In LisaHopcroft/CTutils: Helpful Functions for Clinical Trial Data Analysis at SCTRU (Public Health Scotland)
EUdraCT report
The information required for the EudraCT upload is provided in this report.
Also generated is an XML file for the adverse events upload.
This report is heavily reliant on the relevant information being completed
in the settings.yml file. Please ensure that this file is fully population
withthe relevant information.
vocab = CTutils::read_EudraCT_vocabulary()
EUdraCT_vocabulary = vocab$EudraCT
RMS_vocabulary = vocab$RMS
print_info = function( info_var ) {
info_var.string = deparse(substitute(info_var))
if( !exists(info_var.string) ) {
info_var = "[MISSING]"
}
return( info_var )
}
Trial index
| | |
| ---------------------- | --------------------------------------- |
| Friendly description | r print_info(TRIAL.DESCRIPTION) |
Summary attachments to include:
- Final report to DMC
- Published article
Trial information
Trial identification
| | |
| ---------------------- | --------------------------------------- |
| EudraCT number | r print_info(TRIAL.ID.EudraCT) |
| Sponsor protocol code | r print_info(TRIAL.NAME) v. r print_info(PROTOCOL.VERSION) (dated r print_info(PROTOCOL.DATE)) |
| Full title of trial | r print_info(TRIAL.DESCRIPTION) |
Additional Study identifiers
| | |
| ---------------------- | --------------------------------------- |
| ISRCTN number | r print_info(TRIAL.ID.ISRCTN) |
| ClinicalTrials.gov number | r print_info( TRIAL.ID.NCT ) |
| WHO universal trial number | r print_info( TRIAL.ID.WHO ) |
Other identifiers provided:
other_ids = ls( pattern="TRIAL\\.ID\\..*" ) %>%
discard( ~ str_detect(.x, pattern="ISRCTN|REC|EudraCT") )
if ( length( other_ids ) > 0 ) {
other_ids.list = map( other_ids, get ) %>%
set_names( other_ids %>% str_replace( "TRIAL\\.ID\\.", "" ) )
for( id_i in 1:length(other_ids.list) ) {
cat( sprintf( "- **%s ID**: %s\n",
names(other_ids.list)[id_i],
other_ids.list[id_i]) )
}
} else {
cat( sprintf( "No other trial identifiers have been specified." ))
}
Sponsor details
| | |
| ---------------------- | --------------------------------------- |
| Name of organisation | r print_info( SPONSOR.NAME ) |
| Street address | r print_info( SPONSOR.ADDRESS.STREET ) |
| Town/city | r print_info( SPONSOR.ADDRESS.CITY ) |
| Postcode | r print_info( SPONSOR.ADDRESS.POSTCODE ) |
Scientific contact point
| | |
| ---------------------- | --------------------------------------- |
| Name of organisation | r print_info( SPONSOR.SCIENTIFIC_CONTACT.ORGANISATION ) |
| Functional contact point name | r print_info( SPONSOR.SCIENTIFIC_CONTACT.NAME ) |
| Telephone number | +r print_info( SPONSOR.SCIENTIFIC_CONTACT.TELEPHONE.COUNTRY_CODE) r print_info( SPONSOR.SCIENTIFIC_CONTACT.TELEPHONE.NUMBER) |
| Email address | r print_info( SPONSOR.SCIENTIFIC_CONTACT.EMAIL) |
Public contact point
| | |
| ---------------------- | --------------------------------------- |
| Name of organisation | r print_info( SPONSOR.PUBLIC_CONTACT.ORGANISATION ) |
| Functional contact point name | r print_info( SPONSOR.PUBLIC_CONTACT.NAME ) |
| Telephone number | +r print_info( SPONSOR.PUBLIC_CONTACT.TELEPHONE.COUNTRY_CODE) r print_info( SPONSOR.PUBLIC_CONTACT.TELEPHONE.NUMBER) |
| Email address | r print_info( SPONSOR.PUBLIC_CONTACT.EMAIL) |
Paediatric regulatory details
Is trial part of an agreed paediatric investigation plan (PIP)? r print_info(EUDRACT.PAEDIATRIC.PIP)
Does article 45 of Regulation (EC) No 1901/2006 apply to this trial? r print_info(EUDRACT.PAEDIATRIC.ARTICLE_45_APPLIES)
Does article 46 of Regulation (EC) No 1901/2006 apply to this trial? r print_info(EUDRACT.PAEDIATRIC.ARTICLE_46_APPLIES)
Results analysis stage
# date_of_final_analysis = format(as.Date(EXTRACT.DATE,format="%Y-%m-%d"),format="%d %B %Y")
#
# primary_completion_date = ITT_population.d_extended %>%
# select( Label, ends_with( "Date_Form_Comp" ) ) %>%
# select( -contains( "Scan_review" ) ) %>%
# pivot_longer( -Label ) %>% arrange( desc(dmy(value)) ) %>%
# pull( value ) %>%
# head(1) %>%
# as.character()
#
# last_appointment_columns = grep( ls( ), pattern = "EUDRACT.LAST_APPOINTMENT", value=T ) %>%
# lapply( get ) %>% unlist
#
# global_end_of_trial_date = ITT_population.d_extended %>%
# select( Label, !!!syms( last_appointment_columns ) ) %>%
# pivot_longer( -Label ) %>%
# filter( value != "" ) %>%
# arrange( desc( dmy(value) ) ) %>%
# pull( value ) %>%
# head(1) %>%
# as.character()
| | |
| ---------------------- | --------------------------------------- |
| Analysis stage | r print_info( REPORT.TYPE ) |
| Date of interim/final analysis | r date_of_final_analysis |
| | |
| ---------------------- | --------------------------------------- |
| Is this the analysis of the primary completion data? | TBD |
| Primary completion date | r primary_completion_date |
| | |
| ---------------------- | --------------------------------------- |
| Global end of trial date reached? | Yes |
| Global end of trial date | r global_end_of_trial_date |
| Was the trial ended prematurely? | No |
General information about the trial
Main objective of the trial
r TRIAL.PRIMARY_OBJECTIVE
Actual start date of recruitment
r TRIAL.START_DATE %>% as.character
Long term follow-up planned?
r EUDRACT.LTFU_PLANNED
Independent data monitoring committee (IDMC) involvement?
r ( DMC.COMMITTEE %>% nrow ) > 1
Protection of trial subjects
r print_info( EUDRACT.SUBJECT_PROTECTION )
Background therapy
r print_info( EUDRACT.NON_TEST_OR_COMPARATOR_TREATMENTS )
Evidence for comparator(s)
r print_info( EUDRACT.COMPARATOR_RATIONALE )
Population of trial subjects
Subject number per country
Note that this information may need to be added to the relevant
data frame.
