Veteran’s data

We first illustrate the estimation on non-linear smooth effects using the veteran data from the survival package:

data("veteran", package="survival") # load veteran package
## Warning in data("veteran", package = "survival"): data set 'veteran' not found
veteran <- veteran %>%
  mutate(
    trt   = 1+(trt == 2),
    prior = 1*(prior==10)) %>%
  filter(time < 400)
head(veteran)
##   trt celltype time status karno diagtime age prior
## 1   1 squamous   72      1    60        7  69     0
## 3   1 squamous  228      1    60        3  38     0
## 4   1 squamous  126      1    60        9  63     1
## 5   1 squamous  118      1    70       11  65     1
## 6   1 squamous   10      1    20        5  49     0
## 7   1 squamous   82      1    40       10  69     1

There are two continuous covariates in the data set that could potentially exhibit non-linear effects, age and karno. We fit a Cox-PH model and a PAM of the form

\[ \lambda(t|x) = \lambda_0(t)\exp(f(x_{karno})) \]

and

\[ \lambda(t|x) = \exp(f_0(t_j) + f(x_{karno})) \] respectively:

## Cox-PH
cph <- coxph(Surv(time, status) ~ pspline(karno, df=0), data=veteran)
## PAM
ped <- veteran %>% as_ped(Surv(time, status) ~ ., id="id")
pam <- gam(ped_status ~ s(tend) + s(karno, bs="ps"), data=ped, family=poisson(),
  offset=offset)

The Figure below visualizes the smooth estimates from both models:

Expand here to see R-Code

# crate data set with varying karno values (from min to max in 100 steps)
# and add term contribution of karno from PAM and Cox-PH models
karno_df <- ped %>% make_newdata(karno = seq_range(karno, n = 100)) %>%
  add_term(pam, term="karno") %>%
  mutate(
    cox = predict(cph, newdata=., type="terms")[, "pspline(karno, df = 0)"] -
      predict(cph, newdata=data.frame(karno=mean(veteran$karno))))
gg_karno <- ggplot(karno_df, aes(x=karno, ymin=ci_lower, ymax=ci_upper)) +
  geom_line(aes(y=fit, col="PAM")) +
  geom_ribbon(alpha=0.2) +
  geom_line(aes(y=cox, col="Cox PH"))+
  scale_colour_manual(name="Method",values=c("Cox PH"=Set1[1],"PAM"="black")) +
  xlab(expression(x[plain(karno)])) + ylab(expression(hat(f)(x[plain(karno)])))
# gg_karno

Both methods are in good agreement. The higher the Karnofsky-Score the lower the expected log-hazard. For further evaluation of the Karnofsky-Score effect using time-varying terms of the form \(f(t)\cdot x_{karno}\) and \(f(x_{karno}, t)\) see the time-varying effects vignette.

MGUS data: age example

In the following we consider another example using using data presented in the respective vignette in the survival package (see vignette("splines", package = "survival")).

Cox PH workflow

The example presented in the vignette goes as follows, using the standard base R workflow and termplot for visualization:

data("mgus", package = "survival")
## Warning in data("mgus", package = "survival"): data set 'mgus' not found
mfit <- coxph(Surv(futime, death) ~ sex + pspline(age, df = 4), data = mgus)
mfit
## Call:
## coxph(formula = Surv(futime, death) ~ sex + pspline(age, df = 4), 
##     data = mgus)
## 
##                               coef se(coef)      se2    Chisq   DF      p
## sexmale                    0.22784  0.13883  0.13820  2.69335 1.00   0.10
## pspline(age, df = 4), lin  0.06682  0.00703  0.00703 90.22974 1.00 <2e-16
## pspline(age, df = 4), non                             3.44005 3.05   0.34
## 
## Iterations: 5 outer, 16 Newton-Raphson
##      Theta= 0.851 
## Degrees of freedom for terms= 1.0 4.1 
## Likelihood ratio test=108  on 5.04 df, p=<2e-16
## n= 241, number of events= 225
termplot(mfit, term = 2, se = TRUE, col.term = 1, col.se = 1)

