In this vignette, we describe the `plot`

method for
objects of class `singleEventCB`

which is obtained from
running the `fitSmoothHazard`

function. There are currently
two types of plots: hazard functions and hazard ratios. We describe each
one in detail below. Note that the `plot`

method has only
been properly tested for `family="glm"`

.

The hazard function plots require the `visreg`

package.

To illustrate hazard function plots, we will use the breast cancer
dataset which contains the observations of 686 women taken from the `TH.data`

package. This dataset is also available from the `casebase`

package. In the following, we will show different hazard functions for
different combinations of continuous, binary variables as well as their
interactions.

We first fit a main effects only model with a spline on
`log(time)`

and hormonal therapy as main effects.

```
mod_cb <- fitSmoothHazard(cens ~ ns(log(time), df = 3) + hormon,
data = brcancer,
time = "time")
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(mod_cb)
#> Fitting smooth hazards with case-base sampling
#>
#> Sample size: 686
#> Number of events: 299
#> Number of base moments: 29900
#> ----
#>
#> Call:
#> fitSmoothHazard(formula = cens ~ ns(log(time), df = 3) + hormon,
#> data = brcancer, time = "time")
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -66.6516 14.1663 -4.705 2.54e-06 ***
#> ns(log(time), df = 3)1 39.3469 9.2854 4.238 2.26e-05 ***
#> ns(log(time), df = 3)2 113.8840 27.6210 4.123 3.74e-05 ***
#> ns(log(time), df = 3)3 23.4631 5.6315 4.166 3.09e-05 ***
#> hormon -0.3629 0.1256 -2.890 0.00386 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 3354.9 on 30198 degrees of freedom
#> Residual deviance: 3278.0 on 30194 degrees of freedom
#> AIC: 3288
#>
#> Number of Fisher Scoring iterations: 10
```

All arguments needed for the hazard function plots are supplied
through the `hazard.params`

argument. This is a named list of
arguments which will override the defaults passed to
`visreg::visreg()`

. The default arguments are
`list(fit = x, trans = exp, plot = TRUE, rug = FALSE, alpha = 1, partial = FALSE, overlay = TRUE)`

.
For example, if you want a 95% confidence band, specify
`hazard.params = list(alpha = 0.05)`

. For a complete list of
options, please see the `visreg`

vignettes.

We first plot the hazard as a function of time, for
`hormon = 0`

and `hormon = 1`

. This is achieved by
specifying the `xvar`

argument, as well as the
`cond`

argument. The `cond`

argument must be
provided as a named list. Each element of that list specifies the value
for one of the terms in the model; any elements left unspecified are
filled in with the median/most common category. Note that even though we
fit the `log(time)`

, we must specify `time`

in the
`xvar`

argument.

Alternatively, we can plot the hazard functions on the same plot.
This is accomplished with the `by`

argument:

Note that if we want to extract the data used to construct the plot,
e.g. to create our own, we simply assign the call to `plot`

to an object (we may optionally set `plot=FALSE`

in the
`hazard.params`

argument as to not print any plots):

```
plot_results <- plot(mod_cb,
hazard.params = list(xvar = "time",
by = "hormon",
alpha = 0.10,
ylab = "Hazard",
plot = FALSE))
```

```
head(plot_results$fit)
#> time hormon offset cens visregFit visregLwr visregUpr
#> 1 0.05341138 0 0 0 1.131340e-29 8.587843e-40 1.490397e-19
#> 2 25.93592327 0 0 0 2.694254e-07 1.659938e-08 4.373059e-06
#> 3 51.81843516 0 0 0 6.813619e-06 1.435501e-06 3.234090e-05
#> 4 77.70094705 0 0 0 3.122054e-05 1.152440e-05 8.457905e-05
#> 5 103.58345894 0 0 0 7.708507e-05 3.914608e-05 1.517932e-04
#> 6 129.46597083 0 0 0 1.399014e-04 8.654666e-05 2.261485e-04
```

