iivw

Version 1.0.2 | 2026-04-26

iivw corrects bias from informative visit timing in irregular longitudinal data. In clinic-based studies, sicker patients often visit more frequently, so they contribute more rows to the dataset and bias naive analyses. This package re-weights each observation so the analysis behaves as though patients were observed on a common schedule.

Three weighting strategies are available:

• IIW (inverse intensity weighting) — corrects for outcome-dependent visit frequency
• IPTW (inverse probability of treatment weighting) — corrects for confounding by treatment indication
• FIPTIW (IIW × IPTW) — corrects for both simultaneously

Weighted outcome models are fit via GEE-style estimation (GLM with clustered robust SEs) or mixed effects.

Requirements

• Stata 16 or later
• Stata 17 or later for iivw_fit, model(mixed)

Installation

capture ado uninstall iivw
net install iivw, from("https://raw.githubusercontent.com/tpcopeland/Stata-Tools/main/iivw") replace

Commands

Command	Description
iivw	Package overview and available commands
iivw_weight	Compute IIW, IPTW, or FIPTIW weights
iivw_fit	Fit a weighted outcome model after iivw_weight

When Do I Need This?

You likely need this package if:

1. Your data comes from a clinical registry, electronic health records, or any setting where visit times are determined by clinical need rather than a fixed protocol.
2. You have longitudinal data with unequal numbers of visits per subject, and sicker (or healthier) patients are observed more often.
3. You want to estimate a treatment effect, disease trajectory, or covariate association and need to remove bias from informative visit timing.

You probably do not need this if visits follow a fixed protocol (e.g., randomized trial with scheduled assessments) or if the main concern is dropout rather than differential visit frequency.

How It Works

1. Compute weights with iivw_weight. You always specify id() and time(). The command fits an Andersen-Gill recurrent-event Cox model to estimate each subject's visit intensity, then creates a weight variable in the dataset.
2. Choose the weighting strategy that matches the scientific problem (see table below).
3. Inspect weights with summarize _iivw_weight, detail. Look for extreme values. If the weight distribution has heavy tails, re-run with truncate(1 99) to cap extreme weights.
4. Fit the outcome model with iivw_fit. It reads the weight variable and panel structure from the dataset automatically.

Choosing a Weight Type

Weight type	When to use	Key iivw_weight options
iivw	Visit timing is informative, but treatment weighting is not needed	id() time() visit_cov()
iptw	Treatment confounding only (visits are protocol-driven)	treat() treat_cov() wtype(iptw)
fiptiw	Both informative visit timing and treatment confounding	id() time() visit_cov() treat() treat_cov()

By default, iivw_weight auto-detects the type: specifying treat() triggers FIPTIW; omitting it triggers IIW. Override with wtype().

Worked Examples

These examples use a self-contained synthetic panel because Stata does not ship a built-in irregular-visit dataset that exercises the full workflow.

1. Create example longitudinal data

This creates 80 subjects with 4 visits each, a continuous disability outcome (EDSS), a binary treatment, and a binary event (relapse) that also predicts visit frequency.

clear
set seed 20260417
set obs 320
gen long id = ceil(_n/4)
bysort id: gen byte visit = _n
gen double days = (visit - 1) * 90 + runiform() * 20
replace days = 0 if visit == 1
gen double edss_bl = 2 + 3 * runiform()
bysort id: replace edss_bl = edss_bl[1]
gen double age = 35 + 15 * runiform()
bysort id: replace age = age[1]
gen byte sex = runiform() > 0.5
bysort id: replace sex = sex[1]
gen byte treated = (runiform() < invlogit(-0.8 + 0.5 * edss_bl))
bysort id: replace treated = treated[1]
gen double edss = edss_bl + 0.012 * days - 0.7 * treated + rnormal(0, 0.45)
gen byte relapse = (runiform() < invlogit(-2 + 0.4 * edss))
gen byte treatment = cond(treated == 0, 0, cond(edss_bl < 3.5, 1, 2))
label define arm 0 "Placebo" 1 "Low dose" 2 "High dose"
label values treatment arm

2. IIW only: correct the visit process

When the main concern is that patients with worse disease are seen more often, but treatment assignment is either randomized or not being analyzed:

iivw_weight, id(id) time(days) visit_cov(edss relapse) nolog
summarize _iivw_weight, detail
iivw_fit edss treated edss_bl, model(gee) timespec(linear)

After computing weights, always inspect the distribution before fitting the outcome model. If the weight tails are extreme (e.g., max > 10), re-run iivw_weight with truncate(1 99).