if ( str_detect( "Country", EudraCT_data %>% colnames ) %>% any ) {
countries = EudraCT_data %>%
select( Label, Country ) %>%
unique %>% pull( Country )
country_table = table( countries ) %>%
as_tibble( )
country_table = country_table %>%
add_row( countries = "Total",
n = sum( country_table$n ))
knitr::kable( country_table )
}
Age group breakdown for trial
agegroup.tibble = EudraCT_data$EudraCT_agegroup %>%
discard( ~is.na(.x) ) %>%
as_tibble %>%
group_by( value, .drop=FALSE ) %>%
dplyr::summarise( count = n() ) %>%
mutate( value = as.character(value)) %>%
dplyr::rename( Agegroup = value )
knitr::kable( agegroup.tibble )
Subject disposition
Recruitment details
r print_info(EUDRACT.RECRUITMENT_DETAILS)
Pre-assignment
Screening details
r print_info(EUDRACT.SCREENING_DETAILS)
Arm information (copy this section for each arm)
# patients_enrolled = ITT_population.d_extended %>%
# filter( Label != "" ) %>%
# pull(Label) %>%
# unique ()
#
# patients_dropouts = ITT_population.d_extended %>%
# filter( Miscellaneous_Early_Termin_Trial_ID != "" ) %>%
# select( Label, starts_with( "Miscellaneous_Early_Termin" ) ) %>%
# pull( Label ) %>%
# unique()
#
# n_enrolled = patients_enrolled %>% length( )
# n_completed = setdiff( patients_enrolled, patients_dropouts ) %>% length( )
Reasons for dropping out:
Disease progression:
# do_count( this_data = ITT_population.d_extended %>%
# filter( Miscellaneous_Early_Termin_Trial_ID != "" ),
# this_var = quo( Miscellaneous_Early_Termin_ET_Dis_Prog ),
# key_for_variables = glossary.key,
# missing_data_limit = PIPELINE.MISSING_DATA_LIMIT )
Other reasons:
#
# do_count( this_data = ITT_population.d_extended %>%
# filter( Miscellaneous_Early_Termin_Trial_ID != "" ) %>%
# filter( Miscellaneous_Early_Termin_ET_Other_Reas == "Yes"),
# this_var = quos( Miscellaneous_Early_Termin_ET_Wthdrw_Cons,
# Miscellaneous_Early_Termin_ET_Pat_LTFU,
# Miscellaneous_Early_Termin_ET_PI_Decision,
# Miscellaneous_Early_Termin_ET_Non_Comply,
# Miscellaneous_Early_Termin_ET_Not_Start,
# Miscellaneous_Early_Termin_ET_Death ),
# key_for_variables = glossary.key,
# missing_data_limit = PIPELINE.MISSING_DATA_LIMIT )
#
# reason_per_patient = ITT_population.d_extended %>%
# filter( Miscellaneous_Early_Termin_Trial_ID != "" ) %>%
# filter( Miscellaneous_Early_Termin_ET_Other_Reas == "Yes") %>%
# select( Label,
# Miscellaneous_Early_Termin_ET_Wthdrw_Cons,
# Miscellaneous_Early_Termin_ET_Pat_LTFU,
# Miscellaneous_Early_Termin_ET_PI_Decision,
# Miscellaneous_Early_Termin_ET_Non_Comply,
# Miscellaneous_Early_Termin_ET_Not_Start,
# Miscellaneous_Early_Termin_ET_Death )
#
# reason_check = reason_per_patient %>%
# pivot_longer( -Label,
# names_to = "reason",
# values_to = "response") %>%
# group_by( Label ) %>%
# summarise( REASON_PRESENT = any( response == "Yes"))
r ifelse( reason_check %>% pull( REASON_PRESENT ) %>% all, "", print_error( "Not all patients have a reason provided." ) )
# knitr::kable(PERIOD.INFO %>%
# mutate(section_header = sprintf("**%s**",section_header)) %>%
# mutate(section_header = str_replace(section_header,"\\*\\*\\*\\*","")) %>%
# mutate(question = sprintf("**%s**",question)) %>%
# mutate(question = str_replace(question,"\\*\\*\\*\\*","")),
# col.names=rep.int("",ncol(PERIOD.INFO)))
Number of patients in each population (e.g., ITT, safety etc)
