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sasif

SAS IF-Style Data Step Logic for R — Improving readability and consistency in SDTM & ADaM derivations.

sasif lets clinical programmers write one condition that governs multiple variable assignments in a single block — just like SAS IF ... THEN DO. No repeated conditions. No logic drift. No maintenance burden.

The Core Problem

In traditional R, every variable assignment needs its own repeated condition:

# ❌ Traditional R (case_when) — condition repeated for EVERY variable
adsl <- adsl %>% mutate(
  SAFFL   = case_when(ACTARMCD == "TRTA" ~ "Y"),
  SAFFLN  = case_when(ACTARMCD == "TRTA" ~ 1),
  TRT01A  = case_when(ACTARMCD == "TRTA" ~ ACTARMCD),
  TRT01AN = case_when(ACTARMCD == "TRTA" ~ 1),
  TRTSDT  = case_when(ACTARMCD == "TRTA" ~ as.Date(RFSTDTC, "%Y-%m-%d")),
  TRTEDT  = case_when(ACTARMCD == "TRTA" ~ as.Date(RFENDTC, "%Y-%m-%d")),
  ITTFL   = case_when(ACTARMCD == "TRTA" ~ "Y"),
  FASFL   = case_when(ACTARMCD == "TRTA" ~ "Y"),
  RANDFL  = case_when(ACTARMCD == "TRTA" ~ "Y"),
  PPFL    = case_when(ACTARMCD == "TRTA" ~ "Y")
  # condition repeated 10 times — high QC risk if any diverge
)

If one condition is ever updated, you must find and change it in every single line. Miss one and your derivation silently diverges. This is a real QC risk in regulated clinical trial data.

The sasif Solution

One condition. All assignments grouped together. Just like SAS:

# ✅ sasif — condition written ONCE, governs all assignments
library(sasif)

ADSL <- data_step(adsl,
  if_do(ACTARMCD == "TRTA",
    SAFFL   = "Y",
    SAFFLN  = 1,
    TRT01A  = ACTARMCD,
    TRT01AN = 1,
    TRTSDT  = as.Date(RFSTDTC, "%Y-%m-%d"),
    TRTEDT  = as.Date(RFENDTC, "%Y-%m-%d"),
    ITTFL   = "Y",
    FASFL   = "Y",
    RANDFL  = "Y",
    PPFL    = "Y"
  )
)

Clean. Readable. Audit-friendly. Identical in intent to SAS IF ... THEN DO.

Installation

# Install from CRAN
install.packages("sasif")

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("chandrt23-lang/sasif")

Note: sasif requires a data.table as input. Convert with setDT(df) before calling data_step().

Functions

Function	SAS Equivalent	Description
`data_step()`	`DATA` step	Opens a SAS-style processing block on a `data.table`
`if_do()`	`IF ... THEN DO`	One condition governs multiple variable assignments
`else_if_do()`	`ELSE IF ... THEN DO`	Secondary condition — skipped if prior block matched
`else_do()`	`ELSE DO`	Default assignments when all prior conditions are FALSE
`delete_if()`	`DELETE`	Removes rows where condition is TRUE
`if_independent()`	Multiple standalone `IF`	Each condition evaluated independently — not a chain

Clinical Examples

Example 1 — ADSL: Multiple Population Flags in One Block

The most common use case — one treatment condition drives 10 variable assignments simultaneously:

ADSL <- data_step(adsl,
  if_do(ACTARMCD == "TRTA",
    SAFFL   = "Y",
    SAFFLN  = 1,
    TRT01A  = ACTARMCD,
    TRT01AN = 1,
    TRTSDT  = as.Date(RFSTDTC, "%Y-%m-%d"),
    TRTEDT  = as.Date(RFENDTC, "%Y-%m-%d"),
    ITTFL   = "Y",
    FASFL   = "Y",
    RANDFL  = "Y",
    PPFL    = "Y"
  )
)

Example 2 — ADSL: Age Category with IF / ELSE IF / ELSE

Mutually exclusive chain — first matching condition wins, others are skipped. Both AGECAT (character) and AGECATN (numeric) are derived together:

ADSL <- data_step(adsl,
  if_do(AGE <= 45,
    AGECAT  = "YOUNG",
    AGECATN = 1
  ),
  else_if_do(AGE <= 70,
    AGECAT  = "MIDDLE",
    AGECATN = 2
  ),
  else_do(
    AGECAT  = "OLD",
    AGECATN = 3
  )
)

Compare this to data.table nested fifelse() — which forces you to repeat the condition separately for each variable:

