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SAS Files as a Data Source

Source: vignettes/sas_files.Rmd
sas_files.Rmd

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SAS files

Typically clinical data is stored in several SAS files in a standardized format. We need the files in which the visits and the AEs are recorded. For this demo we have selected an anonymized data set which only contains patients enrolled into the control arm. In those data sets the AE onset dates and the visit dates have been replaced with the number of days that have passed since a specific cut-off date. We can proceed in a similar way

df_ae <- haven::read_sas('adae.sas7bdat') %>%
  select(STUDYID, SUBJID, SITEID, AESTDY) 

df_ae
## # A tibble: 4,702 × 4
##    STUDYID    SUBJID   SITEID AESTDY
##    <chr>      <chr>    <chr>   <dbl>
##  1 CO-101-001 01001001 001       -14
##  2 CO-101-001 01001001 001       -13
##  3 CO-101-001 01001001 001        50
##  4 CO-101-001 01001001 001        67
##  5 CO-101-001 01001001 001        77
##  6 CO-101-001 01001001 001        84
##  7 CO-101-001 01001001 001        99
##  8 CO-101-001 01001001 001        99
##  9 CO-101-001 01001001 001       127
## 10 CO-101-001 01001001 001       127
## # ℹ 4,692 more rows
df_vs <- haven::read_sas('advs.sas7bdat') %>%
  select(STUDYID, SUBJID, SITEID, ADY) 

df_vs
## # A tibble: 45,176 × 4
##    STUDYID    SUBJID   SITEID   ADY
##    <chr>      <chr>    <chr>  <dbl>
##  1 CO-101-001 01001001 001       -7
##  2 CO-101-001 01001001 001        1
##  3 CO-101-001 01001001 001        1
##  4 CO-101-001 01001001 001        8
##  5 CO-101-001 01001001 001        8
##  6 CO-101-001 01001001 001       15
##  7 CO-101-001 01001001 001       15
##  8 CO-101-001 01001001 001       29
##  9 CO-101-001 01001001 001       29
## 10 CO-101-001 01001001 001       38
## # ℹ 45,166 more rows

In order to assign each AE to a visit we union both event tables and sort by date.

df_ae <- df_ae %>%
  rename(DY = AESTDY) %>%
  mutate(EVENT = "AE")

df_vs <- df_vs %>%
  rename(DY = ADY) %>%
  mutate(EVENT = "VS") %>%
  # we ignore visits that have no date
  filter(! is.na(DY)) %>%
  # we are not interested in same day visits
  distinct()

df_aevs <- bind_rows(df_ae, df_vs) %>%
  # NA's get sorted towards the end thus AEs with no date get sorted towards last visit
  arrange(STUDYID, SITEID, SUBJID, DY) %>%
  group_by(STUDYID, SITEID, SUBJID) %>%
  mutate(AE_NO = cumsum(ifelse(EVENT == "AE", 1, 0)),
         VS_NO = cumsum(ifelse(EVENT == "VS", 1, 0))) %>%
  # we remove patients with 0 visits
  filter(max(VS_NO) > 0) %>%
  # AE's before fist visit should register to visit 1 not zero
  mutate(VS_NO = ifelse(VS_NO == 0, 1, VS_NO))

patient example with AE before first visit and AEs with NA in date

df_aevs %>%
  filter(SUBJID == "01007004") %>%
  knitr::kable()
STUDYID SUBJID SITEID DY EVENT AE_NO VS_NO
CO-101-001 01007004 007 -14 AE 1 1
CO-101-001 01007004 007 -8 VS 1 1
CO-101-001 01007004 007 -3 AE 2 1
CO-101-001 01007004 007 -1 AE 3 1
CO-101-001 01007004 007 1 AE 4 1
CO-101-001 01007004 007 1 VS 4 2
CO-101-001 01007004 007 8 VS 4 3
CO-101-001 01007004 007 15 AE 5 3
CO-101-001 01007004 007 15 AE 6 3
CO-101-001 01007004 007 18 AE 7 3
CO-101-001 01007004 007 20 VS 7 4
CO-101-001 01007004 007 29 VS 7 5
CO-101-001 01007004 007 36 AE 8 5
CO-101-001 01007004 007 36 AE 9 5
CO-101-001 01007004 007 36 AE 10 5
CO-101-001 01007004 007 36 VS 10 6
CO-101-001 01007004 007 43 VS 10 7
CO-101-001 01007004 007 57 AE 11 7
CO-101-001 01007004 007 57 AE 12 7
CO-101-001 01007004 007 57 VS 12 8
CO-101-001 01007004 007 58 AE 13 8
CO-101-001 01007004 007 64 VS 13 9
CO-101-001 01007004 007 71 VS 13 10
CO-101-001 01007004 007 85 VS 13 11
CO-101-001 01007004 007 92 VS 13 12
CO-101-001 01007004 007 99 AE 14 12
CO-101-001 01007004 007 99 VS 14 13
CO-101-001 01007004 007 112 VS 14 14
CO-101-001 01007004 007 120 AE 15 14
CO-101-001 01007004 007 123 AE 16 14
CO-101-001 01007004 007 123 AE 17 14
CO-101-001 01007004 007 123 AE 18 14
CO-101-001 01007004 007 125 VS 18 15
CO-101-001 01007004 007 NA AE 19 15
CO-101-001 01007004 007 NA AE 20 15
CO-101-001 01007004 007 NA AE 21 15