Age/gender counts per population
ITT population
# ITT_population.d %>% pull( EudraCT_agegroup ) %>% table
#
# do_summary(ITT_population.d,
# quo( Age),
# key_for_variables = glossary.key,
# missing_data_limit = PIPELINE.MISSING_DATA_LIMIT )
#
# ITT_population.d %>% pull( Enrolment_Trial_Enrolment_Sex ) %>% table
Safety/evaluable population
# treated_population.d %>% pull( EudraCT_agegroup ) %>% table
#
# do_summary(treated_population.d,
# quo( Age),
# key_for_variables = glossary.key,
# missing_data_limit = PIPELINE.MISSING_DATA_LIMIT )
#
# treated_population.d %>% pull( Enrolment_Trial_Enrolment_Sex ) %>% table
Surgical population
#
# ITT_population.d %>% filter( SURGERY_PERFORMED == "Yes" ) %>% pull( EudraCT_agegroup ) %>% table
#
# do_summary(ITT_population.d %>% filter( SURGERY_PERFORMED == "Yes" ),
# quo( Age),
# key_for_variables = glossary.key,
# missing_data_limit = PIPELINE.MISSING_DATA_LIMIT )
#
# ITT_population.d %>% pull( Enrolment_Trial_Enrolment_Sex ) %>% table
Generating the SAE and AE XML uploads for EudraCT
### Example shown below
# SAE.data = ITT_population.d_extended %>%
# filter( !is.na(SAE_Reporting_SAE_SAE_Num) ) %>%
# select( SAE_Reporting_SAE_SAE_Num,
# Label,
#
# # SAE_Reporting_SAE_SAE_Axi_Related,
# SAE_Reporting_SAE_SAE_Oth_Related,
# SAE_Reporting_SAE_SAE_Oth_Spec,
# SAE_Reporting_SAE_SAE_Pro_Related,
# SAE_Reporting_SAE_SAE_Pro_Spec,
#
# SAE_Reporting_SAE_2_SAE_Ax_TDD,
# SAE_Reporting_SAE_2_SAE_Ax_Freq,
# SAE_Reporting_SAE_2_SAE_Ax_Start,
# SAE_Reporting_SAE_2_SAE_Ax_Stop,
# SAE_Reporting_SAE_SAE_Event,
# SAE_Reporting_SAE_SAE_Symp_Start,
#
# # SAE type?
# SAE_Reporting_SAE_SAE_Death,
# SAE_Reporting_SAE_SAE_Hosp,
# SAE_Reporting_SAE_SAE_Defect,
# SAE_Reporting_SAE_SAE_Life,
# SAE_Reporting_SAE_SAE_Incapacity,
# SAE_Reporting_SAE_SAE_Important,
#
# SAE_Reporting_SAE_SAE_Grade,
# SAE_Reporting_SAE_SAE_Axi_Related, # site investigator causailty
# SAE_Reporting_SAE_SAE_CI_Cause, # CI causality
# SAE_Reporting_SAE_SAE_Exp_Unexp,
#
# ### SUSAR?
# #SAE_Reporting_SAE_SAE_Exp_Unexp = Unexpected
# #SAE_Reporting_SAE_SAE_CI_Cause = YES
#
# SAE_Reporting_SAE_2_SAE_Outcome
# ) %>%
# group_by( SAE_Reporting_SAE_SAE_Num ) %>%
# mutate( SUSAR = ifelse(SAE_Reporting_SAE_SAE_Exp_Unexp=="Unexpected" &
# SAE_Reporting_SAE_SAE_CI_Cause=="Yes",
# "Yes","No"),
# SAEtype = paste(
# ifelse(SAE_Reporting_SAE_SAE_Death=="Yes",
# "Death",
# ""),
# ifelse(SAE_Reporting_SAE_SAE_Hosp =="Yes",
# "Hospitalisation",
# ""),
# ifelse(SAE_Reporting_SAE_SAE_Defect=="Yes",
# "Birth defect",
# ""),
# ifelse(SAE_Reporting_SAE_SAE_Life=="Yes",
# "Life threatening",
# ""),
# ifelse(SAE_Reporting_SAE_SAE_Incapacity=="Yes",
# "Disability/incapacity",
# ""),
# collapse="\n"
# ) ) %>%
# select( -SAE_Reporting_SAE_SAE_Death,
# -SAE_Reporting_SAE_SAE_Hosp,
# -SAE_Reporting_SAE_SAE_Defect,
# -SAE_Reporting_SAE_SAE_Life,
# -SAE_Reporting_SAE_SAE_Incapacity,
# -SAE_Reporting_SAE_SAE_Important) %>%
#
# mutate( suspect = paste(
# ifelse(SAE_Reporting_SAE_SAE_Pro_Related=="Yes",
# sprintf( "%s: %s",
# glossary.key[["SAE_Reporting_SAE_SAE_Pro_Related"]],
# SAE_Reporting_SAE_SAE_Pro_Spec),
# ""),
# ifelse(SAE_Reporting_SAE_SAE_Oth_Related=="Yes",
# sprintf( "%s: %s",
# glossary.key[["SAE_Reporting_SAE_SAE_Oth_Related"]],
# SAE_Reporting_SAE_SAE_Oth_Spec),
# ""),
# collapse="\n"
# ) ) %>%
# select( -SAE_Reporting_SAE_SAE_Oth_Related,
# -SAE_Reporting_SAE_SAE_Oth_Spec,
# -SAE_Reporting_SAE_SAE_Pro_Related,
# -SAE_Reporting_SAE_SAE_Pro_Spec ) %>%
# mutate(IMPdose = sprintf( "%s, %s",
# SAE_Reporting_SAE_2_SAE_Ax_TDD,
# SAE_Reporting_SAE_2_SAE_Ax_Freq)) %>%
# select( -SAE_Reporting_SAE_2_SAE_Ax_TDD,
# -SAE_Reporting_SAE_2_SAE_Ax_Freq ) %>%
# mutate( studydrug = sprintf( "Started: %s\nStopped: %s",
# dmy(SAE_Reporting_SAE_2_SAE_Ax_Start),
# dmy(SAE_Reporting_SAE_2_SAE_Ax_Stop) )) %>%
# select( -SAE_Reporting_SAE_2_SAE_Ax_Start,
# -SAE_Reporting_SAE_2_SAE_Ax_Stop ) %>%
# select( SAE_Reporting_SAE_SAE_Num,
# Label,
# suspect,
# IMPdose,
# studydrug,
# SAE_Reporting_SAE_SAE_Event,
# SAE_Reporting_SAE_SAE_Symp_Start,
# SAEtype,
# SAE_Reporting_SAE_SAE_Grade,
# SAE_Reporting_SAE_SAE_Axi_Related,
# SAE_Reporting_SAE_SAE_CI_Cause,
# SAE_Reporting_SAE_SAE_Exp_Unexp,
# SUSAR,
# SAE_Reporting_SAE_2_SAE_Outcome
# ) %>%
# arrange( SAE_Reporting_SAE_SAE_Num ) %>%
# mutate( description = SAE_Reporting_SAE_SAE_Event )
#
#
#
# print_flextable( SAE.data,
# column_recode = list(
# Label = "Patient",
# SAE_Reporting_SAE_SAE_Num = "SCTRU SAE No.",
# description = "SAE event term",
# SAE_Reporting_SAE_SAE_Grade = "CTC Grade V4.0",
# SAE_Reporting_SAE_SAE_Symp_Start = "Onset Date",
# SAE_Reporting_SAE_SAE_Axi_Related = "Causality Assessment of Site I",
# SAE_Reporting_SAE_SAE_CI_Cause = "Causality Assessment of CI",
# SAE_Reporting_SAE_SAE_Exp_Unexp = "Expectedness Assessment of CI",
# SAE_Reporting_SAE_2_SAE_Outcome = "SAE Outcome",
# SUSAR = "SUSAR?",
# SAEtype = "SAE Type",
# suspect = "Suspect drug/procedure",
# IMPdose = "IMP Dose & route",
# studydrug = "Date study drug started and stopped"
# ))
#
#
# AE.data = toxicity_ALL.d %>% filter( value > 0 ) %>%
# dplyr::rename( description = toxicity_description )
# if ( EUDRACT.MEDDRA_SAE.FILE != "" & file.exists( paste( EUDRACT.MEDDRA_SAE.LOCATION,
# EUDRACT.MEDDRA_SAE.FILE,
# sep="/") ) ) {
#
# SAE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_SAE.LOCATION,
# EUDRACT.MEDDRA_SAE.FILE,
# sep="/") ) %>%
# mutate( soc_term = ifelse( is.na(`SOC confirmed`),
# `SOC proposed`,
# `SOC confirmed` ) )
#
# SAE_MedDRA_encoding = SAE_MedDRA_encoding.in %>%
# inner_join( eudract::soc_code %>%
# select( soc_term,
# meddra ),
# by="soc_term" ) %>%
# dplyr::rename( soc = meddra )
#
#
# SAE_MedDRA_merge = SAE.data %>%
# inner_join( SAE_MedDRA_encoding, by=c( "Label", "description" ) ) %>%
# mutate( pt = NA ) %>%
# dplyr::rename( subjid = Label ) %>%
# dplyr::rename( related = SAE_Reporting_SAE_SAE_CI_Cause ) %>%
# mutate( fatal = case_when(
# SAE_Reporting_SAE_2_SAE_Outcome == "Death" ~ 1,
# TRUE ~ 0 ) ) %>%
# mutate( serious = 1 ) %>%
# mutate( group = "Single" ) %>%
# mutate( term = soc_term ) %>%
# select( pt, # CAN BE NA
# subjid, # Label
# related, # SAE.data$SAE_Reporting_SAE_SAE_CI_Cause
# soc, # got this in SAE_MedDRA_encoding$soc
# fatal, # SAE.data$SAE_Reporting_SAE_2_SAE_Outcome
# serious, # this will be 1 for SAEs
# group, # which arm - what to do if there is only one?
