Add age-stratified transition probabilities to Markov and microsimula…#14
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…tion models
Transition probability inputs (disease progression and institutionalization) now
accept either a scalar (backward compatible, constant across ages) or a named
numeric vector keyed by age (e.g., c("65"=0.15, "70"=0.20, "75"=0.25)).
Changes:
- Add f.age_lookup() helper that resolves scalar or age-keyed parameters to
per-cycle vectors using linear interpolation with constant extrapolation
- Markov model: replace rep() with f.age_lookup() for all disease transition
and institutionalization probabilities; use vectorized v.p.*_i throughout
the TP matrix construction
- Microsimulation: pre-compute age-specific vectors and rebuild the transition
matrix each cycle; use age-specific institutionalization probabilities
- Add commented usage examples in both ICER and microsim input lists
https://claude.ai/code/session_01AfopRzm7UqRiszRaQGsFi7
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…tion models
Transition probability inputs (disease progression and institutionalization) now accept either a scalar (backward compatible, constant across ages) or a named numeric vector keyed by age (e.g., c("65"=0.15, "70"=0.20, "75"=0.25)).
Changes:
https://claude.ai/code/session_01AfopRzm7UqRiszRaQGsFi7