Then we aggregate on visit number.

df_aevs_aggr <- df_aevs %>%
  group_by(STUDYID, SITEID, SUBJID, VS_NO) %>%
  summarise(MIN_AE_NO = min(AE_NO),
            MAX_AE_NO = max(AE_NO),
            .groups = "drop") %>%
  group_by(STUDYID, SITEID, SUBJID) %>%
  mutate(MAX_VS_PAT = max(VS_NO)) %>%
  ungroup() %>%
  # assign AEs that occur after last visit to last AE
  mutate(
    CUM_AE = ifelse(
      VS_NO == MAX_VS_PAT,
      MAX_AE_NO,
      MIN_AE_NO)
    )

Same patient example as before.

df_aevs_aggr  %>%
  filter(SUBJID == "01007004") %>%
  knitr::kable()
STUDYID SITEID SUBJID VS_NO MIN_AE_NO MAX_AE_NO MAX_VS_PAT CUM_AE
CO-101-001 007 01007004 1 1 4 15 1
CO-101-001 007 01007004 2 4 4 15 4
CO-101-001 007 01007004 3 4 7 15 4
CO-101-001 007 01007004 4 7 7 15 7
CO-101-001 007 01007004 5 7 10 15 7
CO-101-001 007 01007004 6 10 10 15 10
CO-101-001 007 01007004 7 10 12 15 10
CO-101-001 007 01007004 8 12 13 15 12
CO-101-001 007 01007004 9 13 13 15 13
CO-101-001 007 01007004 10 13 13 15 13
CO-101-001 007 01007004 11 13 13 15 13
CO-101-001 007 01007004 12 13 14 15 13
CO-101-001 007 01007004 13 14 14 15 14
CO-101-001 007 01007004 14 14 18 15 14
CO-101-001 007 01007004 15 18 21 15 21

As a control we check whether the numbers of visits and AEs of our processed data still matches the number of AEs in our original data.

stopifnot(nrow(df_aevs_aggr) == nrow(df_vs))

n_aes <- df_aevs_aggr %>%
  group_by(SUBJID) %>%
  summarize(n_aes = max(CUM_AE)) %>%
  pull(n_aes) %>%
  sum()

n_aes_original <- df_ae %>%
  # all AEs for patients with more than 1 visit
  filter(SUBJID %in% df_aevs$SUBJID) %>%
  nrow()

stopifnot(n_aes == n_aes_original)

{simaerep}

After renaming some of the columns we can pass the aggregated data from the SAS files to simaerep

df_visit <- df_aevs_aggr %>%
  rename(
    study_id = "STUDYID",
    site_number = "SITEID",
    patnum = "SUBJID",
    n_ae = "CUM_AE",
    visit = "VS_NO"
  )  %>%
  select(study_id, site_number, patnum, n_ae, visit)

df_visit
## # A tibble: 5,432 × 5
##    study_id   site_number patnum    n_ae visit
##    <chr>      <chr>       <chr>    <dbl> <dbl>
##  1 CO-101-001 001         01001001     1     1
##  2 CO-101-001 001         01001001     2     2
##  3 CO-101-001 001         01001001     2     3
##  4 CO-101-001 001         01001001     2     4
##  5 CO-101-001 001         01001001     2     5
##  6 CO-101-001 001         01001001     2     6
##  7 CO-101-001 001         01001001     2     7
##  8 CO-101-001 001         01001001     3     8
##  9 CO-101-001 001         01001001     3     9
## 10 CO-101-001 001         01001001     6    10
## # ℹ 5,422 more rows
aerep <- simaerep(df_visit)

plot(aerep)

Left panel shows mean AE reporting per site (lightblue and darkblue lines) against mean AE reporting of the entire study (golden line). Single sites are plotted in descending order by AE under-reporting probability on the right panel in which grey lines denote cumulative AE count of single patients. Grey dots in the left panel plot indicate sites that were picked for single plotting. AE under-reporting probability of dark blue lines crossed threshold of 95%. Numbers in the upper left corner indicate the ratio of patients that have been used for the analysis against the total number of patients. Patients that have not been on the study long enough to reach the evaluation point (visit_med75, see introduction) will be ignored.