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### Transcript of Parametric regression models - Universitetet i oslo sex 0.054402522 0.0305622598 1.780056...

• Parametric regression models

STK4080 H16

1. Likelihood for censored data

2. Parametric regression models

3. Poisson regression models

4. Accelerated failure time models

5. Martingale formulation of parametric survival models

Parametric regression models – p. 1/40

• Parametric models without covariates

Let Ti have hazardα(t|θ), cumulative hazardA(t|θ), density f(t|θ) and survival functionS(t|θ).

We observe right censored data(T̃i, Di) whereT̃i = min(Ci, Ti)

andDi = I(T̃i = Ti) andCi a censoring time independent ofXi.

Such right censored data have likelihoodL(θ) ∝ ∏n

i=1 Li(θ)

where the likelihood contributions equal

Li(θ) = f(T̃i|θ)DiS(T̃i|θ)1−Di = α(T̃i|θ)Di exp(−A(T̃i|θ)).

Under standard assumptions the MLEθ̂ that maximizesL(θ) is

approximately

θ̂ ∼ N(θ, I(θ̂)−1)

whereI(θ) = −∂2 log(L(θ)) ∂θ2

is the information matrix. Parametric regression models – p. 2/40

If the true survival timesTi ∼ exp(ν) and we have right censored data(T̃i, Di) with τ as the maximal observation time,we get a

likelihood,

L =

n∏

i=1

νDi exp(−T̃iν) = νN•(τ) exp(−νR(τ))

whereN•(τ) = ∑n

i=1Di the no. observed events (occurrence)

andR(τ) = ∑n

i=1 T̃i the total exposure time.

It then follows that the MLE ofν equals

ν̂ = N•(τ)

R(τ) =

Occurrence Exposure

and the standard error ofν̂ is 1/ √ I(ν̂) = ν̂/

√ N•(τ).

Parametric regression models – p. 3/40

• Piecewise constant hazardα(t) = θkIk(t)

whereIk(t) = 1 on intervalstk−1 < t ≤ tk and zero otherw. and 0 = t0 < t1 < . . . < tK = τ .

It can then be shown that withOk is the number of events andRk the total observational time (exposure) in intervalk the MLE for

θk is given

θ̂k = Ok Rk

.

Furthermore the information matrix becomes diagonal and the

large sample distribution of thêθk’s is independent normal

distributions with standard errorŝθk/ √ Ok

Also, the standard error forlog(θ̂) becomes1/ √ Ok and

θ̂ exp(±1.96/ √ Ok) is generally a preferable confidence interval.

Parametric regression models – p. 4/40

• Parametric regression models, in general

Will assume that the distribution ofTi depends on a linear

predictorβ′xi and in addition on parameterθ.

The distribution may always be specified via the hazard, thus

Ti ∼ α(t|β′xi, θ)

i.e. the hazard for individuali. With cumulative hazard

A(t|β′xi, θ) we may express the likelihood contribution from individual i by

Li(β, θ) ∝ α(T̃i|β′xi, θ)Di exp(−A(T̃i|β′xi, θ))

where we assume thatTi andCi are independent givenxi.

Parametric regression models – p. 5/40

• Classes of regression models

• Proportional hazards:α(t|β′x, θ) = exp(β′x)α0(t|θ) • Additive hazards:α(t|β′x, θ) = β′x+ α0(t|θ) • Accelerated failure time: α(t|β′x, θ) = exp(β′x)α0(exp(β′x)t|θ)

• Translation modelsα(t|β′x, θ) = α0(t+ β′x|θ) • and many more!

Parametric regression models – p. 6/40

• Proportional hazards model

1. Semi-parametric method is the most common

(Cox-regression)

2. Poisson-regression is a numerically simpler variation

3. Via accelerated failure time models if the baseline is

Weibull! (or exponential)

4. In general by likelihood-optimization.

Have discussed 1. thoroughly, will consider 2. and 3.

Parametric regression models – p. 7/40

• Additive hazard modelsα(t|β′x, θ) = β′x+ α0(t|θ)

1. Semi-parametric, Lin & Ying-model,

2. Special case of Aalen’s additive model,

α(t|β′x, θ) = β(t)′x+ β0(t|θ) 3. Parametric models are possible, but not programmed?

4. Poisson-regression is possible (Breslow, 1987) with

piecewise constant baseline

Have discussed 2. and mentioned 1. Will here briefly consider4.

