`pencal`

?`pencal`

is an `R`

package that has been created to make it easy and efficient to estimate and apply **Penalized Regression Calibration** (Signorelli et al., 2021).

Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing.

Penalized Regression Calibration (PRC) is a statistical method that has been developed to fill this methodological gap, making it possible to **predict survival using as predictors a set of covariates that are at the same time longitudinal and high-dimensional**.

PRC is described in detail in the following article:

Signorelli, M., Spitali, P., Al-Khalili Sgyziarto, C., The Mark-MD Consortium, Tsonaka, R. (2021). Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data. arXiv preprint: arXiv:2101.04426.

In short, PRC comprises three modelling steps:

in the first step, we

**model the trajectories described by the longitudinal biomarkers using mixed effects models**;in the second step, we

**compute subject-specific summaries of the longitudinal trajectories**. In practice, these summaries are the predicted random effects from the mixed models estimated in step 1, and they allow us to summarize the way in which the biomarkers change over time across subjects;in the third step, we

**estimate a penalized Cox model**where the summaries computed in step 3 (alongside with other relevant covariates, such as baseline age or important confounders) are employed as predictors of the survival time.

By performing these 3 steps we can estimate PRC, obtaining a model that allows us to compute predicted survival probabilities.

Additionally, one may want to quantify the predictive performance of the fitted model. To achieve this aim, in `pencal`

we have implemented a **Cluster Bootstrap Optimism Correction Procedure** (CBOCP) that can be used to obtain optimism-corrected estimates of the C index and time-dependent AUC associated to the fitted model. Depending on the dimensionality of your dataset, computing the CBOCP might be time consuming; for this reason, we offer the possibility to parallelize the CBOCP using multiple cores.

Below you can see a graphical representation of the steps involved in the estimation of PRC (see the elements in the lightblue box) and in the computation of the CBOCP (elements in the salmon box).

`pencal`

Currently, `pencal`

contains functions to estimate the PRC-LMM and PRC-MLPMM models proposed in Signorelli et al. (2021). An overview of the different functions is presented in the following subsections.

Estimation of the PRC-LMM model can be performed by running sequentially the following three functions:

`fit_lmms`

, which implements the first step of the estimation of the PRC-LMM;`summarize_lmms`

, which carries out the second step;`fit_prclmm`

, which performs the third step.

As already mentioned, these functions have to be run sequentially, with the output of `fit_lmms`

used as input for `summarize_lmms`

, and the output of `summarize_lmms`

as input for `fit_prclmm`

.

Similarly, estimation of the PRC-MLPMM model can be performed using:

`fit_mlpmms`

, which implements the first step of the estimation of the PRC-MLPMM;`summarize_mlpmms`

, which carries out the second step;`fit_prcmlpmpm`

, which performs the third step.

Computation of the predicted survival probabilities from the PRC-LMM and PRC-MLPMM models can be done with the functions `survpred_prclmm`

and `survpred_prcmlpmm`

.

The evaluation of the predicted performance of the estimated PRC-LMM and PRC-MLPMM can be done using the function `performance_prc`

, which returns the naive and optimism-corrected estimates of the C index and of the time-dependent AUC. The optimism-corrected estimates are based on the CBOCP proposed in Signorelli et al. (2021).

A technical note on how the CBOCP is implemented in `pencal`

: most of the computations required by the CBOCP are performed by `fit_lmms`

, `summarize_lmms`

and `fit_prclmm`

for the PRC-LMM, and by `fit_mlpmms`

, `summarize_mlpmms`

and `fit_prcmlpmm`

for the PRC-MLPMM. Such computations may be time-consuming, and for this reason these functions are designed to work with parallel computing (this can be easily done by setting the argument `n.cores`

to a value \(> 1\)). The last step of the CBOCP is performed by the function `performance_prc`

, which aggregates the outputs of the previous functions and computes the naive and optimism-corrected estimates of the C index and of the time-dependent AUC.

**Important note**: if you just want to estimate the PRC model, and you do not wish to compute the CBOCP, simply set `n.boots = 0`

as argument of `fit_lmms`

. If, instead, you do want to compute the CBOCP, set `n.boots`

equal to the desired number of bootstrap samples (e.g., 100).

In addition to the functions mentioned above, `pencal`

comprises also 3 functions that can be used to simulate example datasets:

`simulate_t_weibull`

to simulate survival data from a Weibull model;`simulate_prclmm_data`

to simulate an example dataset for PRC-LMM that is comprehensive of a number of longitudinal biomarkers, a survival outcome and a censoring indicator;`simulate_prcmlpmm_data`

to simulate an example dataset for PRC-MLPMM;`pencox_baseline`

and`performance_pencox_baseline`

to estimate a penalized Cox model with baseline predictors only (no longitudinal information used for prediction).