# term # SAE_MedDRA_encoding$soc_term
# )
# }
#
#
# SAE_MedDRA_merge = tibble()
# AE_MedDRA_merge = tibble()
#
#
# if ( EUDRACT.MEDDRA_SAE.FILE != "" & file.exists( paste( EUDRACT.MEDDRA_SAE.LOCATION,
# EUDRACT.MEDDRA_SAE.FILE,
# sep="/") ) ) {
#
# SAE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_SAE.LOCATION,
# EUDRACT.MEDDRA_SAE.FILE,
# sep="/") ) %>%
# mutate( soc_term = ifelse( is.na(`SOC confirmed`),
# `SOC proposed`,
# `SOC confirmed` ) )
#
# SAE_MedDRA_encoding = SAE_MedDRA_encoding.in %>%
# inner_join( eudract::soc_code %>%
# select( soc_term,
# meddra ),
# by="soc_term" ) %>%
# dplyr::rename( soc = meddra )
#
#
# SAE_MedDRA_merge = SAE.data %>%
# full_join( SAE_MedDRA_encoding, by=c( "Label", "description" ) ) %>%
# mutate( pt = NA ) %>%
# dplyr::rename( subjid = Label ) %>%
# dplyr::rename( related = SAE_Reporting_SAE_SAE_CI_Cause ) %>%
# mutate( fatal = case_when(
# SAE_Reporting_SAE_2_SAE_Outcome == "Death" ~ 1,
# TRUE ~ 0 ) ) %>%
# mutate( serious = 1 ) %>%
# mutate( group = "Single" ) %>%
# mutate( term = soc_term ) %>%
# select( pt, # CAN BE NA
# subjid, # Label
# related, # SAE.data$SAE_Reporting_SAE_SAE_CI_Cause
# soc, # got this in SAE_MedDRA_encoding$soc
# fatal, # SAE.data$SAE_Reporting_SAE_2_SAE_Outcome
# serious, # this will be 1 for SAEs
# group, # which arm - what to do if there is only one?
# term # SAE_MedDRA_encoding$soc_term
# )
# } else {
# print_warning( "MedDRA coding not provided for SAEs" )
#
# }
#
#
# if ( EUDRACT.MEDDRA_SAE.FILE != "" & file.exists( paste( EUDRACT.MEDDRA_AE.LOCATION,
# EUDRACT.MEDDRA_AE.FILE,
# sep="/") ) ) {
#
# AE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_AE.LOCATION,
# EUDRACT.MEDDRA_AE.FILE,
# sep="/") ) %>%
# mutate( soc_term = ifelse( is.na(`SOC confirmed`),
# `SOC proposed`,
# `SOC confirmed` ) )
#
# AE_MedDRA_encoding = AE_MedDRA_encoding.in %>%
# left_join( eudract::soc_code %>%
# select( soc_term,
# meddra ),
# by="soc_term" ) %>%
# dplyr::rename( soc = meddra ) %>%
# select( -Label )
#
# AE_MedDRA_merge = AE.data %>%
# full_join( AE_MedDRA_encoding, by="description" ) %>%
# mutate( pt = NA ) %>%
# dplyr::rename( subjid = Label ) %>%
# ### Where is the relatedness information for the AEs?
# dplyr::mutate( related = "Yes" ) %>%
# mutate( fatal = 0 ) %>%
# mutate( serious = 0 ) %>%
# mutate( group = "Single" ) %>%
# mutate( term = soc_term ) %>%
# select( pt, # CAN BE NA
# subjid, # Label
# related, # SAE.data$SAE_Reporting_SAE_SAE_CI_Cause
# soc, # got this in SAE_MedDRA_encoding$soc
# fatal, # SAE.data$SAE_Reporting_SAE_2_SAE_Outcome
# serious, # this will be 1 for SAEs
# group, # which arm - what to do if there is only one?
# term # SAE_MedDRA_encoding$soc_term
# )
# } else {
# print_warning( "MedDRA coding not provided for SAEs" )
#
# }
#
# ALL_ADVERSE_EVENTS = SAE_MedDRA_merge %>%
# bind_rows( AE_MedDRA_merge ) %>%
# dplyr::mutate( related = case_when(
# related == "No" ~ FALSE,
# related == "Yes" ~ TRUE,
# TRUE ~ NA
# ))
#
#
#
# ### Enter the number of subjects in this reporting group
# ### exposed to the treatment. It is assumed that all subjects
# ### who received at least one dose of the treatment are
# ### included in the reporting group.
# subjectsExposed = c(Single=n.ITT)
#
# ### Enter the number of deaths in this reporting group.
# ### This includes deaths not related to the trial.
# # count of deaths not in the Safety data. Could be c(0,0)
# deathsExternal = c(Single=ITT_population.d %>%
# filter( Miscellaneous_Death_Trial_ID != "" ) %>%
# pull( Miscellaneous_Death_Trial_ID ) %>%
# unique %>% length)
#
#
# safety_statistics = safety_summary(ALL_ADVERSE_EVENTS,
# exposed=subjectsExposed,
# excess_deaths=deathsExternal,
# freq_threshold = 0
# )
#
# simple <- tempfile(fileext = ".xml")
# eudract_upload_file <- sprintf( "%s/safety_upload.xml", EUDRACT.XML.LOCATION )
#
# simple_safety_xml(safety_statistics, simple)
# eudract_convert(input=simple,
# output=eudract_upload_file)
#
# myschema <- xml2::read_xml(system.file("extdata","adverseEvents.xsd", package="eudract"))
# aes <- xml2::read_xml(eudract_upload_file)
# check <- xml2::xml_validate(aes,myschema)
# if(check){print("Validation against the schema has passed!")}
Generating data in ClinicalTrials.gov format
Tables to be in the following format. These can be generated from the
same Excel spreadsheets as were used to populate the EudraCT XML.
| Preferred Term | SOC term | N patients | N events |
| :---------------- | :-------------------------- | ---------: | -------: |
| Cardiac arrest | Cardiac disorders | 1 | 3 |
| Confusional state | Psychiatric disorders | 1 | 1 |
| Hematuria | Renal and urinary disorders | 2 | 2 |
| Hyperglycemia | Endocrine disorders | 1 | 1 |
There are to be two separate tables - one for SAEs and one for AEs.
Also needing to be generated is the number of patients who were affected
by at least one SAE and at least one AE.
#
# SAE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_SAE.LOCATION,
# EUDRACT.MEDDRA_SAE.FILE,
# sep="/") ) %>%
# mutate( soc_term = ifelse( is.na(`SOC confirmed`),
# `SOC proposed`,
# `SOC confirmed` ) ) %>%
# mutate( pt_term = ifelse( is.na(`Term confirmed`),
# `Term proposed`,
# `Term confirmed` ) )
#
# SAE_MedDRA_encoding = SAE_MedDRA_encoding.in %>%
# inner_join( eudract::soc_code %>%
# select( soc_term,
# meddra ),
# by="soc_term" ) %>%
# dplyr::rename( soc = meddra )
#
#
# SAE_MedDRA_encoding %>%
# group_by( pt_term, soc_term ) %>%
# summarise( n_patients = length( unique( Label ) ),
# n_events = n() ) %>%
# print_flextable
#
# SAE_MedDRA_encoding %>% pull( Label ) %>% unique %>% length
# AE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_AE.LOCATION,
# EUDRACT.MEDDRA_AE.FILE,
# sep="/") ) %>%
# mutate( soc_term = ifelse( is.na(`SOC confirmed`),
# `SOC proposed`,
# `SOC confirmed` ) ) %>%
# mutate( pt_term = ifelse( is.na(`Term confirmed`),
# `Term proposed`,
# `Term confirmed` ) )
#
# AE_MedDRA_encoding = AE_MedDRA_encoding.in %>%
# left_join( eudract::soc_code %>%
# select( soc_term,
# meddra ),
# by="soc_term" ) %>%
# dplyr::rename( soc = meddra ) %>%
# select( -Label )
#
# AE_MedDRA_merge = AE.data %>%
# full_join( AE_MedDRA_encoding, by="description" ) %>%
# select( pt_term, # CAN BE NA
# Label, # Label
# soc_term # SAE_MedDRA_encoding$soc_term
# )
#
# AE_MedDRA_merge %>%
# group_by( pt_term, soc_term ) %>%
# summarise( n_patients = length( unique( Label ) ),
# n_events = n() ) %>%
# print_flextable
# AE_MedDRA_merge %>% pull( Label ) %>% unique %>% length
LisaHopcroft/CTutils documentation built on Oct. 7, 2021, 11:08 p.m.
