forked from RConsortium/submissions-pilot1
-
Notifications
You must be signed in to change notification settings - Fork 9
/
adlbc.r
269 lines (229 loc) · 7.71 KB
/
adlbc.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
# Note to Reviewer
# To rerun the code below, please refer ADRG appendix.
# After required package are installed.
# The path variable needs to be defined by using example code below
#
# nolint start
# path <- list(
# sdtm = "path/to/esub/tabulations/sdtm", # Modify path to the sdtm location
# adam = "path/to/esub/analysis/adam" # Modify path to the adam location
# )
# nolint end
###########################################################################
#' developers : Steven Haesendonckx/Dadong Zhang/Nicole Jones
#' date: 28NOV2022
#' modification History:
#' Dadong Zhang, 17DEC2022
#' Nicole Jones, 12Jan2023
#' Nicole Jones, 13Apr2023
###########################################################################
# Set up ------------------------------------------------------------------
library(haven)
library(admiral)
library(dplyr)
library(tidyr)
library(metacore)
library(metatools)
library(xportr)
library(stringr)
# read source -------------------------------------------------------------
# When SAS datasets are imported into R using read_sas(), missing
# character values from SAS appear as "" characters in R, instead of appearing
# as NA values. Further details can be obtained via the following link:
# https://pharmaverse.github.io/admiral/articles/admiral.html#handling-of-missing-values
# Read and convert NA for SDTM DATASET
## Laboratory Tests Results (LB)
lb <- convert_blanks_to_na(read_xpt(file.path(path$sdtm, "lb.xpt")))
## Supplemental Qualifiers for LB (SUPPLB)
supplb <- convert_blanks_to_na(read_xpt(file.path(path$sdtm, "supplb.xpt")))
# Read and convert NA for ADaM DATASET
## Subject-Level Analysis
adsl <- convert_blanks_to_na(read_xpt(file.path(path$adam, "adsl.xpt")))
# create labels
metacore <- spec_to_metacore(file.path(path$adam, "adam-pilot-3.xlsx"), where_sep_sheet = FALSE, quiet = TRUE)
adlbc_spec <- metacore %>%
select_dataset("ADLBC")
# Formats -----------------------------------------------------------------
## map parameter code and parameter
format_paramn <- function(x) {
case_when(
x == "SODIUM" ~ 18,
x == "K" ~ 19,
x == "CL" ~ 20,
x == "BILI" ~ 21,
x == "ALP" ~ 22,
x == "GGT" ~ 23,
x == "ALT" ~ 24,
x == "AST" ~ 25,
x == "BUN" ~ 26,
x == "CREAT" ~ 27,
x == "URATE" ~ 28,
x == "PHOS" ~ 29,
x == "CA" ~ 30,
x == "GLUC" ~ 31,
x == "PROT" ~ 32,
x == "ALB" ~ 33,
x == "CHOL" ~ 34,
x == "CK" ~ 35
)
}
# Add supplemental information --------------------------------------------
sup <- supplb %>%
select(STUDYID, USUBJID, IDVAR, IDVARVAL, QNAM, QLABEL, QVAL) %>%
pivot_wider(
id_cols = c(STUDYID, USUBJID, IDVARVAL),
names_from = QNAM,
values_from = QVAL
) %>%
mutate(LBSEQ = as.numeric(IDVARVAL)) %>%
select(-IDVARVAL)
adlb00 <- lb %>%
left_join(sup, by = c("STUDYID", "USUBJID", "LBSEQ")) %>%
filter(LBCAT == "CHEMISTRY")
# ADSL information --------------------------------------------------------
adsl <- adsl %>%
select(
STUDYID, SUBJID, USUBJID, TRT01PN, TRT01P, TRT01AN, TRT01A, TRTSDT, TRTEDT, AGE, AGEGR1, AGEGR1N, RACE, RACEN, SEX,
COMP24FL, DSRAEFL, SAFFL
)
adlb01 <- adlb00 %>%
left_join(adsl, by = c("STUDYID", "USUBJID"))
# Dates -------------------------------------------------------------------
adlb02 <- adlb01 %>%
derive_vars_dt(
new_vars_prefix = "A",
dtc = LBDTC,
highest_imputation = "n"
) %>%
derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))
# AVAL(C) -----------------------------------------------------------------
# No imputations are done for values below LL or above UL
adlb03 <- adlb02 %>%
mutate(
AVAL = LBSTRESN,
AVALC = ifelse(!is.na(AVAL), LBSTRESC, NA)
)