# ❌ data.table nested fifelse — condition repeated per variable
adlb[, `:=`(
  AGECAT  = fifelse(AGE <= 45, "YOUNG", fifelse(AGE <= 70, "MIDDLE", "OLD")),
  AGECATN = fifelse(AGE <= 45, 1L,      fifelse(AGE <= 70, 2L,       3L))
)]

Example 3 — ADLB: Lab Categorisation (Character + Numeric Together)

Derive both the category label and its numeric code from one condition block:

out <- data_step(adlb,
  if_do(LBTESTCD == "ALB" & AVAL < ANRLO,
    ALBCAT  = "LOW",
    ALBCATN = 1
  ),
  else_if_do(LBTESTCD == "ALB" & AVAL > ANRHI,
    ALBCAT  = "HIGH",
    ALBCATN = 2
  ),
  else_do(
    ALBCAT  = "NORMAL",
    ALBCATN = 3
  )
)

Example 4 — ADAE: Treatment-Emergent Flag

Flag adverse events that started on or after treatment start date:

ADAE <- data_step(adae,
  if_do(ASTDT >= TRTSDT & ASTDT <= TRTEDT,
    TRTEMFL = "Y",
    TRTEMA  = AEDECOD
  )
)

Example 5 — ADSL: Multi-Arm Treatment Assignment

Assign treatment label, numeric code, and start date together per arm:

ADSL <- data_step(adsl,
  if_do(ACTARMCD == "TRTA",
    TRT01A  = "Treatment A",
    TRT01AN = 1,
    TRTSDT  = as.Date(RFSTDTC, "%Y-%m-%d")
  ),
  else_if_do(ACTARMCD == "TRTB",
    TRT01A  = "Treatment B",
    TRT01AN = 2,
    TRTSDT  = as.Date(RFSTDTC, "%Y-%m-%d")
  ),
  else_do(
    TRT01A  = "Placebo",
    TRT01AN = 99,
    TRTSDT  = as.Date(RFSTDTC, "%Y-%m-%d")
  )
)

Example 6 — ADLB: Independent Flags (if_independent)

Use if_independent() when conditions are not mutually exclusive — each is evaluated on its own, so multiple flags can apply to the same row:

out <- data_step(adlb,
  if_independent(AVAL < ANRLO,  LOWNFL = "Y"),
  if_independent(AVAL > ANRHI,  HINFL  = "Y"),
  if_independent(LBTESTCD == "ALB", ALBFL = "Y")
)

Important: Do not mix if_do() chains with if_independent() on the same variable — if_independent() runs after the chain and will overwrite it. Use one approach consistently per variable.

Example 7 — DELETE: Remove Unwanted Rows

Remove screen failures and unscheduled visits explicitly:

# Remove screen failure subjects
ADSL <- data_step(adsl,
  delete_if(ACTARMCD == "SCRNFAIL")
)

# Remove records with missing test codes and unscheduled visits
ADLB <- data_step(adlb,
  delete_if(is.na(LBTESTCD)),
  delete_if(VISIT == "UNSCHEDULED")
)

Why Not case_when() or fifelse()?

Feature	sasif	case_when()	data.table fifelse()
One condition → multiple variables	✅ Natural	❌ Repeated per variable	❌ Repeated per variable
IF / ELSE IF / ELSE chain	✅ Native	⚠️ Simulated	⚠️ Nested
SAS programmer readability	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐
Risk of condition drift across variables	Low ✅	High ⚠️	High ⚠️
Vectorized performance	✅	✅	✅
Audit-friendly derivation flow	✅	⚠️	⚠️

When to Use sasif

Deriving multiple population flags (SAFFL, ITTFL, FASFL, RANDFL) from one condition
Multi-variable ADaM derivations where the same condition governs several assignments
Migrating SAS IF ... THEN DO blocks directly to R
QC-sensitive code where auditability and readability matter

When NOT to Use sasif

sasif is focused on conditional derivation logic. Use standard R packages for:

RETAIN / stateful accumulation → use data.table or base R
LAG / previous-row values → use shift() in data.table
ARRAY processing → use lapply or data.table column operations
By-group stateful logic → use data.table by= syntax

Validation

A formal IQ/OQ/PQ Validation Document is available for GxP-regulated environments, covering all 6 functions in accordance with:

FDA 21 CFR Part 11
ICH E6(R2) Good Clinical Practice
GAMP 5

Contact the maintainer to request the validation document.

Getting Help

CRAN documentation: https://CRAN.R-project.org/package=sasif
Bug reports & feature requests: https://github.com/chandrt23-lang/sasif/issues

Citation

citation("sasif")

License

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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