3. FIPTIW: correct visit timing and treatment confounding together

Add treat() and treat_cov() when treatment assignment is also non-random:

iivw_weight, id(id) time(days) ///
    visit_cov(edss relapse) ///
    treat(treated) treat_cov(age sex edss_bl) ///
    truncate(1 99) replace nolog

iivw_fit edss treated age sex edss_bl, model(gee) timespec(quadratic)

4. Add time-varying effects in the weighted outcome model

Once weights are in place, iivw_fit can add flexible time trends and time × covariate interactions:

iivw_fit edss treated age sex edss_bl, ///
    model(gee) timespec(ns(3)) interaction(treated) replace

Use timespec(linear), timespec(quadratic), timespec(cubic), timespec(ns(#)), or timespec(none) depending on how flexible the time trend should be. Start with linear, then compare to ns(3) to check sensitivity.

5. Use categorical predictors in the outcome model

categorical() expands a multi-level variable into labeled dummy variables. It affects the outcome model only — it does not create multi-arm IPTW.

iivw_weight, id(id) time(days) visit_cov(edss relapse) replace nolog
iivw_fit edss treatment edss_bl, ///
    categorical(treatment) timespec(ns(3)) interaction(treatment) replace

6. Bootstrap standard errors

Bootstrap replicates apply to the outcome model fit with fixed weights. The weights are not re-estimated inside each bootstrap draw:

iivw_fit edss treated edss_bl, bootstrap(500) nolog replace

7. Export results to Excel

Use the collect option with regtab (from the tabtools package) to build publication-ready tables:

collect clear
iivw_fit edss treated edss_bl, model(gee) nolog replace collect
regtab, xlsx(iivw_results.xlsx) sheet(Results) title(IIW Analysis) stats(n)

Weight Diagnostics

After running iivw_weight, check these before fitting the outcome model:

Diagnostic	What to look for	Action if concerning
summarize _iivw_weight, detail	Max > 10, max/min ratio > 100	Add truncate(1 99)
Effective sample size (reported automatically)	ESS much less than N	Simplify the visit model or truncate
Weight mean (reported automatically)	Mean far from 1.0	Check model specification
Compare with/without truncation	Treatment effect changes substantially	Result may be driven by a few extreme weights
summarize _iivw_tw, detail (FIPTIW only)	Extreme treatment weights	Positivity violations — check covariate overlap

Interpreting Results

• Coefficients (default GEE with gaussian family) are the change in the outcome per one-unit change in the predictor, averaged over the population.
• Treatment effect: The coefficient on the treatment variable estimates the causal treatment effect, assuming the visit model (and propensity model, for FIPTIW) is correctly specified and there is no unmeasured confounding.
• Standard errors are sandwich (robust) SEs clustered at the subject level. They do not account for weight estimation uncertainty.
• Post-estimation: All standard Stata post-estimation commands work after iivw_fit (predict, lincom, test, margins).

Practical Notes

• treat() must be binary (0/1) and time-invariant within each subject. For time-varying treatments, consider marginal structural models instead.
• iivw_fit automatically reads the weight variable, panel ID, and time variable stored by iivw_weight.
• categorical() is for the outcome model only. It does not define IPTW treatment levels.
• bootstrap() reflects outcome-model uncertainty only because the weights are treated as fixed.
• efron in iivw_weight uses the Efron tie-handling method in the Cox model (matches R's coxph() default; Breslow remains the Stata default).

Validation

The package ships with functional, validation, and cross-validation QA under qa/, including comparisons against independent R workflows for both IIW-style weighting and the FIPTIW setting.

Demo

The full FIPTIW workflow (weighting, weight diagnostics, outcome model) is rendered as a self-contained HTML document:

View rendered output (console_output.html)

The demo also produces a multi-model comparison table (demo/iivw_results.xlsx) showing IIW, FIPTIW, FIPTIW with treatment-time interaction, and FIPTIW with categorical treatment side by side.

Regenerate with:

do iivw/demo/demo_iivw.do

References

• Buzkova P, Lumley T. Longitudinal data analysis for generalized linear models with follow-up dependent on outcome-related variables. Canadian Journal of Statistics. 2007;35(4):485-500.
• Lin H, Scharfstein DO, Rosenheck RA. Analysis of longitudinal data with irregular, outcome-dependent follow-up. Journal of the Royal Statistical Society Series B. 2004;66(3):791-813.
• Pullenayegum EM. Multiple outputation for the analysis of longitudinal data subject to irregular observation. Statistics in Medicine. 2016;35(11):1800-1818.
• Tompkins G, Dubin JA, Wallace M. On flexible inverse probability of treatment and intensity weighting. Statistical Methods in Medical Research. 2025.

Changelog

v1.0.2 (2026-04-26)

• Added efron option to iivw_weight for Efron tie-handling in the Cox model (matches R's coxph default; Breslow remains the Stata default)
• Added collect option to iivw_fit for Stata's collect framework integration
• Improved stabcov() documentation with guidance on numerator model specification in FIPTIW settings
• Added Remarks in iivw_fit.sthlp for choosing between GEE and mixed models, and for timespec selection
• Expanded entry() documentation for late-entry/left-truncation designs
• Fixed iivw.sthlp Example 1 to match README (was showing wrong predictors)
• Improved error message for time-varying treatment (suggests MSMs as alternative)

Author

Timothy P Copeland, Karolinska Institutet