The function is flexible because you may leverage
`ggplot2`

just by specifying `gg = TRUE`

, the plot
will return a `ggplot`

object:

```
gg_object <- plot(mod_cb,
hazard.params = list(xvar = "time",
by = "hormon",
alpha = 0.20, # 80% CI
ylab = "Hazard",
gg = TRUE))
```

Now we can use it downstream for any plot while leveraging the entire
`ggplot2`

ecosystem of packages and functions:

Next, we fit an interaction model with a time-varying covariate, i.e. to test the hypothesis that the effect of hormonal therapy on the hazard varies with time.

```
mod_cb_tvc <- fitSmoothHazard(cens ~ hormon * ns(log(time), df = 3),
data = brcancer,
time = "time")
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(mod_cb_tvc)
#> Fitting smooth hazards with case-base sampling
#>
#> Sample size: 686
#> Number of events: 299
#> Number of base moments: 29900
#> ----
#>
#> Call:
#> fitSmoothHazard(formula = cens ~ hormon * ns(log(time), df = 3),
#> data = brcancer, time = "time")
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -83.584 22.085 -3.785 0.000154 ***
#> hormon -31.467 49.508 -0.636 0.525037
#> ns(log(time), df = 3)1 50.860 14.550 3.496 0.000473 ***
#> ns(log(time), df = 3)2 146.159 42.961 3.402 0.000669 ***
#> ns(log(time), df = 3)3 30.266 8.819 3.432 0.000599 ***
#> hormon:ns(log(time), df = 3)1 20.911 32.826 0.637 0.524115
#> hormon:ns(log(time), df = 3)2 59.743 95.878 0.623 0.533210
#> hormon:ns(log(time), df = 3)3 12.946 19.877 0.651 0.514861
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 3354.9 on 30198 degrees of freedom
#> Residual deviance: 3273.0 on 30191 degrees of freedom
#> AIC: 3289
#>
#> Number of Fisher Scoring iterations: 11
```

Now we can easily plot the hazard function over time for each
`hormon`

group:

Now we fit a model with an interaction between a continuous variable,
estrogen receptor (in `fmol`

), and time.

```
mod_cb_tvc <- fitSmoothHazard(cens ~ estrec * ns(log(time), df = 3),
data = brcancer,
time = "time")
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(mod_cb_tvc)
#> Fitting smooth hazards with case-base sampling
#>
#> Sample size: 686
#> Number of events: 299
#> Number of base moments: 29900
#> ----
#>
#> Call:
#> fitSmoothHazard(formula = cens ~ estrec * ns(log(time), df = 3),
#> data = brcancer, time = "time")
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -70.9573 18.8668 -3.761 0.000169 ***
#> estrec -0.6200 0.3444 -1.800 0.071854 .
#> ns(log(time), df = 3)1 41.5050 12.3820 3.352 0.000802 ***
#> ns(log(time), df = 3)2 123.2372 36.7392 3.354 0.000795 ***
#> ns(log(time), df = 3)3 24.4522 7.5337 3.246 0.001172 **
#> estrec:ns(log(time), df = 3)1 0.4245 0.2310 1.837 0.066151 .
#> estrec:ns(log(time), df = 3)2 1.1707 0.6608 1.772 0.076457 .
#> estrec:ns(log(time), df = 3)3 0.2638 0.1417 1.861 0.062682 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 3354.9 on 30198 degrees of freedom
#> Residual deviance: 3261.6 on 30191 degrees of freedom
#> AIC: 3277.6
#>
#> Number of Fisher Scoring iterations: 13
```

There are now many ways to plot the time-varying effect of estrogen
receptor on the hazard function. The default is to plot the 10th, 50th
and 90th quantiles of the `by`

variable:

```
# computed at the 10th, 50th and 90th quantiles of estrec
plot(mod_cb_tvc,
hazard.params = list(xvar = "time",
by = "estrec",
alpha = 1,
ylab = "Hazard"))
```