To illustrate how `pencal`

works, let us simulate an example dataset from the PRC-LMM model. Hereafter we generate a dataset that comprises \(n = 100\) subjects, \(p = 10\) longitudinal biomarkers that are measured at \(t = 0, 0.2, 0.5, 1, 1.5, 2\) years from baseline, and a survival outcome that is associated with 5 (`p.relev`

) of the 10 biomarkers:

```
set.seed(1234)
p = 10
simdata = simulate_prclmm_data(n = 100, p = p, p.relev = 5,
lambda = 0.2, nu = 1.5,
seed = 1234, t.values = c(0, 0.2, 0.5, 1, 1.5, 2))
ls(simdata)
```

`## [1] "censoring.prop" "long.data" "surv.data"`

Note that in this example we are setting \(n > p\), but `pencal`

can handle both low-dimensional (\(n > p\)) and high-dimensional (\(n \leq p\)) datasets.

In order to estimate the PRC-LMM, you need to **provide the following two inputs**:

**a dataset in long format**, which should contain (at least) the following variables: a subject identifier that should be named`id`

, the longitudinal biomarkers (here called`marker1`

, …,`marker10`

), and the relevant time variables (in this example we will use`age`

as covariate in the LMMs estimated in step 1, and`baseline.age`

as covariate in the penalized Cox model estimated in step 3):

```
## id base.age t.from.base age marker1 marker2 marker3 marker4
## 1 1 4.269437 0.0 4.269437 1.5417408 4.452282 15.13419 5.809207
## 2 1 4.269437 0.2 4.469437 1.2346437 5.252873 15.98066 5.896591
## 3 1 4.269437 0.5 4.769437 2.0773929 3.714174 18.44501 6.634897
## 4 1 4.269437 1.0 5.269437 0.2137868 4.092887 20.23194 6.179779
## 5 1 4.269437 1.5 5.769437 1.3354611 5.032044 18.99531 5.914160
## 6 1 4.269437 2.0 6.269437 0.7811953 4.946483 22.19621 5.981212
## marker5 marker6 marker7 marker8 marker9 marker10
## 1 13.62807 -7.041495 15.19982 10.12011 2.7166023 15.16749
## 2 15.03320 -5.763194 16.25356 10.20624 1.2764132 13.11855
## 3 14.90197 -6.478355 17.40369 11.74692 1.9369628 13.91899
## 4 16.48330 -8.994558 18.44549 11.91967 1.5949944 15.50285
## 5 16.38560 -9.034169 19.61104 12.59247 1.3730259 15.86172
## 6 17.23651 -10.220797 19.71013 12.79891 -0.2164965 15.89041
```

```
# visualize the trajectories for a randomly picked biomarker
library(ptmixed)
ptmixed::make.spaghetti(x = age, y = marker5,
id = id, group = id,
data = simdata$long.data,
margins = c(4, 4, 2, 2),
legend.inset = - 1)
```

**a dataset with information on the survival outcome**, which should contain (at least) the following variables: a subject identifier that should be named`id`

, the time to event outcome called`time`

, and the binary event indicator called`event`

(NB: make sure that the variable names associated to these three variables are indeed`id`

,`time`

and`event`

!)

```
## id baseline.age time event
## 1 1 4.269437 0.8368389 0
## 2 2 4.705434 1.4288656 1
## 3 3 3.220979 1.6382975 1
## 4 4 4.379400 0.5809532 1
## 5 5 4.800329 0.2441706 1
## 6 6 3.394879 0.4901404 1
```

`## [1] 0.22`

`## Loading required package: ggplot2`

`## Loading required package: ggpubr`

`pencal`

Before we begin to estimate the PRC-LMM, let’s determine the number of cores that will be used for the computation of the CBOCP. In general you can use as many cores as available to you; to do this, you can set

Since the CRAN Repository Policy allow us to use at most 2 cores when building the vignettes, in this example we will limit the number of cores used to 2:

Be aware, however, that **using more than 2 cores will speed computations up, and it is thus recommended**. Several functions in `pencal`

will actually return a warning when you perform computations using less cores than available: the goal of such warnings is to remind you that you could use more cores to speed computations up; however, if you are purposedly using a smaller number of cores you can ignore the warning.

Hereafter we show how to implement the three steps involved in the estimation of the PRC-LMM, alongside with the computation of the CBOCP.

