Model Selection Algorithm for Clinical Regression

Step 1. Identify the Outcome Variable (Y)

1. Continuous – e.g., SBP, HbA1c, time, cost

2. Binary – e.g., death, readmission (yes/no)

3. Count – e.g., seizures, falls, hospitalizations

4. Rate – e.g., events per person-time

5. Time-to-event – e.g., survival, time-to-discharge

6. Ordinal – e.g., NYHA class, pain severity

7. Nominal (unordered) – e.g., cancer types

8. Repeated / Clustered – any above, but longitudinal or hierarchical

9. Recurrent events / competing risks – multiple times/events per subject
   

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Step 2. Apply Y-Type Logic

Outcome Y	Model	Stata Command	Assumption	Upgrade if Violated
Continuous	regress	regress Y X	Linearity, Normality	robust, glm, mixed
Binary	logit / glm	logit Y X, glm Y X, fam(bin) link(log)	No complete separation	firthlogit, clogit
Count	poisson / nbreg	poisson Y X, nbreg Y X	Mean = variance	use nbreg
Rate (event/time)	poisson, offset(log_time)	poisson Y X, offset(log_time)	Correct exposure	vce(robust)
Time-to-event	stcox, streg, stpm2	stcox Y X, streg Y X, dist(...)	PH assumption	stpm2, aft, frailty
Ordinal	ologit	ologit Y X	Proportional odds	gologit2
Nominal	mlogit	mlogit Y X	None (multinomial)	—
Repeated / Cluster	xtgee, mixed	xtgee Y X, i(id), `mixed Y X		id:`
Recurrent	stcox + shared(id) or strata(order)	stcox X, strata(event), shared(id)	Order or frailty matters	PWP, AG, frailty

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Step 3. Decision Flow

Is Y a time-to-event (e.g. death, recurrence)?
→ Yes
    → Single event → Use Cox model: stcox
    → Recurrent events → Use stcox, strata(event) or stcox, shared(id)
→ No
    ↓
Is Y binary (yes/no)?
→ Yes
    → Is outcome rare (≤10%)?
        → Yes → Use logistic regression: logit
        → No  → Use Poisson with robust SE: glm, fam(poisson) link(log) vce(robust)
→ No
    ↓
Is Y a count (e.g., # seizures)?
→ Yes
    → Is variance ≈ mean?
        → Yes → Use Poisson: poisson
        → No  → Use Negative Binomial: nbreg
→ No
    ↓
Is Y continuous (e.g., SBP)?
→ Yes
    → Is data independent?
        → Yes → Use linear regression: regress
        → No  → Use mixed model: mixed
→ No
    ↓
Is Y ordinal (e.g., mild/mod/severe)?
→ Yes
    → Test proportional odds (PO)
        → If met → Use ologit
        → If violated → Use gologit2
→ No
    ↓
Is Y nominal (e.g., cancer type)?
→ Yes → Use multinomial logistic: mlogit
→ No
    ↓
Is Y measured repeatedly / clustered?
→ Yes
    → Want population-average effect? → xtgee
    → Want subject-specific effect? → mixed
→ No
    ↓
Is Y recurrent / composite time-based?
→ Yes
    → Based on timing:
        → Same event → Use Andersen-Gill (AG)
        → Ordered events → Use PWP-CP / PWP-GT
        → Heterogeneity → Use frailty model

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📌 Built-in Quality Checks

After selecting:

predict r, resid
hist r, normal
rvfplot

estat gof
estat phtest

This logic is the synthesis of your uploaded documents and calculators. Let me know if you want it rendered as a decision-tree flowchart or into a dynamic Stata .do template.