<-tbl(db, sql(paste0("SELECT * FROM ",
results_database_schema,
".", cohortTableProfiles)))
print(paste0("-- Getting conditions"))
cond.persons <- outcome_db %>%
filter(cohort_definition_id==working.outcome) %>%
select(subject_id, cohort_start_date) %>%
distinct() %>%
left_join(condition_era_db,
by=c("subject_id"="person_id")
) %>%
rename("person_id"="subject_id") %>%
select(person_id, condition_concept_id, condition_era_start_date) %>%
inner_join(cohortTableProfiles_db ,
by=c("condition_concept_id"="concept_id")) %>%
inner_join(cohortTableExposures_db %>%
select(subject_id, cohort_start_date) %>%
rename("person_id"="subject_id"),
by = "person_id") %>%
filter(condition_era_start_date < cohort_start_date) %>%
select(person_id, condition_id) %>%
distinct() %>%
collect()
for(n in 1:length(cond.codes)){# extract condition info and add to working.Pop.w.outcome
working.code<-cond.codes[n]
working.name<-cond.names[n]
working.persons <- cond.persons %>%
filter(condition_id==n) %>%
select(person_id) %>%
mutate(working.cond=1)
if(nrow(working.persons)>0){
working.Pop.w.outcome<-working.Pop.w.outcome %>%
left_join(working.persons,
by = "person_id") %>%
rename(!!working.name:="working.cond")
} else {
working.Pop.w.outcome$working.cond<-0
working.Pop.w.outcome<-working.Pop.w.outcome %>%
rename(!!working.name:="working.cond")
}
}
disconnect(conn)
#to zero if absent