---
title: "Stagewise Variable Selection for Joint Semi-Competing Risk Models"
author: "Lingfeng Huo, Ziren Jiang, Jue Hou, Jared D. Huling"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{Stagewise Variable Selection for Joint Semi-Competing Risk Models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse  = TRUE,
  comment   = "#>",
  fig.width = 7,
  fig.height = 5
)
set.seed(2025)
```

## Overview

The `swjm` package implements **stagewise variable selection for joint models
of semi-competing risks**.  In many medical settings — such as hospital
readmission following discharge — patients can experience a *non-terminal*
recurrent event (readmission) and a *terminal* event (death).  Death precludes
future readmissions, but readmission does not preclude death, a structure
known as **semi-competing risks**.

Two joint model frameworks are supported:

| Model | Type | Recurrence process | Terminal process |
|-------|------|--------------------|-----------------|
| **JFM** | Joint Frailty Model (Cox) | Proportional hazards | Proportional hazards |
| **JSCM** | Joint Scale-Change Model (AFT) | Rank-based estimating equations | Rank-based estimating equations |

Three penalty types are available: **cooperative lasso** (`"coop"`), **lasso**
(`"lasso"`), and **group lasso** (`"group"`).  The cooperative lasso is the
recommended default; it encourages predictors that affect both outcomes to
enter together with the same sign.

---

## 1. Statistical Background

### 1.1 Semi-Competing Risks

Let $N_i^R(t)$ count the readmission events for subject $i$ by time $t$, and
let $T_i^D$ denote the time to death.  Death censors future readmissions;
readmission does not censor death.

Each subject $i$ ($i = 1, \ldots, n$) has:

- A $p$-dimensional covariate vector $Z_i$ (possibly time-varying).
- An observed follow-up interval $[0, C_i]$ where $C_i$ is the censoring time.

The parameter vector of interest is
$$\theta = (\alpha^\top, \beta^\top)^\top \in \mathbb{R}^{2p},$$
where $\alpha \in \mathbb{R}^p$ governs the recurrence (readmission) process
and $\beta \in \mathbb{R}^p$ governs the terminal (death) process.

### 1.2 Joint Frailty Model (JFM)

The JFM (Kalbfleisch et al., 2013) introduces a subject-specific frailty
$\omega_i \sim \text{Gamma}(\kappa, \kappa)$ that links the two processes:

$$
\lambda^R(t \mid Z_i, \omega_i) = \lambda_0^R(t)\,
  e^{\alpha^\top Z_i(t)}\, \omega_i,
\qquad
\lambda^D(t \mid Z_i, \omega_i) = \lambda_0^D(t)\,
  e^{\beta^\top Z_i}\, \omega_i^\eta,
$$

where $\lambda_0^R$ and $\lambda_0^D$ are unspecified baseline hazard
functions.  Marginalising over $\omega_i$ yields estimating equations that
are functions only of $(\alpha, \beta)$ and the two baseline hazards.

In the package, `alpha` is always the **readmission** coefficient vector and
`beta` is always the **death** coefficient vector.

### 1.3 Joint Scale-Change Model (JSCM)

The JSCM (Xu et al.) replaces proportional hazards with an AFT-type
scale-change specification:

$$
\lambda^R(t \mid Z_i) = e^{\alpha^\top Z_i}\,
  \lambda_0^R(t\,e^{\alpha^\top Z_i}),
\qquad
\lambda^D(t \mid Z_i) = e^{\beta^\top Z_i}\,
  \lambda_0^D(t\,e^{\beta^\top Z_i}).
$$

Estimation is based on rank-based estimating equations implemented in C++
via RcppArmadillo.

### 1.4 Stagewise Variable Selection

The goal is to find a sparse $\theta$ that minimizes a penalized estimating
equation criterion.  Three penalty structures are supported:

**Scaled lasso** (independent selection):
$$
\text{pen}(\theta;\lambda) = \lambda \sum_{j=1}^p
  \left(\frac{|\alpha_j|}{s_\alpha} + \frac{|\beta_j|}{s_\beta}\right),
$$

**Group lasso** (simultaneous entry of $(\alpha_j, \beta_j)$ pairs):
$$
\text{pen}(\theta;\lambda) = \lambda \sum_{j=1}^p
  \left\|\left(\frac{\alpha_j}{s_\alpha}, \frac{\beta_j}{s_\beta}\right)\right\|_2,
$$

**Cooperative lasso** (encourages shared sign and support):
$$
\text{pen}(\theta;\lambda) = \lambda \sum_{j=1}^p
\begin{cases}
  \left\|\left(\frac{\alpha_j}{s_\alpha},
    \frac{\beta_j}{s_\beta}\right)\right\|_2
    & \text{if } \text{sgn}(\alpha_j) = \text{sgn}(\beta_j), \\[6pt]
  \left\|\left(\frac{\alpha_j}{s_\alpha},
    \frac{\beta_j}{s_\beta}\right)\right\|_\infty
    & \text{if } \text{sgn}(\alpha_j) \ne \text{sgn}(\beta_j).
\end{cases}
$$

The cooperative lasso uses the L2 norm when both coefficients agree in sign
(rewarding variables that affect both outcomes in the same direction) and the
L-infinity norm when they disagree (applying a harsher penalty).

The stagewise algorithm approximates the penalized solution by taking small
gradient steps in the direction determined by the dual norm of the current
estimating equation score.  At each iteration:

1. **Compute the EE score** $U(\theta)$ (gradient of the unpenalized
   estimating equation objective).
2. **Find the active coordinate(s)** with the largest penalized dual norm.
3. **Update** $\theta$ by a small step $\epsilon$ in that direction.

The regularization path is indexed by $\lambda$, recorded as the dual norm at
each step.  Cross-validation over a grid of $\lambda$ values selects the
optimal tuning parameter.

### 1.5 Cross-Validation

`cv_stagewise()` performs stratified K-fold cross-validation.  For each fold,
it evaluates the cross-fitted EE score norm — the score from the held-out fold
evaluated at the coefficient fit from the remaining folds.  The optimal
$\lambda$ minimizes the total cross-fitted norm across both sub-models.

---

## 2. Installation

