Introduction

This short “how-to” is written for researchers who hit the same wall you did:

• You want multivariable fractional polynomials (MFP) for continuous predictors (non-linearity handling),
• but mfp does not accept factor-variable syntax (i.var, c.var##c.var),
• and it often breaks inside mi estimate unless you handle categorical variables correctly.

The solution is usually simple: pre-create dummy variables (best practice) or use xi: (quick fix). mfpa is an alternative when you need factor variables.

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1) What is a dummy variable?

A dummy variable is a 0/1 indicator representing membership in a category.

• For a binary predictor (e.g., male/female), one dummy is enough.
• For a k-level categorical predictor (e.g., 3 levels), you need k−1 dummies (choose a reference level). This avoids perfect collinearity (“dummy variable trap”).

Key rule: preserve missingness

In Stata, expressions like (thal==1) will return 0 when thal is missing (because . is not equal to 1). That silently misclassifies missing values.

✅ Always do:

gen thal_trait = (thal==1) if !missing(thal)
replace thal_trait = . if missing(thal)

Or simpler (same effect):

gen thal_trait = .
replace thal_trait = 1 if thal==1
replace thal_trait = 0 if thal!=. & thal!=1

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2) How to create dummy variables (three common methods)

Method A — Manual gen (most transparent; best for papers)

Example: thal has three levels: 0 = no, 1 = trait, 2 = disease. Reference = “no thalassemia” (0). Create two dummies:

gen thal_trait   = (thal==1) if !missing(thal)
gen thal_disease = (thal==2) if !missing(thal)

label var thal_trait   "Thalassemia trait"
label var thal_disease "Thalassemia disease"

Example: cirrhosis has 3 levels: 0=no, 1=compensated, 2=decompensatedReference = 0:

gen cir_comp = (cirrhos==1) if !missing(cirrhos)
gen cir_decomp = (cirrhos==2) if !missing(cirrhos)

label var cir_comp   "Compensated cirrhosis"
label var cir_decomp "Decompensated cirrhosis"

✅ This is the easiest to interpret and to report.

Method B — tab var, gen(prefix) (fast)

tab thal, gen(thal_)

Stata will create thal_1, thal_2, thal_3 ... based on the levels it sees.

You must choose a reference and drop one dummy (or omit it from the model).

Also check missingness handling—tab, gen() does not always behave the way you want with missing categories, so always verify:

misstable sum thal thal_*
tab thal, missing

Method C — xi: prefix (automatic expansion; quick fix)

xi: expands i.var into dummy variables before the model runs.

Example:

xi: logistic group_gimalig age hb i.thal i.cirrhos

Creates internal variables like Ithal1, Ithal2 ...

✅ Useful when you want speed ❌ Less readable output and harder to control references ❌ For prediction modeling / TRIPOD reporting, manual dummies are usually preferred.

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3) Why this matters for mfp

The limitation

mfp does NOT accept factor-variable or time-series operators.So these will fail:

• mfp ... i.thal
• mfp ... c.age##c.age
• mfp ... i.site##c.age
• and often inside MI: mi estimate: mfp ... i.var

Typical error:

factor-variable and time-series operators not allowed

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4) How to use mfp correctly with dummy variables

The syntax pattern (what mfp expects)

mfp [options] : regression_cmd y xvarlist

Important points:

• mfp tests continuous variables for FP transformations (shape).
• mfp can also do variable selection (backward elimination).
• Variables inside parentheses ( ... ):
  • are tested jointly for inclusion,
  • and are not eligible for FP transformation (which is fine for categorical blocks).

That “joint testing” is very important for dummy sets.

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5) Three mfp patterns you said you use frequently

Pattern 1 — Default MFP (shape + selection)

mfp logistic group_gimalig ///
    age hb wbc plt mcv rdw ferritin si ///
    male pain wtloss abnbm gib ///
    thal_trait thal_disease ///
    cir_comp cir_decomp

What it does:

• Tests each continuous predictor for FP shape (linear vs FP1 vs FP2),
• and may remove predictors based on default selection rules.

Best used for:

• exploration
• initial model building

Not ideal if you need a “full” pre-specified model.

Pattern 2 — Full model (no variable removal): select(1)

mfp, select(1) : logistic group_gimalig ///
    age hb wbc plt mcv rdw ferritin si ///
    male pain wtloss abnbm gib ///
    thal_trait thal_disease ///
    cir_comp cir_decomp

What it does:

• Forces every predictor to remain (p-to-remove = 1.0),
• still chooses FP shapes for continuous variables.

