Consolidate findings and write role-based Excel reports

Description

Merges new findings with previously saved issues, incorporates reviewer feedback from role-separated feedback folders, and writes four role-specific reports plus all_open.xlsx and all_closed.xlsx to cfg$reports.

Usage

build_reports(cfg, state)

Arguments

Details

Feedback loop: After distributing reports to reviewers, they place updated files in the appropriate subfolder under cfg$feedback:

• feedback/DM/ - Data Management reviewer

• feedback/MW/ - Medical Writing reviewer

• feedback/SDTM/ - SDTM programmer

• feedback/ADAM/ - ADaM programmer

On the next run, build_reports() reads the most recent file from each feedback folder, merges analyst_note, review_note and status updates back into the findings, and overwrites the reports with the merged version.

Value

Invisibly NULL. Reports are written to cfg$reports.

Examples

tmp_rep <- tempdir()
cfg <- list(
  rule_registry = system.file("extdata", "rule_registry.xlsx",
                              package = "rCoreGage"),
  trial_checks  = tmp_rep,
  study_checks  = tmp_rep,
  inputs        = tmp_rep,
  reports       = tmp_rep,
  feedback      = tmp_rep
)
state         <- setup_coregage(cfg)
state$domains <- load_inputs(cfg)
state         <- run_checks(cfg, state)
build_reports(cfg, state)

Collect findings from a single check into the master issues table

Description

Called at the end of every individual check block. Validates the findings data frame, counts valid observations via count_valid, and appends to state$issues and state$summary_log.

Usage

collect_findings(
  state,
  df,
  id,
  desc_col = "description",
  sobs = TRUE,
  unblind_codes = character(0)
)

Arguments

Value

Updated state object.

Examples

# Build a minimal state object
state <- list(
  issues = data.frame(id = character(0), subj_id = character(0),
                      vis_id = numeric(0), description = character(0),
                      review = character(0), stringsAsFactors = FALSE),
  summary_log = data.frame(headlink = character(0), nu = integer(0),
                           rule_set = character(0), sobs = character(0),
                           stringsAsFactors = FALSE)
)

# A findings data frame produced by a check
MY_CHECK <- data.frame(
  subj_id     = c("SUBJ-001", "SUBJ-002"),
  vis_id      = NA_real_,
  description = c("AESEV missing for RASH", "AESEV missing for HEADACHE"),
  stringsAsFactors = FALSE
)

state <- collect_findings(state, MY_CHECK, id = "AECHK001")

Count valid observations in a findings data frame

Description

Returns the number of rows in df, optionally excluding rows that could unblind the study (negative subject IDs where none of the unblinding topic codes appear in the description).

Usage

count_valid(df, unblind_codes = character(0))

Arguments

Value

Integer. Number of valid rows.

Examples

df <- data.frame(subj_id = c("001", "-002"),
                 description = c("Issue A", "Issue B"),
                 stringsAsFactors = FALSE)
count_valid(df)

Create a new CoreGage project

Description

Scaffolds a complete CoreGage project folder structure at the specified path and copies all required template files from the package installation. After running this function, open the generated .Rproj file in RStudio and start working.

Usage

create_project(name, path, overwrite = FALSE)

Arguments

Value

Invisibly returns the full path to the created project.

Examples

proj_path <- file.path(tempdir(), "example_project")
dir.create(proj_path, showWarnings = FALSE)
create_project(name = "TRIAL_ABC", path = proj_path)

Load domain input data files

Description

Reads every .csv and (optionally) .sas7bdat file from the inputs/ folder into a named list. The name of each element is the lowercase filename without extension (e.g. AE.csv becomes domains$ae). Drop a new domain file into inputs/ and it is picked up automatically on the next run - no code change required.

Usage

load_inputs(cfg)

Arguments

Value

A named list of data frames, one per domain file found.

Examples

# Create a temporary inputs folder with a sample CSV
tmp_inp <- tempdir()
write.csv(
  data.frame(USUBJID = "SUBJ-001", AETERM = "RASH"),
  file.path(tmp_inp, "AE.csv"), row.names = FALSE
)
cfg     <- list(inputs = tmp_inp)
domains <- load_inputs(cfg)
# domains$ae contains the loaded Adverse Events data

Execute all active check programs

Description

Loops through all active rule sets defined in the rule registry, sources each check script from the appropriate folder (trial checks from cfg$trial_checks, study checks from cfg$study_checks), and calls check_RULESET(state, cfg), where RULESET is the value from the Rule_Set column in rule_registry.xlsx.

Usage

run_checks(cfg, state)

Arguments

Value

Updated state object with issues and summary_log populated.

Examples

tmp_rep <- tempdir()
cfg <- list(
  rule_registry = system.file("extdata", "rule_registry.xlsx",
                              package = "rCoreGage"),
  trial_checks  = tmp_rep,
  study_checks  = tmp_rep,
  inputs        = tmp_rep,
  reports       = tmp_rep,
  feedback      = tmp_rep
)
state          <- setup_coregage(cfg)
state$domains  <- load_inputs(cfg)
state          <- run_checks(cfg, state)

Initialise a CoreGage run session

Description

Reads the rule registry (rule_registry.xlsx), builds the active rule switch vector, and initialises all empty master tables required for a check run. This is the first function called in every run.

Usage

setup_coregage(cfg)

Arguments

Value

A named list (the state object) containing: rule_registry, active_rules, session, issues, summary_log, review_log.

Examples

tmp_rep <- tempdir()
cfg <- list(
  rule_registry = system.file("extdata", "rule_registry.xlsx",
                              package = "rCoreGage"),
  trial_checks  = tmp_rep,
  study_checks  = tmp_rep,
  inputs        = tmp_rep,
  reports       = tmp_rep,
  feedback      = tmp_rep
)
state <- setup_coregage(cfg)