```{r install, eval = FALSE}
# From the package source directory:
devtools::install("swjm")

# Or from a built tarball:
install.packages("swjm_0.1.0.tar.gz", repos = NULL, type = "source")
```

---

## 3. Data Format

All functions expect a **data frame in counting-process (interval) format**
with the following required columns:

| Column | Description |
|--------|-------------|
| `id` | Subject identifier |
| `t.start` | Interval start time |
| `t.stop` | Interval end time |
| `event` | 1 = readmission (recurrent event), 0 = death/censoring row |
| `status` | 1 = death, 0 = alive/censored |
| `x1, …, xp` | Covariate columns |

Each subject contributes multiple rows:

- One row per readmission interval (with `event = 1`), followed by
- One terminal row (with `event = 0`) recording either death (`status = 1`)
  or censoring (`status = 0`).

The covariate values may differ across rows for the same subject (JFM supports
time-varying covariates; JSCM uses the baseline values from the `event = 0`
rows).

---

## 4. Simulating Data

`generate_data()` is a unified data-generation interface for both models.

```{r library}
library(swjm)
```

### 4.1 Joint Frailty Model data

```{r gen-jfm}
set.seed(123)
dat_jfm  <- generate_data(n = 500, p = 10, scenario = 1, model = "jfm")
Data_jfm <- dat_jfm$data

# Preview
head(Data_jfm[, 1:8])
```

JFM: `r length(unique(Data_jfm$id))` subjects, `r nrow(Data_jfm)` rows, `r sum(Data_jfm$event)` readmissions, `r sum(Data_jfm$status)` deaths

The returned list also contains the true generating coefficients:

True alpha (readmission):

```{r true-jfm-alpha}
round(dat_jfm$alpha_true, 2)
```

True beta (death):

```{r true-jfm-beta}
round(dat_jfm$beta_true, 2)
```

**Scenario descriptions** (for both JFM and JSCM):

| Scenario | Signal structure |
|----------|-----------------|
| 1 | Variables affecting readmission only, death only, and both processes |
| 2 | Larger block of shared-sign signals |
| 3 | Mixed-sign signals (some variables have opposite effects on the two outcomes) |

### 4.2 Joint Scale-Change Model data