Best used for:

• prediction modeling, where you want a full model but allow non-linearity

Pattern 3 — Force some predictors + allow selection in others

Example: force age and hb in the model, but allow selection of the rest:

mfp, select( ///
      wbc plt mcv rdw ferritin si male pain wtloss abnbm gib ///
      (thal_trait thal_disease) (cir_comp cir_decomp) : 0.05, ///
      age hb : 1 ///
    ) df(age hb:4) : ///
    logistic group_gimalig ///
      age hb wbc plt mcv rdw ferritin si ///
      male pain wtloss abnbm gib ///
      (thal_trait thal_disease) (cir_comp cir_decomp)

What it does (conceptually):

• Selection rule:
  • age hb:1 → forced in (never dropped)
  • others → can be dropped at 0.05
  • dummy blocks in parentheses are tested jointly (important!)
• Shape rule:
  • df(age hb:4) allows more flexible FP shapes for age and hb (up to 4 df).

Why parentheses matter here:

• Without parentheses, mfp might drop only thal_disease but keep thal_trait, which is usually undesirable clinically/statistically.
• With (thal_trait thal_disease) the whole “thalassemia factor” stays or goes together.

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6) Using xi: with mfp (quick compatibility mode)

If you don’t want to manually create dummies, this usually works:

xi: mfp, select(1) : logistic group_gimalig ///
    age hb wbc plt mcv rdw ferritin si ///
    male pain wtloss abnbm gib ///
    i.thal i.cirrhos

Why it works

• xi: expands i.thal i.cirrhos to dummy variables first.
• mfp then sees only plain variable names (no i. syntax), so it runs.

When I recommend xi: vs manual dummies

• Use xi:: quick exploratory work, teaching, or when dummies are numerous and you don’t care about clean names.
• Use manual dummies: final models, reporting, validation workflows, and reproducible manuscripts.

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7) MI error: “mfp on m=1” and how to fix it

You mentioned:

“an error occurred when mi estimate executed mfp on m=1”

This usually happens because:

• mi estimate is calling a command line that still contains i. or c. syntax (directly or indirectly),
• and mfp refuses it.

Fix 1 (best practice): create dummies as passive under MI

If thal or cirrhos are imputed, your dummies should be passive (derived from the imputed parent variable):

mi passive: gen thal_trait   = (thal==1) if !missing(thal)
mi passive: gen thal_disease = (thal==2) if !missing(thal)

mi passive: gen cir_comp   = (cirrhos==1) if !missing(cirrhos)
mi passive: gen cir_decomp = (cirrhos==2) if !missing(cirrhos)

Then:

mi estimate: mfp, select(1) : logistic group_gimalig ///
    age hb wbc plt mcv rdw ferritin si ///
    male pain wtloss abnbm gib ///
    thal_trait thal_disease cir_comp cir_decomp

Fix 2 (quick fix): xi: inside MI

Often works:

mi estimate: xi: mfp, select(1) : logistic group_gimalig ///
    age hb wbc plt mcv rdw ferritin si ///
    male pain wtloss abnbm gib ///
    i.thal i.cirrhos

If it runs, it’s fine for teaching and quick work. For publication, I still prefer passive dummies because it’s explicit and reproducible.

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8) Where mfpa fits (and why people use it)

What mfpa adds

• Supports factor variables (so i.var works).
• Adds linadj() and acd() options (advanced extensions).
• Uses different post-estimation helpers (xfracplot, xfracpred).

When to use mfpa

• You truly need factor-variable syntax and interactions in the model-building step.
• You accept the additional complexity and package dependencies.

Why some people avoid mfpa for performance validation

Not because validation is impossible—you can still:

• predict p, pr
• lroc
• pmcalplot

…but because:

• your model-building procedure is more complex to replicate inside bootstrap/MI pipelines, and
• fewer teams are familiar with the post-estimation ecosystem.

For many clinical papers, manual dummies + official mfp remains the cleanest “standard”.

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Practical checklist (what to do in real life)

If you use mfp and have categorical variables:

✅ Create dummies manually (best) OR use xi: (quick)

For multi-level categorical predictors:

✅ Use k−1 dummies

✅ Consider testing as a block: (d1 d2 ... dk-1)

Under MI:

✅ Make dummies passive

or ✅ use mi estimate: xi: ... as a shortcut

If you see:

factor-variable and time-series operators not allowed

• ✅ Remove i. / c. / ##
• ✅ Replace with dummies or xi: