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seerSurv: Cancer Progression Transition Probabilities from Aggregate Survival Data

Source: R/seerTP-package.R
seerSurv-package.Rd

The seerSurv package provides a transparent, reproducible framework for estimating monthly transition probabilities used in state-transition health economic models that include a local/regional recurrence (LR) state.

Survival data for LR and distant-recurrence (DR) populations—typically extracted from the SEER-17 registry—are extrapolated using a weighted blend of parametric survival models. Age- and sex-specific background mortality from US-CDC life-tables is layered on top to produce net (all-cause) survival curves. The area differential between the LR and DR curves yields the mean LR sojourn time, from which \(P(\text{LR})\) is derived. A convolution optimisation then recovers \(P(\text{DR})\).

Main workflow

  1. Prepare pseudo-individual patient data from aggregate survival proportions with prep_ipd.

  2. Fit a panel of parametric models with fit_models.

  3. Select and weight the top-\(k\) models by AIC or BIC using extract_ic and compute_weights.

  4. Blend the weighted survival curves with blend_survival.

  5. Adjust for background mortality with make_background_surv.

  6. Run the complete analysis pipeline for one or many tumour types with run_tumour_analysis.

Bundled data

lifetable_seer

US-CDC annual death-rate life-table (ages 0–100) with separate columns for males and females, aligned to the SEER-17 study period.

tumour_data_seer

Reference 5-year aggregate survival proportions (years 0–5) for LR and DR populations across 11 cancer types derived from SEER-17, together with sample sizes, sex proportions, and mean ages.

References

Mansoori S, Pandey S, Rani R, Singh B, Kurt M (2025). "Deriving Cancer Progression Rates After Local/Regional Recurrence Using Aggregate Survival Data: An R Package for Health Economic Evaluations." Value in Health (submitted).

Surveillance, Epidemiology, and End Results (SEER) Program (https://seer.cancer.gov/) SEER*Stat Database.

See also

Useful links:

Author

Sameer Mansoori, Shubhram Pandey, Rashi Rani, Barinder Singh, Murat Kurt

Maintainer: Shubhram Pandey shubhram.pandey@heorlytics.com