Major changes

New dataset

New dataset only including events happening after the inclusion (2015)

# SELECT LINES WITH EVENTS > 2015
dgn_complete.2015 <- dgn_complete %>% 
  select(evdate, inclusion, num_enq, RREC_COD_ANO, DGN_PAL) %>%
  filter(evdate > inclusion)

count(dgn_complete) # 18543
count(dgn_complete.2015) # 12678

# SORTING
dgn_one <- dgn_complete.2015 %>%
  group_by(num_enq, lubridate::date(evdate)) %>%
  arrange(evdate) %>%
  slice(1) %>%
  ungroup() %>%
  select(evdate, num_enq, DGN_PAL, DDIRT)

count(dgn_one) # 12678

# SQL COUNTING
library("sqldf")
dgn_oneclassifier <- sqldf("SELECT a.*, COUNT(*) count
       FROM dgn_one a, dgn_one b 
       WHERE a.num_enq = b.num_enq AND b.ROWID <= a.ROWID 
       GROUP BY a.ROWID"
)

# NEW DATABASE WITH ONLY THE FIRST EVENT PER PERSON
dgn_line = filter(dgn_oneclassifier, count == 1)
count(dgn_line) # 7979

Number of total events in ADPKD population

no-event    event
2255        305

Events according to transplantation status

When transplantation is taken into account, the number of total events drops to 255. Fifty events occur after the transplantation and are not accounted as events anymore in the final analysis.

Il y a la question de Clémence à laquelle je n’ai pas encore repondu dans cette analyse. En effet, les sujets ayant reçu une transplantation ne sont plus suivi dans le registre REIN et leur date de décès pourrait ne pas être disponible - alors qu’ils sont bien decedés.

            no event    event 
event       0           255
no event    2255        50

Columns refer to the variable apkd$event which does not include the information about transplantation. Rows refer to the variable includinding the information about transplantation timing.

Type of event ¹

G45  I60  I61  I62  I63  I64  I74 R471 
 18    5   18   12   30    1  219    2 

        G45         I60         I61         I62         I63 
0.059016393 0.016393443 0.059016393 0.039344262 0.098360656 
        I64         I74        R471 
0.003278689 0.718032787 0.006557377

Follow-UP

Definition of the follow-up periods

Until the end of the follow up, set as December 31st 2019

FUP.19 = as.Date(adpkd$december, "%d/%m/%Y") - as.Date(adpkd$DDIRT, "%d/%m/%Y")

The variable adpkd$december replaces the previous date des dernières nouvelles variable.

Until death or event

# DEATH
FUP.D = as.Date(adpkd$DDC, "%d/%m/%Y") - as.Date(adpkd$DDIRT, "%d/%m/%Y") 

# EVENT
FUP.E = as.Date(adpkd$evdate, "%d/%m/%Y") - as.Date(adpkd$DDIRT, "%d/%m/%Y")

Until transplatation

FUP.T = as.Date(adpkd$DGRF, "%d/%m/%Y") - as.Date(adpkd$DDIRT, "%d/%m/%Y")

Follow-up with `case_when`nested in `mutate` ²

adpkd <- adpkd %>% 
  mutate(epilogus = case_when(
    # the patient is dead
    DEATH == "1" & e.b.d == "0" ~ FUP.D, 
    # the patient is transplanted before the event
    t.b.e == "1" ~ FUP.T, 
    # the patient is transplanted after the event
    t.b.e == "0" ~ FUP.E,
    # event
    EVENT == "1" & e.b.i != "1" ~ FUP.E,
    # no event
    EVENT == "0" ~ FUP.19
  ))

Acronyms t.b.e and e.b.d stand for transplantation before the event and event before death respectively. The latter is to be considered only as an additional check on the database coherence:

# TRANSPLANTATION HAPPENS BEFORE THE EVENT
0 (after transplantation)   1 (before transplantation) 
45                          50