We can also show the quartiles of `estrec`

by specifying
the `breaks`

argument. If `breaks`

is a single
number, that will be the used as the number of breaks:

```
# computed at quartiles of estrec
plot(mod_cb_tvc,
hazard.params = list(xvar = c("time"),
by = "estrec",
alpha = 1,
breaks = 4,
ylab = "Hazard"))
```

Alternatively, if `breaks`

is a vector, it will be used as
the actual values to be used:

```
# computed where I want
plot(mod_cb_tvc,
hazard.params = list(xvar = c("time"),
by = "estrec",
alpha = 1,
breaks = c(3,2200),
ylab = "Hazard"))
```

```
visreg2d(mod_cb_tvc,
xvar = "time",
yvar = "estrec",
trans = exp,
print.cond = TRUE,
zlab = "Hazard",
plot.type = "image")
visreg2d(mod_cb_tvc,
xvar = "time",
yvar = "estrec",
trans = exp,
print.cond = TRUE,
zlab = "Hazard",
plot.type = "persp")
# this can also work if 'rgl' is installed
# visreg2d(mod_cb_tvc,
# xvar = "time",
# yvar = "estrec",
# trans = exp,
# print.cond = TRUE,
# zlab = "Hazard",
# plot.type = "rgl")
```

All the examples so far have only included two predictors in the regression equation. In this example, we fit a smooth hazard model with several predictors:

```
mod_cb_tvc <- fitSmoothHazard(cens ~ estrec * ns(log(time), df = 3) +
horTh +
age +
menostat +
tsize +
tgrade +
pnodes +
progrec,
data = brcancer,
time = "time")
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(mod_cb_tvc)
#> Fitting smooth hazards with case-base sampling
#>
#> Sample size: 686
#> Number of events: 299
#> Number of base moments: 29900
#> ----
#>
#> Call:
#> fitSmoothHazard(formula = cens ~ estrec * ns(log(time), df = 3) +
#> horTh + age + menostat + tsize + tgrade + pnodes + progrec,
#> data = brcancer, time = "time")
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -6.591e+01 1.754e+01 -3.758 0.000171 ***
#> estrec -4.856e-01 3.050e-01 -1.592 0.111330
#> ns(log(time), df = 3)1 3.818e+01 1.146e+01 3.332 0.000864 ***
#> ns(log(time), df = 3)2 1.130e+02 3.418e+01 3.306 0.000945 ***
#> ns(log(time), df = 3)3 2.314e+01 7.030e+00 3.292 0.000994 ***
#> horThyes -3.487e-01 1.302e-01 -2.679 0.007388 **
#> age -9.146e-03 9.295e-03 -0.984 0.325172
#> menostatPost 2.865e-01 1.847e-01 1.551 0.120900
#> tsize 7.236e-03 3.963e-03 1.826 0.067830 .
#> tgrade.L 5.386e-01 1.908e-01 2.823 0.004765 **
#> tgrade.Q -2.223e-01 1.224e-01 -1.815 0.069454 .
#> pnodes 5.418e-02 8.066e-03 6.717 1.85e-11 ***
#> progrec -2.244e-03 5.762e-04 -3.894 9.84e-05 ***
#> estrec:ns(log(time), df = 3)1 3.331e-01 2.040e-01 1.633 0.102488
#> estrec:ns(log(time), df = 3)2 9.179e-01 5.851e-01 1.569 0.116718
#> estrec:ns(log(time), df = 3)3 2.083e-01 1.260e-01 1.653 0.098405 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 3354.9 on 30198 degrees of freedom
#> Residual deviance: 3163.7 on 30183 degrees of freedom
#> AIC: 3195.7
#>
#> Number of Fisher Scoring iterations: 13
```

In the following plot, we show the time-varying effect of
`estrec`

while controlling for all other variables. By
default, the other terms in the model are set to their median if the
term is numeric or the most common category if the term is a factor. The
values of the other variables are shown in the output:

```
plot(mod_cb_tvc,
hazard.params = list(xvar = "time",
by = "estrec",
alpha = 1,
breaks = 2,
ylab = "Hazard"))
#> Conditions used in construction of plot
#> estrec: 8 / 175
#> horTh: no
#> age: 53
#> menostat: Post
#> tsize: 25
#> tgrade: II
#> pnodes: 3
#> progrec: 48
#> offset: 0
```