```{r gen-jscm}
set.seed(456)
dat_jscm  <- generate_data(n = 500, p = 10, scenario = 1, model = "jscm")
Data_jscm <- dat_jscm$data
```

JSCM: `r length(unique(Data_jscm$id))` subjects, `r nrow(Data_jscm)` rows, `r sum(Data_jscm$event)` readmissions, `r sum(Data_jscm$status)` deaths

For the JSCM, covariates are drawn from $\text{Uniform}(-1, 1)$, and a
gamma frailty ($\text{shape} = 4$, $\text{scale} = 0.25$) is used in
simulation.  Censoring times are $\text{Uniform}(0, 4)$.

---

## 5. Joint Frailty Model (JFM) Workflow

### 5.1 Fit the Stagewise Regularization Path

`stagewise_fit()` traces the full coefficient path as $\lambda$ decreases from
a large value (all coefficients zero) to a small value (many active variables):

```{r fit-jfm}
fit_jfm <- stagewise_fit(
  Data_jfm,
  model   = "jfm",
  penalty = "coop"    # cooperative lasso
)
fit_jfm
```

The returned `swjm_path` object contains:

| Component | Description |
|-----------|-------------|
| `alpha` | $p \times (k+1)$ matrix of readmission coefficients along the path |
| `beta` | $p \times (k+1)$ matrix of death coefficients along the path |
| `theta` | $2p \times (k+1)$ combined matrix (`rbind(alpha, beta)`) |
| `lambda` | Dual norm at each step (regularization path index) |
| `model` | `"jfm"` or `"jscm"` |
| `penalty` | `"coop"`, `"lasso"`, or `"group"` |
| `p` | Number of covariates |

### 5.2 Explore the Regularization Path

```{r path-explore}
p <- 10
k <- ncol(fit_jfm$alpha)
active_final <- which(fit_jfm$alpha[, k] != 0 |
                      fit_jfm$beta[, k]  != 0)
```

- Path length: `r k` steps
- Lambda range: [`r sprintf("%.4g", min(fit_jfm$lambda))`, `r sprintf("%.4g", max(fit_jfm$lambda))`]
- Active variables at final step: `r paste(active_final, collapse = " ")`

Readmission (alpha) coefficients at the final step:

```{r path-explore-alpha}
round(fit_jfm$alpha[, k], 4)
```

`summary()` shows a compact table of path-end coefficients annotated with
variable type (shared, readmission-only, or death-only):

```{r path-summary}
summary(fit_jfm)
```

### 5.3 Plot the Coefficient Path

`plot()` produces a glmnet-style coefficient trajectory plot.  Two panels are
drawn by default — one for readmission ($\alpha$) and one for death ($\beta$)
— with the number of active variables on the top axis.

```{r plot-path, fig.height = 8}
plot(fit_jfm)
```

To plot only one sub-model:

```{r plot-path-re, fig.height = 5}
plot(fit_jfm, which = "readmission")
```

### 5.4 Cross-Validation

`cv_stagewise()` selects the optimal $\lambda$ by K-fold
cross-validation using cross-fitted EE score norms.

It is good practice to restrict the $\lambda$ grid to the **strictly
decreasing** portion of the path (using `extract_decreasing_indices()`):

```{r cv-jfm-prep}
lambda_path <- fit_jfm$lambda
dec_idx     <- swjm:::extract_decreasing_indices(lambda_path)
lambda_seq  <- lambda_path[dec_idx]
```

Full path: `r length(lambda_path)` steps; decreasing path: `r length(lambda_seq)` steps

```{r cv-jfm, cache = TRUE}
set.seed(1)
cv_jfm <- cv_stagewise(
  Data_jfm,
  model      = "jfm",
  penalty    = "coop",
  lambda_seq = lambda_seq,
  K          = 3L
)
cv_jfm
```

The returned `swjm_cv` object contains:

| Component | Description |
|-----------|-------------|
| `alpha` | Readmission coefficients at the optimal $\lambda$ |
| `beta` | Death coefficients at the optimal $\lambda$ |
| `position_CF` | Index of optimal $\lambda$ in `lambda_seq` |
| `lambda_seq` | The $\lambda$ grid used for cross-validation |
| `Scorenorm_crossfit` | Combined cross-fitted EE norm over the grid |
| `Scorenorm_crossfit_re` | Readmission component |
| `Scorenorm_crossfit_ce` | Death component |
| `n_active_alpha` | Number of active readmission variables per $\lambda$ |
| `n_active_beta` | Number of active death variables per $\lambda$ |
| `n_active` | Total active variables |
| `baseline` | Cumulative baseline hazards (Breslow for JFM; Nelson-Aalen on accelerated scale for JSCM) |

The optimal $\lambda$ is `cv_jfm$lambda_seq[cv_jfm$position_CF]`.

### 5.5 Plot the CV Results