# EVENT HAPPENS BEFORE DEATH
0 (event after death)  1 (event before death) 
20                     26

Multivariate

Multivariate by `coxph`

Results of the multivariate Cox proportional hazards model


             coef exp(coef)  se(coef)      z        p
URGn    -0.157209  0.854525  0.276306 -0.569  0.56938
KTTINIn -0.029415  0.971013  0.211573 -0.139  0.88943
EPOINIn -0.380860  0.683274  0.173490 -2.195  0.02814
IRCn    -1.053448  0.348733  0.454990 -2.315  0.02060
O2n      1.115171  3.050091  0.395874  2.817  0.00485
ICn      0.007248  1.007275  0.282037  0.026  0.97950
IDMn     0.167159  1.181942  0.340742  0.491  0.62373
ANEVn   -0.101613  0.903380  0.530427 -0.192  0.84808
AMIn    -0.017282  0.982866  0.324674 -0.053  0.95755
KCn     -0.395796  0.673144  0.422623 -0.937  0.34901
VHCn     0.716229  2.046701  1.024804  0.699  0.48462
bmi     -0.007109  0.992916  0.015758 -0.451  0.65188
tabac2   0.239885  1.271103  0.182133  1.317  0.18781
sex     -0.127581  0.880222  0.177280 -0.720  0.47173
age      0.012324  1.012400  0.006794  1.814  0.06969
TRANSP1 -1.075155  0.341245  0.217639 -4.940 7.81e-07

Proportional hazards assumption hypothesis testing

         chisq df      p
URGn     1.7642  1 0.1841
KTTINIn  0.0419  1 0.8379
EPOINIn  0.0459  1 0.8303
IRCn     0.0325  1 0.8569
O2n      0.5678  1 0.4511
ICn      2.3551  1 0.1249
IDMn     1.4723  1 0.2250
ANEVn    0.9764  1 0.3231
AMIn     1.3344  1 0.2480
KCn      0.0370  1 0.8475
VHCn     1.8136  1 0.1781
bmi      0.5879  1 0.4432
tabac2   1.1035  1 0.2935
sex      0.6310  1 0.4270
age      0.0500  1 0.8230
TRANSP   9.2069  1 0.0024
GLOBAL  24.3757 16 0.0816

Schoenfeld residuals for the multivariate `coxph`

Graphics

Kaplan-Meier estimator

Cumulative incidence

Adjusted survival by `cph` for the ADPKD population

Adjusted survival by `cph` for the non ADPKD population

Incidence by dialysis method ³

Note: Nodes still represent an issue.

Incidence ADPKD VS OTHER NEPHROPATHY

CIM-10	Diagnostic
G45	accidents ischémiques cérébraux transitories et syndromes apparentés
I60	hémorragie sous-arachnoïdienne
I61	hémorragie intracérébrale
I62	autres hémorragies intracrâniennes non traumatiques
I63	infarctus cérébral
I64	accident vasculaire cérévral non précisé (hémorragique ou infarctus)
I74	embolie et thromose artérielle
R471	dysarthrie et anarthrie