In the first step, for each biomarker we estimate a **linear mixed model** (LMM) where the longitudinal biomarker levels \(y_{ij}\) depend on two fixed effects (one intercept, \(\beta_0\) and one slope for age, \(\beta_1\)), on a subject-specific random intercept \(u_{0i}\) and on a random slope for age \(u_{1i}\):

\[y_{ij} = \beta_0 + u_{0i} + \beta_1 a_{ij} + u_{1i} a_{ij} + \varepsilon_{ij}.\]

To do this in `R`

we use the `fit_lmms`

function:

```
y.names = paste('marker', 1:p, sep = '')
step1 = fit_lmms(y.names = y.names,
fixefs = ~ age, ranefs = ~ age | id,
long.data = simdata$long.data,
surv.data = simdata$surv.data,
t.from.base = t.from.base,
n.boots = 10, n.cores = n.cores)
```

```
## Sorting long.data by subject id
## Sorting surv.data by subject id
## Preliminary step: remove measurements taken after event / censoring.
## Removed: 209 measurements. Retained: 391 measurements.
## Estimating the LMMs on the original dataset...
## ...done
## Bootstrap procedure started
## This computation will be run in parallel, using 2 cores
## Bootstrap procedure finished
## Computation of step 1: finished :)
```

The LMM fitted here is just an example of how to model longitudinal biomarker trajectories: depending on the data you are dealing with, you may choose to specify different fixed and random effects formulas, or even to consider the MLPMM instead of the LMM.

Note that here I have set `n.boots = 10`

to reduce computing time for the CBOCP, given that CRAN only allows me to use two cores when compiling the vignette. In general, it is recommended to set `n.boots = 0`

if you do not wish to compute the CBOCP, or to set `n.boots`

equal to a larger number (e.g., 50, 100 or 200) if you want to accurately compute the CBOCP. In the latter case, consider using as many cores as available to you to speed computations up.

`fit_lmms`

returns as output a list with several elements; among them is `lmm.fits.orig`

, which contains the LMMs fitted to each biomarker:

```
## [1] "boot.ids" "call.info" "df.sanitized" "lmm.fits.boot"
## [5] "lmm.fits.orig" "n.boots"
```

```
## $marker1
## Linear mixed-effects model fit by REML
## Data: df.sub
## Log-restricted-likelihood: -678.8922
## Fixed: fixef.formula
## (Intercept) age
## 3.491217 -1.025154
##
## Random effects:
## Formula: ~age | id
## Structure: General positive-definite, Log-Cholesky parametrization
## StdDev Corr
## (Intercept) 1.4809966 (Intr)
## age 0.8241438 0.391
## Residual 0.7262114
##
## Number of Observations: 391
## Number of Groups: 100
```

For more details about the arguments of `fit_lmms`

and its outputs, see the help page: `?fit_lmms`

.

In the second step we **compute the predicted random intercepts and random slopes** for the LMMs fitted in step 1:

```
## Computing the predicted random effects on the original dataset...
## ...done
## Bootstrap procedure started
## This computation will be run in parallel, using 2 cores
## Bootstrap procedure finished
## Computation of step 2: finished :)
```

`summarize_lmms`

returns as output a list that contains, among other elements, a matrix `ranef.orig`

with the predicted random effects for the LMMs fitted in step 1:

```
## [1] "boot.ids" "call" "n.boots" "ranef.boot.train"
## [5] "ranef.boot.valid" "ranef.orig"
```

```
## marker1_b_int marker1_b_age marker2_b_int marker2_b_age
## 1 0.468791151 0.50257974 0.4806176 0.9897538
## 2 -0.651187418 -1.18949489 0.3094290 -1.2798246
## 3 0.541710892 1.35639622 -1.1855801 -0.4835886
## 4 1.065842498 0.82801424 -0.4332657 0.1949000
## 5 0.002714736 -0.09192979 -0.4224219 -1.4788431
```

For more details about the arguments of `summarize_lmms`

and its outputs, see the help page: `?summarize_lmms`

.

Lastly, in the third step of PRC-LMM we estimate a **penalized Cox model** where we employ as predictors baseline age and all the summaries (predicted random effects) computed in step 2:

```
step3 = fit_prclmm(object = step2, surv.data = simdata$surv.data,
baseline.covs = ~ baseline.age,
penalty = 'ridge', n.cores = n.cores)
```

```
## Estimated penalized Cox model on the original dataset...
## ...done
## Bootstrap procedure started
## This computation will be run in parallel, using 2 cores
## Bootstrap procedure finished
## Computation of step 3: finished :)
```

In this example I have set `penalty = 'ridge'`

, but you may also use elasticnet or lasso as alternatives. Moreover, by default the predicted random effects are standardized when included in the penalized Cox model (if you don’t want to perform such standardization, set `standardize = F`

).