You can of course set the values of the other covariates as before,
i.e. by specifying the `cond`

argument as a named list to the
`hazard.params`

argument:

```
plot(mod_cb_tvc,
hazard.params = list(xvar = "time",
by = "estrec",
cond = list(tgrade = "III", age = 49),
alpha = 1,
breaks = 2,
ylab = "Hazard"))
#> Conditions used in construction of plot
#> estrec: 8 / 175
#> horTh: no
#> age: 49
#> menostat: Post
#> tsize: 25
#> tgrade: III
#> pnodes: 3
#> progrec: 48
#> offset: 0
```

In this section we illustrate how to plot hazard ratios using the
`plot`

method for objects of class `singleEventCB`

which is obtained from running the `fitSmoothHazard`

function. Note that these function have only been thoroughly tested with
`family = "glm"`

.

In what follows, the hazard ratio for a variable \(X\) is defined as

\[ \frac{h\left(t | X=x_1, \mathbf{Z}=\mathbf{z_1} ; \hat{\beta}\right)}{h(t | X=x_0, \mathbf{Z}=\mathbf{z_0} ; \hat{\beta})} \] where \(h(t|\cdot;\hat{\beta})\) is the hazard rate as a function of the variable \(t\) (which is usually time, but can be any other continuous variable), \(x_1\) is the value of \(X\) for the exposed group, \(x_0\) is the value of \(X\) for the unexposed group, \(\mathbf{Z}\) are other covariates in the model which are equal to \(\mathbf{z_1}\) in the exposed and \(\mathbf{z_0}\) in the unexposed group, and \(\hat{\beta}\) are the estimated regression coefficients.

As indicated by the formula above, it is most instructive to plot the hazard ratio as a function of a variable \(t\) only if there is an interaction between \(t\) and \(X\). Otherwise, the resulting plot will simply be a horizontal line across time.

We use data from the Manson trial (NEJM 2003) which is included in
the `casebase`

package. This randomized clinical trial
investigated the effect of estrogen plus progestin (`estPro`

)
on coronary heart disease (CHD) risk in 16,608 postmenopausal women who
were 50 to 79 years of age at base line. Participants were randomly
assigned to receive `estPro`

or `placebo`

. The
primary efficacy outcome of the trial was CHD (nonfatal myocardial
infarction or death due to CHD).

We fit a model with the interaction between time and treatment arm. We are therefore interested in visualizing the hazard ratio of the treatment over time.

```
data("eprchd")
eprchd <- transform(eprchd,
treatment = factor(treatment, levels = c("placebo","estPro")))
str(eprchd)
#> 'data.frame': 16608 obs. of 3 variables:
#> $ time : num 0.0833 0.0833 0.0833 0.0833 0.0833 ...
#> $ status : num 0 0 0 0 0 0 0 0 0 0 ...
#> $ treatment: Factor w/ 2 levels "placebo","estPro": 1 1 1 1 1 1 1 1 1 1 ...
fit_mason <- fitSmoothHazard(status ~ treatment*time,
data = eprchd,
time = "time")
summary(fit_mason)
#> Fitting smooth hazards with case-base sampling
#>
#> Sample size: 16608
#> Number of events: 324
#> Number of base moments: 32400
#> ----
#>
#> Call:
#> fitSmoothHazard(formula = status ~ treatment * time, data = eprchd,
#> time = "time")
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -6.08226 0.17462 -34.831 < 2e-16 ***
#> treatmentestPro 0.59757 0.22352 2.673 0.00751 **
#> time 0.10909 0.04742 2.300 0.02143 *
#> treatmentestPro:time -0.12467 0.06313 -1.975 0.04829 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 3635.4 on 32723 degrees of freedom
#> Residual deviance: 3626.1 on 32720 degrees of freedom
#> AIC: 3634.1
#>
#> Number of Fisher Scoring iterations: 7
```