PAM workflow

The equivalent fit using PAMs requires the additional step of transforming the data into a suitable format. We then use mgcv::gam to fit the model:

mgus.ped <- mgus %>% as_ped(Surv(futime, death)~sex + age, id = "id")
head(mgus.ped)
##   id tstart tend interval   offset ped_status    sex age
## 1  1      0    6    (0,6] 1.791759          0 female  78
## 2  1      6    7    (6,7] 0.000000          0 female  78
## 3  1      7   31   (7,31] 3.178054          0 female  78
## 4  1     31   32  (31,32] 0.000000          0 female  78
## 5  1     32   39  (32,39] 1.945910          0 female  78
## 6  1     39   60  (39,60] 3.044522          0 female  78
pamfit <- gam(ped_status ~ s(tend) + sex + s(age, bs = "ps"), data = mgus.ped,
  method = "REML", family = poisson(), offset = offset)
summary(pamfit)
## 
## Family: poisson 
## Link function: log 
## 
## Formula:
## ped_status ~ s(tend) + sex + s(age, bs = "ps")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -9.1597     0.1194 -76.685   <2e-16 ***
## sexmale       0.2461     0.1362   1.807   0.0708 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##           edf Ref.df Chi.sq  p-value    
## s(tend) 1.001  1.002  67.11 2.68e-16 ***
## s(age)  1.001  1.002  90.01  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.000299   Deviance explained = 5.19%
## -REML = 1350.2  Scale est. = 1         n = 28326

For visualization of the smooth effects we can use the default mgcv::plot.gam function:

layout(matrix(1:2, nrow = 1))
termplot(mfit, term = 2, se = TRUE, col.term = 1, col.se = 1)
plot(pamfit, select = 2, se = TRUE, ylim = c(-4, 3.5))

In this example the PAM approach estimates a linear effect of age, which is consistent with the estimation using coxph, as there is only weak non-linearity.

MGUS data: Hemoglobin example

Another example from the same vignette shows that estimated effects are very similar if the effect of the covariate is in fact strongly non-linear:

fit <- coxph(Surv(futime, death) ~ age + pspline(hgb, 4), mgus2)
mgus2.ped <- mgus2 %>% as_ped(Surv(futime, death)~age + hgb, id = "id")
pamfit2 <- gam(ped_status~s(tend) + age + s(hgb), data = mgus2.ped,
    family = poisson(), offset = offset)
layout(matrix(1:2, nrow = 1))
termplot(fit, term = 2, se = TRUE, col.term = 1, col.se = 1)
plot(pamfit2, select = 2, se = TRUE, ylim = c(-0.5, 2))

Monotonicity constraints

The vignette in the survival package further discusses enforcing monotonicity constraints on the effect of hgb. These can be achieved here more easily using the functionality provided in the scam package (see also Pya and Wood (2015)). The usage is exactly the same as before, replacing the call to gam with a call to scam and specifying bs = "mpd". Note that the fit using constraints takes considerably longer compared to the unconstrained fit.

library(scam)
mpam <- scam(ped_status ~ s(tend) + age + s(hgb, bs = "mpd"), data = mgus2.ped,
  family = poisson(), offset = offset)
layout(matrix(1:2, nrow = 1))
plot(pamfit2, select = 2, se = TRUE, ylim = c(-0.75, 2), main="Unconstrained fit")
# 1.72 = intercept difference between pamfit2 and mpam
const <- abs(coef(pamfit2)[1] - coef(mpam)[1])
plot(mpam, select = 2, se = TRUE, ylim = c(-.75, 2), main="Fit with monotonicity constraint")

References

Pya, Natalya, and Simon N Wood. 2015. “Shape Constrained Additive Models.” Statistics and Computing 25 (3): 543–59.