`fit_prclmm`

returns as output a list that contains, among other elements, the fitted penalized Cox model `pcox.orig`

, which is a `glmnet`

object:

`## [1] "boot.ids" "call" "n.boots" "pcox.boot" "pcox.orig" "surv.data"`

`## [1] "cv.glmnet"`

`## Loading required package: Matrix`

`## Loaded glmnet 4.0-2`

```
## baseline.age marker1_b_int marker1_b_age marker2_b_int marker2_b_age
## 1 0.001253804 -0.02091294 -0.005629194 0.05632732 0.1530983
## marker3_b_int marker3_b_age marker4_b_int marker4_b_age marker5_b_int
## 1 0.103437 0.06736506 -0.0103572 -0.0005451742 -0.3558578
## marker5_b_age marker6_b_int marker6_b_age marker7_b_int marker7_b_age
## 1 -0.074645 2.170329 0.02189938 0.03473504 0.01001565
## marker8_b_int marker8_b_age marker9_b_int marker9_b_age marker10_b_int
## 1 -0.3016531 0.02321143 -0.1515156 -0.03053159 0.02916538
## marker10_b_age
## 1 0.001877107
```

For more details about the arguments of `fit_prclmm`

and its outputs, see the help page: `?fit_prclmm`

.

Estimation of the PRC-MLPMM(U) and PRC-MLPMM(U+B) models proceeds in a similar fashion. For details and examples about this multivariate approach, see: `?fit_mlpmms`

(step 1), `?summarize_mlpmms`

(step 2), and `?fit_prcmlpmm`

(step 3).

After fitting the model, you will probably want to obtain **predicted survival probabilities** for each individual at several time points. This can be done through the function `survpred_prclmm`

, which takes as inputs the outputs of step1, step 2 and step 3, alongside with the time points at which to compute the survival probabilities:

```
## id S(1) S(2) S(3)
## 1 1 0.4685893 0.1666528 0.08151238
## 2 2 0.7506514 0.5076432 0.38729468
## 3 3 0.7088022 0.4432699 0.32036642
## 4 4 0.5153831 0.2087025 0.11167056
## 5 5 0.6207733 0.3239896 0.20662124
## 6 6 0.5630938 0.2572870 0.14965965
```

To accurately quantify the predicted performance of the fitted PRC model, we need to recur to some form of **internal validation** strategy (e.g., bootstrap, cross-validation, etc…).

In `pencal`

the internal validation is performed through a Cluster Bootstrap Optimism Correction Procedure (CBOCP) that allows to compute **optimism-corrected estimates of the concordance (C) index and of the time-dependent AUC**.

Most of the steps that the CBOCP requires are directly computed by the functions `fit_lmms`

, `summarize_lmms`

and `fit_prclmm`

whenever the argument `n.boots`

of `fit_lmms`

is set equal to an integer > 0 (in other words: most of the computations needed for the CBOCP have already been performed in the code chunks executed above, so we are almost done!).

To gather the results of the CBOCP we can use the function `performance_prc`

:

```
## Computation of optimism correction started
## This computation will be run in parallel, using 2 cores
## Computation of the optimism correction: finished :)
```

```
## n.boots C.naive cb.opt.corr C.adjusted
## 1 10 0.7855 -0.0397 0.7458
```

```
## pred.time tdAUC.naive cb.opt.corr tdAUC.adjusted
## 1 1 0.8874 -0.0386 0.8488
## 2 2 0.8337 -0.0442 0.7895
## 3 3 0.8303 -0.0674 0.7629
```

From the results above we can see that:

- the naive C index is estimated = 0.786, and the optimism-corrected C index is estimated = 0.747;
- the naive tdAUC for predictions at 1 year from baseline is estimated = 0.887, and the optimism-corrected estimate is = 0.850;
- the naive tdAUC for predictions at 2 years from baseline is estimated = 0.834, and the optimism-corrected estimate is = 0.796;
- the naive tdAUC for predictions at 3 years from baseline is estimated = 0.830, and the optimism-corrected estimate is = 0.770.

The aim of this vignette is to provide a quick-start introduction to the `R`

package `pencal`

. Here I have focused my attention on the fundamental aspects that one needs to use the package.

Further details, functions and examples can be found in the manual of the package.

The description of the method is available in an article that you can read here.