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EUdraCT report
The information required for the EudraCT upload is provided in this report. Also generated is an XML file for the adverse events upload.
This report is heavily reliant on the relevant information being completed in the settings.yml file. Please ensure that this file is fully population withthe relevant information.
vocab = CTutils::read_EudraCT_vocabulary() EUdraCT_vocabulary = vocab$EudraCT RMS_vocabulary = vocab$RMS print_info = function( info_var ) { info_var.string = deparse(substitute(info_var)) if( !exists(info_var.string) ) { info_var = "[MISSING]" } return( info_var ) }
Trial index
| | |
| ---------------------- | --------------------------------------- |
| Friendly description | r print_info(TRIAL.DESCRIPTION) |
Summary attachments to include:
- Final report to DMC
- Published article
Trial information
Trial identification
| | |
| ---------------------- | --------------------------------------- |
| EudraCT number | r print_info(TRIAL.ID.EudraCT) |
| Sponsor protocol code | r print_info(TRIAL.NAME) v. r print_info(PROTOCOL.VERSION) (dated r print_info(PROTOCOL.DATE)) |
| Full title of trial | r print_info(TRIAL.DESCRIPTION) |
Additional Study identifiers
| | |
| ---------------------- | --------------------------------------- |
| ISRCTN number | r print_info(TRIAL.ID.ISRCTN) |
| ClinicalTrials.gov number | r print_info( TRIAL.ID.NCT ) |
| WHO universal trial number | r print_info( TRIAL.ID.WHO ) |
Other identifiers provided:
other_ids = ls( pattern="TRIAL\\.ID\\..*" ) %>% discard( ~ str_detect(.x, pattern="ISRCTN|REC|EudraCT") ) if ( length( other_ids ) > 0 ) { other_ids.list = map( other_ids, get ) %>% set_names( other_ids %>% str_replace( "TRIAL\\.ID\\.", "" ) ) for( id_i in 1:length(other_ids.list) ) { cat( sprintf( "- **%s ID**: %s\n", names(other_ids.list)[id_i], other_ids.list[id_i]) ) } } else { cat( sprintf( "No other trial identifiers have been specified." )) }
Sponsor details
| | |
| ---------------------- | --------------------------------------- |
| Name of organisation | r print_info( SPONSOR.NAME ) |
| Street address | r print_info( SPONSOR.ADDRESS.STREET ) |
| Town/city | r print_info( SPONSOR.ADDRESS.CITY ) |
| Postcode | r print_info( SPONSOR.ADDRESS.POSTCODE ) |
Scientific contact point
| | |
| ---------------------- | --------------------------------------- |
| Name of organisation | r print_info( SPONSOR.SCIENTIFIC_CONTACT.ORGANISATION ) |
| Functional contact point name | r print_info( SPONSOR.SCIENTIFIC_CONTACT.NAME ) |
| Telephone number | +r print_info( SPONSOR.SCIENTIFIC_CONTACT.TELEPHONE.COUNTRY_CODE) r print_info( SPONSOR.SCIENTIFIC_CONTACT.TELEPHONE.NUMBER) |
| Email address | r print_info( SPONSOR.SCIENTIFIC_CONTACT.EMAIL) |
Public contact point
| | |
| ---------------------- | --------------------------------------- |
| Name of organisation | r print_info( SPONSOR.PUBLIC_CONTACT.ORGANISATION ) |
| Functional contact point name | r print_info( SPONSOR.PUBLIC_CONTACT.NAME ) |
| Telephone number | +r print_info( SPONSOR.PUBLIC_CONTACT.TELEPHONE.COUNTRY_CODE) r print_info( SPONSOR.PUBLIC_CONTACT.TELEPHONE.NUMBER) |
| Email address | r print_info( SPONSOR.PUBLIC_CONTACT.EMAIL) |
Paediatric regulatory details
Is trial part of an agreed paediatric investigation plan (PIP)? r print_info(EUDRACT.PAEDIATRIC.PIP)
Does article 45 of Regulation (EC) No 1901/2006 apply to this trial? r print_info(EUDRACT.PAEDIATRIC.ARTICLE_45_APPLIES)
Does article 46 of Regulation (EC) No 1901/2006 apply to this trial? r print_info(EUDRACT.PAEDIATRIC.ARTICLE_46_APPLIES)
Results analysis stage
# date_of_final_analysis = format(as.Date(EXTRACT.DATE,format="%Y-%m-%d"),format="%d %B %Y") # # primary_completion_date = ITT_population.d_extended %>% # select( Label, ends_with( "Date_Form_Comp" ) ) %>% # select( -contains( "Scan_review" ) ) %>% # pivot_longer( -Label ) %>% arrange( desc(dmy(value)) ) %>% # pull( value ) %>% # head(1) %>% # as.character() # # last_appointment_columns = grep( ls( ), pattern = "EUDRACT.LAST_APPOINTMENT", value=T ) %>% # lapply( get ) %>% unlist # # global_end_of_trial_date = ITT_population.d_extended %>% # select( Label, !!!syms( last_appointment_columns ) ) %>% # pivot_longer( -Label ) %>% # filter( value != "" ) %>% # arrange( desc( dmy(value) ) ) %>% # pull( value ) %>% # head(1) %>% # as.character()
| | |
| ---------------------- | --------------------------------------- |
| Analysis stage | r print_info( REPORT.TYPE ) |
| Date of interim/final analysis | r date_of_final_analysis |
| | |
| ---------------------- | --------------------------------------- |
| Is this the analysis of the primary completion data? | TBD |
| Primary completion date | r primary_completion_date |
| | |
| ---------------------- | --------------------------------------- |
| Global end of trial date reached? | Yes |
| Global end of trial date | r global_end_of_trial_date |
| Was the trial ended prematurely? | No |
General information about the trial
Main objective of the trial
r TRIAL.PRIMARY_OBJECTIVE
Actual start date of recruitment
r TRIAL.START_DATE %>% as.character
Long term follow-up planned?
r EUDRACT.LTFU_PLANNED
Independent data monitoring committee (IDMC) involvement?
r ( DMC.COMMITTEE %>% nrow ) > 1
Protection of trial subjects
r print_info( EUDRACT.SUBJECT_PROTECTION )
Background therapy
r print_info( EUDRACT.NON_TEST_OR_COMPARATOR_TREATMENTS )
Evidence for comparator(s)
r print_info( EUDRACT.COMPARATOR_RATIONALE )
Population of trial subjects
Subject number per country
Note that this information may need to be added to the relevant data frame.