↩︎

# COMPLETE FOLLOW-UP   
   0    2    3    4    7    9   10   12   14   38   42   43   44 
  15    1    2    1    1    3    2    2    1    1    1    1    3 
  45   62   68   69   70   71   72   73  100  101  102  103  104 
   1    1    2    1    1    1    1    2    1    2    1    2    1 
 127  130  131  132  134  159  160  162  163  164  165  166  186 
   2    1    2    1    1    2    1    2    1    1    1    1    2 
 188  191  192  193  218  219  220  221  222  225  246  248  249 
   2    2    1    2    1    1    2    1    2    1    2    1    1 
 250  251  253  254  255  278  279  280  281  283  284  285  286 
   1    2    2    1    1    1    2    1    5    3    1    2    2 
 287  308  311  314  317  319  339  342  345  346  348  357  358 
   1    3    1    1    1    1    3    1    3    1    1    1    1 
 362  364  365  366  367  368  369  370  372  373  374  375  376 
   1    1    3    3    2    1    3    1    3    2    1    1    2 
 378  401  402  403  405  407  410  432  434  438  459  462  463 
   2    1    1    1    1    2    1    1    1    1    1    1    1 
 465  468  469  489  491  492  495  496  497  500  520  527  550 
   2    1    1    2    1    1    3    1    1    1    1    1    1 
 552  553  558  559  582  583  585  587  589  612  614  615  617 
   1    1    2    1    1    1    2    3    2    1    2    1    2 
 621  642  643  645  650  652  679  680  681  702  704  711  712 
   2    2    1    1    1    1    1    1    1    1    1    1    2 
 718  719  720  721  722  723  724  725  726  727  728  729  730 
  46   44   54   43   27   42   61   38   63   47   42   74    1 
 732  734  738  739  742  745  768  772  774  776  794  803  827 
   2    1    2    1    1    1    1    1    1    1    1    1    1 
 855  857  859  863  890  915  923  924  950  982  983  984 1008 
   1    1    1    1    1    1    1    2    1    1    2    1    1 
1013 1043 1047 1070 1072 1076 1078 1083 1084 1085 1086 1087 1088 
   1    2    2    1    1    1    1   37   56   57   57   51   46 
1089 1090 1091 1092 1093 1094 1095 1097 1098 1099 1102 1103 1106 
  57   49   43   58   58   76    1    1    1    1    2    1    1 
1109 1132 1140 1158 1164 1167 1195 1222 1256 1261 1282 1287 1320 
   1    1    1    1    1    1    1    1    1    1    1    1    1 
1378 1433 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 
   1    1   36   40   44   66   27   38   35   46   42   57   46 
1460 1463 1466 1500 1504 1653 1712 1738 1814 1815 1816 1817 1818 
  54    1    1    1    1    1    1    1   33   48   36   49   35 
1819 1820 1821 1822 1823 1824 1825 
  47   47   36   47   46   40   46

↩︎

Methode Événement Pas d’événement

(1) Hémodialyse 240 1691

(2) Dialyse péritonéale 15 275

(3) Greffe 0 339

↩︎

Major changes

Damiano

12/16/2021

New dataset

New dataset only including events happening after the inclusion (2015)

Number of total events in ADPKD population

Events according to transplantation status

Type of event ¹

Follow-UP

Definition of the follow-up periods

Until the end of the follow up, set as December 31st 2019

Until death or event

Until transplatation

Follow-up with `case_when`nested in `mutate` ²

Multivariate

Multivariate by `coxph`

Results of the multivariate Cox proportional hazards model

Proportional hazards assumption hypothesis testing

Schoenfeld residuals for the multivariate `coxph`

Graphics

Kaplan-Meier estimator

Cumulative incidence

Adjusted survival by `cph` for the ADPKD population

Adjusted survival by `cph` for the non ADPKD population

Incidence by dialysis method ³

Incidence ADPKD VS OTHER NEPHROPATHY

Methode	Événement	Pas d’événement
(1) Hémodialyse	240	1691
(2) Dialyse péritonéale	15	275
(3) Greffe	0	339

Major changes

Damiano

12/16/2021

New dataset

New dataset only including events happening after the inclusion (2015)

Number of total events in ADPKD population

Events according to transplantation status

Type of event 1

Follow-UP

Definition of the follow-up periods

Until the end of the follow up, set as December 31st 2019

Until death or event

Until transplatation

Follow-up with case_whennested in mutate 2

Multivariate

Multivariate by coxph

Results of the multivariate Cox proportional hazards model

Proportional hazards assumption hypothesis testing

Schoenfeld residuals for the multivariate coxph

Graphics

Kaplan-Meier estimator

Cumulative incidence

Adjusted survival by cph for the ADPKD population

Adjusted survival by cph for the non ADPKD population

Incidence by dialysis method 3

Incidence ADPKD VS OTHER NEPHROPATHY

Type of event ¹

Follow-up with `case_when`nested in `mutate` ²

Multivariate by `coxph`

Schoenfeld residuals for the multivariate `coxph`

Adjusted survival by `cph` for the ADPKD population

Adjusted survival by `cph` for the non ADPKD population

Incidence by dialysis method ³