Marginal Structural Model Calculator

Estimate stabilized weights for time varying treatment decisions. Review weighted outcomes, assumptions, diagnostics, and clear interpretation steps carefully.

Calculator Inputs

Choose mean difference or risk-based interpretation.
Observed mean or event risk for treated group.
Observed mean or event risk for control group.
Common practice trims extreme weights for stability.

Treatment Model Probabilities

Numerator uses reduced conditioning. Denominator uses full confounder history.

Censoring Survival Probabilities

Enter probabilities of remaining uncensored at each time point.

Example Data Table

ID A0 A1 A2 Num Prob Product Den Prob Product Censor Product Ratio Final Weight Outcome
101 1 1 0 0.1449 0.1650 1.1310 0.9938 18.6
102 0 1 1 0.1064 0.0893 1.0825 1.2902 21.1
103 1 0 0 0.1500 0.1357 1.0450 1.1548 16.4
104 0 0 1 0.1182 0.1012 1.0941 1.2780 13.9

This sample illustrates how observed treatment paths and modeled probabilities create stabilized weights for pseudo-population analysis.

Formula Used

1. Stabilized treatment weight

SWA = ∏t=0T P(At=at | reduced history) / P(At=at | full history)

2. Stabilized censoring weight

SWC = ∏t=0T P(Ct=0 | reduced history) / P(Ct=0 | full history)

3. Final stabilized weight

SW = SWA × SWC

4. Weighted marginal effect

Weighted Effect = (Weighted treated outcome) - (Weighted control outcome)

Marginal structural models target causal effects when treatment varies over time and standard regression may mis-handle time-dependent confounding affected by prior exposure.

How to Use This Calculator

  1. Choose whether your outcome is continuous or binary.
  2. Enter observed treated and control summary outcomes.
  3. Provide treated and control group sizes.
  4. Select the observed treatment at each time point.
  5. Enter numerator and denominator treatment probabilities.
  6. Optionally include censoring survival probabilities.
  7. Set a trim cap to limit extreme weights.
  8. Press Calculate Model to see the weighted effect above the form.

Important Notes

Frequently Asked Questions

1. What does this calculator estimate?

It estimates stabilized inverse probability weights and a weighted marginal effect for longitudinal treatment settings with time-varying confounding.

2. Why are numerator and denominator probabilities both needed?

The numerator stabilizes the weight, while the denominator reflects the full treatment or censoring model conditioned on observed history.

3. When should censoring weights be included?

Include them when loss to follow-up or dropout may depend on covariates or prior treatment and could bias observed outcomes.

4. What does a very large weight mean?

Very large weights often signal sparse data, near-positivity violations, or poorly fitted models. They deserve closer diagnostic review.

5. Why is trimming offered?

Trimming caps extreme weights to reduce instability, overly influential records, and inflated variance in weighted causal estimates.

6. Can this handle binary and continuous outcomes?

Yes. Continuous mode emphasizes weighted mean differences. Binary mode also reports weighted risk and odds comparisons when possible.

7. Does this replace formal modeling software?

No. It is useful for education, validation, and quick checks, but complete analyses still need robust model fitting and diagnostics.

8. Which assumptions matter most?

Exchangeability, positivity, consistency, and correct specification of treatment and censoring models are central to valid causal interpretation.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.