if ( str_detect( "Country", EudraCT_data %>% colnames ) %>% any ) { countries = EudraCT_data %>% select( Label, Country ) %>% unique %>% pull( Country ) country_table = table( countries ) %>% as_tibble( ) country_table = country_table %>% add_row( countries = "Total", n = sum( country_table$n )) knitr::kable( country_table ) }
Age group breakdown for trial
agegroup.tibble = EudraCT_data$EudraCT_agegroup %>% discard( ~is.na(.x) ) %>% as_tibble %>% group_by( value, .drop=FALSE ) %>% dplyr::summarise( count = n() ) %>% mutate( value = as.character(value)) %>% dplyr::rename( Agegroup = value ) knitr::kable( agegroup.tibble )
Subject disposition
Recruitment details
r print_info(EUDRACT.RECRUITMENT_DETAILS)
Pre-assignment
Screening details
r print_info(EUDRACT.SCREENING_DETAILS)
Arm information (copy this section for each arm)
# patients_enrolled = ITT_population.d_extended %>% # filter( Label != "" ) %>% # pull(Label) %>% # unique () # # patients_dropouts = ITT_population.d_extended %>% # filter( Miscellaneous_Early_Termin_Trial_ID != "" ) %>% # select( Label, starts_with( "Miscellaneous_Early_Termin" ) ) %>% # pull( Label ) %>% # unique() # # n_enrolled = patients_enrolled %>% length( ) # n_completed = setdiff( patients_enrolled, patients_dropouts ) %>% length( )
Reasons for dropping out:
Disease progression:
# do_count( this_data = ITT_population.d_extended %>% # filter( Miscellaneous_Early_Termin_Trial_ID != "" ), # this_var = quo( Miscellaneous_Early_Termin_ET_Dis_Prog ), # key_for_variables = glossary.key, # missing_data_limit = PIPELINE.MISSING_DATA_LIMIT )
Other reasons:
# # do_count( this_data = ITT_population.d_extended %>% # filter( Miscellaneous_Early_Termin_Trial_ID != "" ) %>% # filter( Miscellaneous_Early_Termin_ET_Other_Reas == "Yes"), # this_var = quos( Miscellaneous_Early_Termin_ET_Wthdrw_Cons, # Miscellaneous_Early_Termin_ET_Pat_LTFU, # Miscellaneous_Early_Termin_ET_PI_Decision, # Miscellaneous_Early_Termin_ET_Non_Comply, # Miscellaneous_Early_Termin_ET_Not_Start, # Miscellaneous_Early_Termin_ET_Death ), # key_for_variables = glossary.key, # missing_data_limit = PIPELINE.MISSING_DATA_LIMIT ) # # reason_per_patient = ITT_population.d_extended %>% # filter( Miscellaneous_Early_Termin_Trial_ID != "" ) %>% # filter( Miscellaneous_Early_Termin_ET_Other_Reas == "Yes") %>% # select( Label, # Miscellaneous_Early_Termin_ET_Wthdrw_Cons, # Miscellaneous_Early_Termin_ET_Pat_LTFU, # Miscellaneous_Early_Termin_ET_PI_Decision, # Miscellaneous_Early_Termin_ET_Non_Comply, # Miscellaneous_Early_Termin_ET_Not_Start, # Miscellaneous_Early_Termin_ET_Death ) # # reason_check = reason_per_patient %>% # pivot_longer( -Label, # names_to = "reason", # values_to = "response") %>% # group_by( Label ) %>% # summarise( REASON_PRESENT = any( response == "Yes"))
r ifelse( reason_check %>% pull( REASON_PRESENT ) %>% all, "", print_error( "Not all patients have a reason provided." ) )
# knitr::kable(PERIOD.INFO %>% # mutate(section_header = sprintf("**%s**",section_header)) %>% # mutate(section_header = str_replace(section_header,"\\*\\*\\*\\*","")) %>% # mutate(question = sprintf("**%s**",question)) %>% # mutate(question = str_replace(question,"\\*\\*\\*\\*","")), # col.names=rep.int("",ncol(PERIOD.INFO)))
Number of patients in each population (e.g., ITT, safety etc)
Age/gender counts per population
ITT population
# ITT_population.d %>% pull( EudraCT_agegroup ) %>% table # # do_summary(ITT_population.d, # quo( Age), # key_for_variables = glossary.key, # missing_data_limit = PIPELINE.MISSING_DATA_LIMIT ) # # ITT_population.d %>% pull( Enrolment_Trial_Enrolment_Sex ) %>% table
Safety/evaluable population
# treated_population.d %>% pull( EudraCT_agegroup ) %>% table # # do_summary(treated_population.d, # quo( Age), # key_for_variables = glossary.key, # missing_data_limit = PIPELINE.MISSING_DATA_LIMIT ) # # treated_population.d %>% pull( Enrolment_Trial_Enrolment_Sex ) %>% table
Surgical population
# # ITT_population.d %>% filter( SURGERY_PERFORMED == "Yes" ) %>% pull( EudraCT_agegroup ) %>% table # # do_summary(ITT_population.d %>% filter( SURGERY_PERFORMED == "Yes" ), # quo( Age), # key_for_variables = glossary.key, # missing_data_limit = PIPELINE.MISSING_DATA_LIMIT ) # # ITT_population.d %>% pull( Enrolment_Trial_Enrolment_Sex ) %>% table
Generating the SAE and AE XML uploads for EudraCT
### Example shown below # SAE.data = ITT_population.d_extended %>% # filter( !is.na(SAE_Reporting_SAE_SAE_Num) ) %>% # select( SAE_Reporting_SAE_SAE_Num, # Label, # # # SAE_Reporting_SAE_SAE_Axi_Related, # SAE_Reporting_SAE_SAE_Oth_Related, # SAE_Reporting_SAE_SAE_Oth_Spec, # SAE_Reporting_SAE_SAE_Pro_Related, # SAE_Reporting_SAE_SAE_Pro_Spec, # # SAE_Reporting_SAE_2_SAE_Ax_TDD, # SAE_Reporting_SAE_2_SAE_Ax_Freq, # SAE_Reporting_SAE_2_SAE_Ax_Start, # SAE_Reporting_SAE_2_SAE_Ax_Stop, # SAE_Reporting_SAE_SAE_Event, # SAE_Reporting_SAE_SAE_Symp_Start, # # # SAE type? # SAE_Reporting_SAE_SAE_Death, # SAE_Reporting_SAE_SAE_Hosp, # SAE_Reporting_SAE_SAE_Defect, # SAE_Reporting_SAE_SAE_Life, # SAE_Reporting_SAE_SAE_Incapacity, # SAE_Reporting_SAE_SAE_Important, # # SAE_Reporting_SAE_SAE_Grade, # SAE_Reporting_SAE_SAE_Axi_Related, # site investigator causailty # SAE_Reporting_SAE_SAE_CI_Cause, # CI causality # SAE_Reporting_SAE_SAE_Exp_Unexp, # # ### SUSAR? # #SAE_Reporting_SAE_SAE_Exp_Unexp = Unexpected # #SAE_Reporting_SAE_SAE_CI_Cause = YES # # SAE_Reporting_SAE_2_SAE_Outcome # ) %>% # group_by( SAE_Reporting_SAE_SAE_Num ) %>% # mutate( SUSAR = ifelse(SAE_Reporting_SAE_SAE_Exp_Unexp=="Unexpected" & # SAE_Reporting_SAE_SAE_CI_Cause=="Yes", # "Yes","No"), # SAEtype = paste( # ifelse(SAE_Reporting_SAE_SAE_Death=="Yes", # "Death", # ""), # ifelse(SAE_Reporting_SAE_SAE_Hosp =="Yes", # "Hospitalisation", # ""), # ifelse(SAE_Reporting_SAE_SAE_Defect=="Yes", # "Birth defect", # ""), # ifelse(SAE_Reporting_SAE_SAE_Life=="Yes", # "Life threatening", # ""), # ifelse(SAE_Reporting_SAE_SAE_Incapacity=="Yes", # "Disability/incapacity", # ""), # collapse="\n" # ) ) %>% # select( -SAE_Reporting_SAE_SAE_Death, # -SAE_Reporting_SAE_SAE_Hosp, # -SAE_Reporting_SAE_SAE_Defect, # -SAE_Reporting_SAE_SAE_Life, # -SAE_Reporting_SAE_SAE_Incapacity, # -SAE_Reporting_SAE_SAE_Important) %>% # # mutate( suspect = paste( # ifelse(SAE_Reporting_SAE_SAE_Pro_Related=="Yes", # sprintf( "%s: %s", # glossary.key[["SAE_Reporting_SAE_SAE_Pro_Related"]], # SAE_Reporting_SAE_SAE_Pro_Spec), # ""), # ifelse(SAE_Reporting_SAE_SAE_Oth_Related=="Yes", # sprintf( "%s: %s", # glossary.key[["SAE_Reporting_SAE_SAE_Oth_Related"]], # SAE_Reporting_SAE_SAE_Oth_Spec), # ""), # collapse="\n" # ) ) %>% # select( -SAE_Reporting_SAE_SAE_Oth_Related, # -SAE_Reporting_SAE_SAE_Oth_Spec, # -SAE_Reporting_SAE_SAE_Pro_Related, # -SAE_Reporting_SAE_SAE_Pro_Spec ) %>% # mutate(IMPdose = sprintf( "%s, %s", # SAE_Reporting_SAE_2_SAE_Ax_TDD, # SAE_Reporting_SAE_2_SAE_Ax_Freq)) %>% # select( -SAE_Reporting_SAE_2_SAE_Ax_TDD, # -SAE_Reporting_SAE_2_SAE_Ax_Freq ) %>% # mutate( studydrug = sprintf( "Started: %s\nStopped: %s", # dmy(SAE_Reporting_SAE_2_SAE_Ax_Start), # dmy(SAE_Reporting_SAE_2_SAE_Ax_Stop) )) %>% # select( -SAE_Reporting_SAE_2_SAE_Ax_Start, # -SAE_Reporting_SAE_2_SAE_Ax_Stop ) %>% # select( SAE_Reporting_SAE_SAE_Num, # Label, # suspect, # IMPdose, # studydrug, # SAE_Reporting_SAE_SAE_Event, # SAE_Reporting_SAE_SAE_Symp_Start, # SAEtype, # SAE_Reporting_SAE_SAE_Grade, # SAE_Reporting_SAE_SAE_Axi_Related, # SAE_Reporting_SAE_SAE_CI_Cause, # SAE_Reporting_SAE_SAE_Exp_Unexp, # SUSAR, # SAE_Reporting_SAE_2_SAE_Outcome # ) %>% # arrange( SAE_Reporting_SAE_SAE_Num ) %>% # mutate( description = SAE_Reporting_SAE_SAE_Event ) # # # # print_flextable( SAE.data, # column_recode = list( # Label = "Patient", # SAE_Reporting_SAE_SAE_Num = "SCTRU SAE No.", # description = "SAE event term", # SAE_Reporting_SAE_SAE_Grade = "CTC Grade V4.0", # SAE_Reporting_SAE_SAE_Symp_Start = "Onset Date", # SAE_Reporting_SAE_SAE_Axi_Related = "Causality Assessment of Site I", # SAE_Reporting_SAE_SAE_CI_Cause = "Causality Assessment of CI", # SAE_Reporting_SAE_SAE_Exp_Unexp = "Expectedness Assessment of CI", # SAE_Reporting_SAE_2_SAE_Outcome = "SAE Outcome", # SUSAR = "SUSAR?", # SAEtype = "SAE Type", # suspect = "Suspect drug/procedure", # IMPdose = "IMP Dose & route", # studydrug = "Date study drug started and stopped" # )) # # # AE.data = toxicity_ALL.d %>% filter( value > 0 ) %>% # dplyr::rename( description = toxicity_description )
# if ( EUDRACT.MEDDRA_SAE.FILE != "" & file.exists( paste( EUDRACT.MEDDRA_SAE.LOCATION, # EUDRACT.MEDDRA_SAE.FILE, # sep="/") ) ) { # # SAE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_SAE.LOCATION, # EUDRACT.MEDDRA_SAE.FILE, # sep="/") ) %>% # mutate( soc_term = ifelse( is.na(`SOC confirmed`), # `SOC proposed`, # `SOC confirmed` ) ) # # SAE_MedDRA_encoding = SAE_MedDRA_encoding.in %>% # inner_join( eudract::soc_code %>% # select( soc_term, # meddra ), # by="soc_term" ) %>% # dplyr::rename( soc = meddra ) # # # SAE_MedDRA_merge = SAE.data %>% # inner_join( SAE_MedDRA_encoding, by=c( "Label", "description" ) ) %>% # mutate( pt = NA ) %>% # dplyr::rename( subjid = Label ) %>% # dplyr::rename( related = SAE_Reporting_SAE_SAE_CI_Cause ) %>% # mutate( fatal = case_when( # SAE_Reporting_SAE_2_SAE_Outcome == "Death" ~ 1, # TRUE ~ 0 ) ) %>% # mutate( serious = 1 ) %>% # mutate( group = "Single" ) %>% # mutate( term = soc_term ) %>% # select( pt, # CAN BE NA # subjid, # Label # related, # SAE.data$SAE_Reporting_SAE_SAE_CI_Cause # soc, # got this in SAE_MedDRA_encoding$soc # fatal, # SAE.data$SAE_Reporting_SAE_2_SAE_Outcome # serious, # this will be 1 for SAEs # group, # which arm - what to do if there is only one? # term # SAE_MedDRA_encoding$soc_term # ) # } # # # SAE_MedDRA_merge = tibble() # AE_MedDRA_merge = tibble() # # # if ( EUDRACT.MEDDRA_SAE.FILE != "" & file.exists( paste( EUDRACT.MEDDRA_SAE.LOCATION, # EUDRACT.MEDDRA_SAE.FILE, # sep="/") ) ) { # # SAE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_SAE.LOCATION, # EUDRACT.MEDDRA_SAE.FILE, # sep="/") ) %>% # mutate( soc_term = ifelse( is.na(`SOC confirmed`), # `SOC proposed`, # `SOC confirmed` ) ) # # SAE_MedDRA_encoding = SAE_MedDRA_encoding.in %>% # inner_join( eudract::soc_code %>% # select( soc_term, # meddra ), # by="soc_term" ) %>% # dplyr::rename( soc = meddra ) # # # SAE_MedDRA_merge = SAE.data %>% # full_join( SAE_MedDRA_encoding, by=c( "Label", "description" ) ) %>% # mutate( pt = NA ) %>% # dplyr::rename( subjid = Label ) %>% # dplyr::rename( related = SAE_Reporting_SAE_SAE_CI_Cause ) %>% # mutate( fatal = case_when( # SAE_Reporting_SAE_2_SAE_Outcome == "Death" ~ 1, # TRUE ~ 0 ) ) %>% # mutate( serious = 1 ) %>% # mutate( group = "Single" ) %>% # mutate( term = soc_term ) %>% # select( pt, # CAN BE NA # subjid, # Label # related, # SAE.data$SAE_Reporting_SAE_SAE_CI_Cause # soc, # got this in SAE_MedDRA_encoding$soc # fatal, # SAE.data$SAE_Reporting_SAE_2_SAE_Outcome # serious, # this will be 1 for SAEs # group, # which arm - what to do if there is only one? # term # SAE_MedDRA_encoding$soc_term # ) # } else { # print_warning( "MedDRA coding not provided for SAEs" ) # # } # # # if ( EUDRACT.MEDDRA_SAE.FILE != "" & file.exists( paste( EUDRACT.MEDDRA_AE.LOCATION, # EUDRACT.MEDDRA_AE.FILE, # sep="/") ) ) { # # AE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_AE.LOCATION, # EUDRACT.MEDDRA_AE.FILE, # sep="/") ) %>% # mutate( soc_term = ifelse( is.na(`SOC confirmed`), # `SOC proposed`, # `SOC confirmed` ) ) # # AE_MedDRA_encoding = AE_MedDRA_encoding.in %>% # left_join( eudract::soc_code %>% # select( soc_term, # meddra ), # by="soc_term" ) %>% # dplyr::rename( soc = meddra ) %>% # select( -Label ) # # AE_MedDRA_merge = AE.data %>% # full_join( AE_MedDRA_encoding, by="description" ) %>% # mutate( pt = NA ) %>% # dplyr::rename( subjid = Label ) %>% # ### Where is the relatedness information for the AEs? # dplyr::mutate( related = "Yes" ) %>% # mutate( fatal = 0 ) %>% # mutate( serious = 0 ) %>% # mutate( group = "Single" ) %>% # mutate( term = soc_term ) %>% # select( pt, # CAN BE NA # subjid, # Label # related, # SAE.data$SAE_Reporting_SAE_SAE_CI_Cause # soc, # got this in SAE_MedDRA_encoding$soc # fatal, # SAE.data$SAE_Reporting_SAE_2_SAE_Outcome # serious, # this will be 1 for SAEs # group, # which arm - what to do if there is only one? # term # SAE_MedDRA_encoding$soc_term # ) # } else { # print_warning( "MedDRA coding not provided for SAEs" ) # # } # # ALL_ADVERSE_EVENTS = SAE_MedDRA_merge %>% # bind_rows( AE_MedDRA_merge ) %>% # dplyr::mutate( related = case_when( # related == "No" ~ FALSE, # related == "Yes" ~ TRUE, # TRUE ~ NA # )) # # # # ### Enter the number of subjects in this reporting group # ### exposed to the treatment. It is assumed that all subjects # ### who received at least one dose of the treatment are # ### included in the reporting group. # subjectsExposed = c(Single=n.ITT) # # ### Enter the number of deaths in this reporting group. # ### This includes deaths not related to the trial. # # count of deaths not in the Safety data. Could be c(0,0) # deathsExternal = c(Single=ITT_population.d %>% # filter( Miscellaneous_Death_Trial_ID != "" ) %>% # pull( Miscellaneous_Death_Trial_ID ) %>% # unique %>% length) # # # safety_statistics = safety_summary(ALL_ADVERSE_EVENTS, # exposed=subjectsExposed, # excess_deaths=deathsExternal, # freq_threshold = 0 # ) # # simple <- tempfile(fileext = ".xml") # eudract_upload_file <- sprintf( "%s/safety_upload.xml", EUDRACT.XML.LOCATION ) # # simple_safety_xml(safety_statistics, simple) # eudract_convert(input=simple, # output=eudract_upload_file) # # myschema <- xml2::read_xml(system.file("extdata","adverseEvents.xsd", package="eudract")) # aes <- xml2::read_xml(eudract_upload_file) # check <- xml2::xml_validate(aes,myschema) # if(check){print("Validation against the schema has passed!")}
Generating data in ClinicalTrials.gov format
Tables to be in the following format. These can be generated from the same Excel spreadsheets as were used to populate the EudraCT XML.
| Preferred Term | SOC term | N patients | N events | | :---------------- | :-------------------------- | ---------: | -------: | | Cardiac arrest | Cardiac disorders | 1 | 3 | | Confusional state | Psychiatric disorders | 1 | 1 | | Hematuria | Renal and urinary disorders | 2 | 2 | | Hyperglycemia | Endocrine disorders | 1 | 1 |
There are to be two separate tables - one for SAEs and one for AEs.
Also needing to be generated is the number of patients who were affected by at least one SAE and at least one AE.
# # SAE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_SAE.LOCATION, # EUDRACT.MEDDRA_SAE.FILE, # sep="/") ) %>% # mutate( soc_term = ifelse( is.na(`SOC confirmed`), # `SOC proposed`, # `SOC confirmed` ) ) %>% # mutate( pt_term = ifelse( is.na(`Term confirmed`), # `Term proposed`, # `Term confirmed` ) ) # # SAE_MedDRA_encoding = SAE_MedDRA_encoding.in %>% # inner_join( eudract::soc_code %>% # select( soc_term, # meddra ), # by="soc_term" ) %>% # dplyr::rename( soc = meddra ) # # # SAE_MedDRA_encoding %>% # group_by( pt_term, soc_term ) %>% # summarise( n_patients = length( unique( Label ) ), # n_events = n() ) %>% # print_flextable # # SAE_MedDRA_encoding %>% pull( Label ) %>% unique %>% length
# AE_MedDRA_encoding.in = read_xlsx( paste( EUDRACT.MEDDRA_AE.LOCATION, # EUDRACT.MEDDRA_AE.FILE, # sep="/") ) %>% # mutate( soc_term = ifelse( is.na(`SOC confirmed`), # `SOC proposed`, # `SOC confirmed` ) ) %>% # mutate( pt_term = ifelse( is.na(`Term confirmed`), # `Term proposed`, # `Term confirmed` ) ) # # AE_MedDRA_encoding = AE_MedDRA_encoding.in %>% # left_join( eudract::soc_code %>% # select( soc_term, # meddra ), # by="soc_term" ) %>% # dplyr::rename( soc = meddra ) %>% # select( -Label ) # # AE_MedDRA_merge = AE.data %>% # full_join( AE_MedDRA_encoding, by="description" ) %>% # select( pt_term, # CAN BE NA # Label, # Label # soc_term # SAE_MedDRA_encoding$soc_term # ) # # AE_MedDRA_merge %>% # group_by( pt_term, soc_term ) %>% # summarise( n_patients = length( unique( Label ) ), # n_events = n() ) %>% # print_flextable # AE_MedDRA_merge %>% pull( Label ) %>% unique %>% length
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