Parametric Frailty Models in R

Information about the current plublic release on CRAN can be found here.
Information about the project development can be found here.
A published paper giving more details on the theory of frailty models and on the use of the parfm package can be found here [5].

Frailty models [1, 2] are survival models for clustered or overdispersed time-to-event data. They consist in proportional hazards Cox's models [3] with the addition of a random effect, accounting for different risk levels.

When the form of the baseline hazard is somehow known in advance, the parametric estimation approach can be used advantageously. The parfm package provides a wide range of parametric frailty models in R. The

• gamma,
• inverse Gaussian,
• positive stable and
• lognormal

• Weibull,
• exponential,
• Gompertz,
• lognormal,
• loglogistic.

Parameter estimation is done by maximising the log-likelihood, with right-censored and possibly left-truncated data.

Parametrisations

Baseline hazards

The exponential model is

h(t; λ) = λ,

with λ>0.

The Weibull model is

h(t; ρ, λ) = ρ λ t^ρ-1,

with ρ,λ>0.

The Gompertz model is

h(t; γ, λ) = λ e^γt,

with γ,λ>0.

The lognormal model is

h(t; μ, σ) = { φ([log t -μ]/σ)} / { σ t [1-Φ([log t -μ]/σ)]},

with μ∈R, σ>0 and φ(.) and Φ(.) the density and distribution functions of a standard Normal.

The loglogistic model is

h(t; α, κ) = {exp(α) κ t^κ-1 } / { 1 + exp(α) t^κ},

with α∈R and κ>0.

Frailty distributions

The gamma distribution is

f(u;, θ) = { u^1/θ-1 e^-u/θ }/{ Γ(1/θ) θ^1/θ },

with θ>0 and Γ(.) the Gamma function.

The inverse Gaussian distribution is

f(u; θ) = (2θπ)^-1/2 u^-3/2 exp{(u-1)² / 2uθ },

with θ>0.

The positive stable distribution is

f(u; ν) = -Σ_k=1...∞{-u^-(1-ν)k sin((1-ν)kπ) Γ((1-ν)k+1)/k!} / πu,

with ν∈(0, 1) and Γ(.) the Gamma function.

The lognormal distribution is

f(u; ν) = (2σ²π)^-1/2 u^-1 exp{-(log u)² / 2σ² },