Parametric regression models – p. 8/40

• Poisson-regression

AssumeYi ∼ Po(ni exp(ψ + β′xi). This generates likelihood

L = ∏n

i=1

[ n Yi i exp(Yi(ψ+β

′xi))

Yi! exp(−ni exp(ψ + β′xi))

]

∝ ∏n

i=1 [exp(Yi(ψ + β ′xi)) exp(−ni exp(ψ + β′xi))]

which may be maximized with a program for Poisson-regression,

in Ras

• Generalized linear model:glm

• with Poisson-familyfamily=poisson

• Need "offset " for log(ni) (or alternatively with weighting)

• May also fit other link functions than default log-link f.ex. additive model E[Yi] = ni(ψ + β′xi)

Parametric regression models – p. 9/40

Ti ∼ α(t|xi) = exp(ψ + β′xi)

and cumulative hazardA(t|xi) = t exp(ψ + β′xi).

With observations̃Ti = right censored lifetimes andDi =

indicator for events gives likelihood

L = ∏n

i=1 α(T̃i|xi)Di exp(−A(T̃i|xi)) =

∏n i=1 exp(Di(ψ + β

′xi)) exp(−T̃i exp(ψ + β′xi))

which is proportional to a Poisson-likelihood under assumption

Di ∼ Po(T̃i exp(ψ + β′xi)).

May thus fit this model by Poisson-regression! !!

Parametric regression models – p. 10/40

• Example: Melanoma data

family=poisson))

Deviance Residuals:

Min 1Q Median 3Q Max

-1.757198 -0.7833086 -0.4186261 0.5735247 2.35767

Coefficients:

Value Std. Error z value

(Intercept) -3.27166724 0.793644496 -4.122333

ulcer -0.96045856 0.324053150 -2.963892

logthick 0.51104055 0.177549752 2.878295

age 0.01301791 0.007918443 1.643999

sex 0.34101496 0.270580076 1.260311

(Dispersion Parameter for Poisson family taken to be 1 )

Null Deviance: 232.0768 on 204 degrees of freedom

Residual Deviance: 188.029 on 200 degrees of freedom

Number of Fisher Scoring Iterations: 5

Parametric regression models – p. 11/40

• Alternative to offset: Weighting

AssumeYi ∼ Po(niµi) so that E[Yi/ni] = µi and

Var

[ Yi ni

] = niµi n2i

= µi ni

May alternatively fit the model by

• ResponsesYi ni

• Weightswi = ni (in R: glm )

• Link function log(µi) = ψ + β′xi

glm -routine inRdo not require integer responses!

It will also work for "quasi-survival-responses"Di/T̃i with weightsT̃i.

Parametric regression models – p. 12/40

• Example: Melanoma data, weighting (compare pg. 19)

Deviance Residuals:

Min 1Q Median 3Q Max

-1.757189 -0.7833315 -0.4186496 0.5735243 2.357629

Coefficients:

Value Std. Error t value

(Intercept) -3.27167476 0.792273252 -4.129478

ulcer -0.96041516 0.323111908 -2.972392

logthick 0.51101978 0.177128703 2.885020

age 0.01301783 0.007912787 1.645164

sex 0.34101511 0.270233537 1.261927

(Dispersion Parameter for Poisson family taken to be 1 )

Null Deviance: 232.0768 on 204 degrees of freedom

Residual Deviance: 188.029 on 200 degrees of freedom

Number of Fisher Scoring Iterations: 5

Parametric regression models – p. 13/40

Ti ∼ α(t|xi) = ψ + β′xi With censored data(T̃i, Di) we get likelihood corresponding to

• Di ∼ Po(T̃iµi) • whereµi = ψ + β′xi • and such that E[Di/T̃i] = µi and Var

[ Di T̃i

] = T̃iµi

T̃ 2i = µi

T̃i

It is thus possible to fit this survival model byglm , weighting

However: With identity linkRneeds modification of responses

Di/T̃i = 0, need to make new responseD′i = Di + �i for small

�i, f.ex. 0.0001.

This may still be unstable, in the example on next page I needed to usegrthick instead oflogthick and omitage .

Parametric regression models – p. 14/40

• Example: Melanoma data, additive exponential model

Deviance Residuals:

Min 1Q Median 3Q Max

-1.605446 -0.8411342 -0.3830547 0.6907776 2.426813

Coefficients:

Value Std. Error t value

(Intercept) 0.07537012 0.03424506 2.200905

ulcer -0.04302607 0.01545833 -2.783358

factor(grthick)2 0.03898369 0.01583326 2.462139

factor(grthick)3 0.03337742 0.02378281 1.403426

sex 0.01950260 0.01178936 1.654254

(Dispersion Parameter for Poisson family taken to be 1 )

Null Deviance: 232.0459 on 204 degrees of freedom

Residual Deviance: 192.2642 on 200 degrees of freedom

Number of Fisher Scoring Iterations: 5

Parametric regression models – p. 15/40

• While we are at it: Exponential model, square root link