# Parameter ---------------------------------------------------------------
adlb04 <- adlb03 %>%
mutate(
PARAM = paste0(LBTEST, " (", LBSTRESU, ")"),
PARAMCD = LBTESTCD,
PARAMN = format_paramn(LBTESTCD),
PARCAT1 = "CHEM" # changed to match prod dataset
)
# Baseline ----------------------------------------------------------------
## updating to use admiral programming
adlb05 <- adlb04 %>%
mutate(ABLFL = LBBLFL) %>%
derive_var_base(
by_vars = exprs(STUDYID, USUBJID, PARAMCD),
source_var = AVAL,
new_var = BASE
) %>%
derive_var_chg() %>%
mutate(CHG = ifelse(VISITNUM == 1, NA, CHG))
# VISITS ------------------------------------------------------------------
eot <- adlb05 %>%
filter(ENDPOINT == "Y" | VISITNUM == 12) %>%
mutate(
AVISIT = "End of Treatment",
AVISITN = 99,
AENTMTFL = "Y"
)
adlb06 <- adlb05 %>%
filter(grepl("WEEK", VISIT, fixed = TRUE) |
grepl("UNSCHEDULED", VISIT, fixed = TRUE) |
grepl("SCREENING", VISIT, fixed = TRUE)) %>% # added conditions to include screening and unscheduled visits
mutate(
AVISIT = case_when(
ABLFL == "Y" ~ "Baseline",
grepl("UNSCHEDULED", VISIT) == TRUE ~ "",
TRUE ~ str_to_sentence(VISIT)
),
AVISITN = case_when(
AVISIT == "Baseline" ~ 0,
TRUE ~ as.numeric(gsub("[^0-9]", "", AVISIT))
),
AENTMTFL = ""
) %>%
rbind(eot) %>%
mutate(
AVISITN = ifelse(AVISITN == -1, "", AVISITN)
)
# get EOT for those that did not make it to week 24
eot2 <- adlb06 %>%
arrange(STUDYID, USUBJID, PARAMCD, desc(AVISITN)) %>%
group_by(STUDYID, USUBJID, PARAMCD) %>%
filter(VISITNUM != 13) %>%
slice(1) %>%
filter(!is.na(AVISITN), AVISITN != 0, AVISITN != 99) %>%
mutate(
AVISITN = 99,
AVISIT = "End of Treatment",
AENTMTFL = "Y"
)
adlb07 <- adlb06 %>%
filter(VISITNUM <= 12 & AVISITN > 0 & AVISITN != 99 & !grepl("UN", VISIT)) %>%
group_by(USUBJID, PARAMCD) %>%
mutate(AENTMTFL_1 = ifelse(max(AVISITN, na.rm = TRUE) == AVISITN, "Y", "")) %>%
select(USUBJID, PARAMCD, AENTMTFL_1, LBSEQ) %>%
full_join(adlb06, by = c("USUBJID", "PARAMCD", "LBSEQ"), multiple = "all") %>%
mutate(AENTMTFL = ifelse(AENTMTFL == "Y", AENTMTFL, AENTMTFL_1)) %>%
select(-AENTMTFL_1) %>%
rbind(eot2) %>%
ungroup()
# Limits ------------------------------------------------------------------
# updating to use admiral dataset
adlb08 <- adlb07 %>%
mutate(
ANRLO = LBSTNRLO,
ANRHI = LBSTNRHI,
A1LO = LBSTNRLO,
A1HI = LBSTNRHI,
R2A1LO = AVAL / A1LO,
R2A1HI = AVAL / A1HI,
BR2A1LO = BASE / A1LO,
BR2A1HI = BASE / A1HI,
ONE = abs((LBSTRESN - (1.5 * LBSTNRHI))),
TWO = abs(((.5 * LBSTNRLO) - LBSTRESN)),
ALBTRVAL = ifelse(ONE > TWO, ONE, TWO),
ANRIND = ifelse(AVAL < (0.5 * LBSTNRLO), "L", ifelse(AVAL > (1.5 * LBSTNRHI), "H", "N")),
ANRIND = ifelse(is.na(AVAL), "N", ANRIND)
) %>%
# derive_var_anrind() %>%
derive_var_base(
by_vars = exprs(STUDYID, USUBJID, PARAMCD),
source_var = ANRIND,
new_var = BNRIND
) %>% # Low and High values are repeating
group_by(STUDYID, USUBJID, PARAMCD) %>%
ungroup() %>%
select(-ONE, -TWO)
# Derive ANL01FL
adlb09 <- adlb08 %>%
filter((VISITNUM >= 4 & VISITNUM <= 12) & !grepl("UN", VISIT)) %>%
group_by(USUBJID, PARAMCD) %>%
mutate(
maxALBTRVAL = ifelse(!is.na(ALBTRVAL), max(ALBTRVAL, na.rm = TRUE), ALBTRVAL),
ANL01FL = ifelse(maxALBTRVAL == ALBTRVAL, "Y", "")
) %>%
arrange(desc(ANL01FL)) %>%
select(USUBJID, PARAMCD, LBSEQ, ANL01FL) %>%
slice(1) %>%
full_join(adlb08, by = c("USUBJID", "PARAMCD", "LBSEQ"), multiple = "all")
# Treatment Vars ------------------------------------------------------------
adlbc <- adlb09 %>%
mutate(
TRTP = TRT01P,
TRTPN = TRT01PN,
TRTA = TRT01A,
TRTAN = TRT01AN
) %>%
drop_unspec_vars(adlbc_spec) %>%
order_cols(adlbc_spec) %>%
set_variable_labels(adlbc_spec) %>%
xportr_format(adlbc_spec$var_spec %>%
mutate_at(c("format"), ~ replace_na(., "")), "ADLBC") %>%
xportr_write(file.path(path$adam, "adlbc.xpt"),
label = "Analysis Dataset Lab Blood Chemistry"
)