To plot the hazard ratio, we must specify the `newdata`

argument with a covariate pattern for the reference group. In this
example, we treat the `placebo`

as the reference group.
Because we have fit an interaction with time, we also provide a sequence
of times at which we would like to calculate the hazard ratio.

```
newtime <- quantile(fit_mason[["originalData"]][[fit_mason[["timeVar"]]]],
probs = seq(0.01, 0.99, 0.01))
# reference category
newdata <- data.frame(treatment = factor("placebo",
levels = c("placebo", "estPro")),
time = newtime)
str(newdata)
#> 'data.frame': 99 obs. of 2 variables:
#> $ treatment: Factor w/ 2 levels "placebo","estPro": 1 1 1 1 1 1 1 1 1 1 ...
#> $ time : num 0.917 1.75 2.5 3.167 3.417 ...
plot(fit_mason,
type = "hr",
newdata = newdata,
var = "treatment",
increment = 1,
xvar = "time",
ci = T,
rug = T)
```

In the call to `plot`

we specify the `xvar`

which is the variable plotted on the x-axis, the `var`

argument which specified the variable for which we want the hazard
ratio. The `increment = 1`

indicates that we want to
increment `var`

by 1 level, which in this case is
`estPro`

. Alternatively, we can specify the
`exposed`

argument which should be a function that takes
`newdata`

and returns the exposed dataset. The following call
is equivalent to the one above:

```
plot(fit_mason,
type = "hr",
newdata = newdata,
exposed = function(data) transform(data, treatment = "estPro"),
xvar = "time",
ci = T,
rug = T)
```

Alternatively, if we want the `placebo`

group to be the
exposed group, we can change the `newdata`

argument to the
following:

```
newdata <- data.frame(treatment = factor("estPro",
levels = c("placebo", "estPro")),
time = newtime)
str(newdata)
#> 'data.frame': 99 obs. of 2 variables:
#> $ treatment: Factor w/ 2 levels "placebo","estPro": 2 2 2 2 2 2 2 2 2 2 ...
#> $ time : num 0.917 1.75 2.5 3.167 3.417 ...
levels(newdata$treatment)
#> [1] "placebo" "estPro"
```

Note that the reference category in `newdata`

is still
`placebo`

. Therefore we must set `increment = -1`

in order to get the `exposed`

dataset:

```
plot(fit_mason,
type = "hr",
newdata = newdata,
var = "treatment",
increment = -1,
xvar = "time",
ci = TRUE,
rug = TRUE)
```

If the \(X\) variable has more than
two levels, than, `increment`

works the same way,
e.g. `increment = 2`

will provide an `exposed`

group two levels above the value in `newdata`

.

In order to save the data used to make the plot, you simply have to
assign the call to `plot`

to a variable. This is particularly
useful if you want to really customize the plot aesthetics:

```
result <- plot(fit_mason,
type = "hr",
newdata = newdata,
var = "treatment",
increment = -1,
xvar = "time",
ci = TRUE,
rug = TRUE)
```

```
head(result)
#> treatment time log_hazard_ratio standarderror hazard_ratio lowerbound
#> 1% estPro 0.9166667 -0.4832895 0.1760663 0.6167512 0.4367592
#> 2% estPro 1.7500000 -0.3793964 0.1399023 0.6842743 0.5201698
#> 3% estPro 2.5000000 -0.2858926 0.1184127 0.7513433 0.5957243
#> 4% estPro 3.1666667 -0.2027782 0.1133642 0.8164593 0.6537908
#> 5% estPro 3.4166667 -0.1716103 0.1154383 0.8423074 0.6717526
#> 6% estPro 3.9166667 -0.1092744 0.1255777 0.8964844 0.7008916
#> upperbound
#> 1% 0.8709194
#> 2% 0.9001509
#> 3% 0.9476140
#> 4% 1.0196011
#> 5% 1.0561653
#> 6% 1.1466599
```