```{r plot-cv}
plot(cv_jfm)
```

The plot shows three curves: the combined norm (black, solid), the readmission
component (blue, dashed), and the death component (red, dotted).  The vertical
dashed line marks the optimal $\lambda$.

### 5.6 Extract Coefficients and Summarize

Selected readmission (alpha) variables: `r paste(which(cv_jfm$alpha != 0), collapse = " ")`

Selected death (beta) variables: `r paste(which(cv_jfm$beta != 0), collapse = " ")`

Nonzero alpha:

```{r coef-jfm-alpha}
round(cv_jfm$alpha[cv_jfm$alpha != 0], 4)
```

Nonzero beta:

```{r coef-jfm-beta}
round(cv_jfm$beta[cv_jfm$beta != 0], 4)
```

`summary()` shows a formatted table with the CV-optimal coefficients:

```{r summary-jfm}
summary(cv_jfm)
```

`coef()` returns the combined $2p$-vector `c(alpha, beta)` for
programmatic use:

```{r coef-vec}
theta_best <- coef(cv_jfm)
length(theta_best)  # 2p
```

### 5.7 Baseline Hazard

`baseline_hazard()` evaluates the cumulative baseline hazards at specified
time points.  For JFM, Breslow-type estimators are used:

```{r baseline}
bh <- baseline_hazard(cv_jfm, times = c(0.5, 1.0, 2.0, 4.0, 6.0))
print(bh)
```

To retrieve only one of the two processes:

```{r baseline-re}
bh_re <- baseline_hazard(cv_jfm, times = seq(0, 5, by = 0.5),
                         which = "readmission")
head(bh_re)
```

### 5.8 Survival Prediction

`predict()` computes subject-specific survival curves for both readmission and
death.  For JFM, Breslow cumulative baseline hazards are used:
$$
S_{\text{re}}(t \mid z) = \exp\!\bigl(-\hat\Lambda_0^r(t)\,
  e^{\hat\alpha^\top z}\bigr), \qquad
S_{\text{de}}(t \mid z) = \exp\!\bigl(-\hat\Lambda_0^d(t)\,
  e^{\hat\beta^\top z}\bigr).
$$
For JSCM, Nelson-Aalen baselines on the accelerated time scale are used
(see Section 7.5).

```{r predict-jfm, fig.height = 7}
set.seed(7)
newz <- matrix(rnorm(30), nrow = 12, ncol = 10)
rownames(newz) <- paste0("Patient_", 1:12)
colnames(newz) <- paste0("x", 1:10)

pred <- predict(cv_jfm, newdata = newz)
pred
```

The `swjm_pred` object contains:

- `S_re`: readmission-free survival matrix (subjects × time points)
- `S_de`: death-free survival matrix
- `lp_re`: linear predictors $\hat\alpha^\top z_i$
- `lp_de`: linear predictors $\hat\beta^\top z_i$
- `contrib_re`, `contrib_de`: per-predictor contributions $\hat\alpha_j z_{ij}$

```{r pred-survival}
# Survival probabilities for all subjects at first few time points
round(pred$S_re[, 1:5], 3)
```

`plot()` on a `swjm_pred` object produces a four-panel figure: survival curves
for both processes (all subjects in grey, highlighted subject in color) plus
bar charts of predictor contributions:

```{r plot-pred, fig.height = 8}
plot(pred, which_subject = 7)
```

To focus on only one process:

```{r plot-pred-re, fig.height = 5}
plot(pred, which_subject = 2, which_process = "readmission")
```

---

## 6. Other Penalty Types (JFM)

All three penalties are available for both models.  Here we illustrate the
lasso and group lasso on the JFM data.

### 6.1 Lasso

The lasso penalizes each coordinate independently.  It allows variables to
enter the readmission path without entering the death path (and vice versa):

```{r lasso, eval = FALSE}
fit_lasso <- stagewise_fit(Data_jfm, model = "jfm", penalty = "lasso")
set.seed(2)
cv_lasso <- cv_stagewise(Data_jfm, model = "jfm", penalty = "lasso", K = 3L)
summary(cv_lasso)
```

### 6.2 Group Lasso

The group lasso treats $(\alpha_j, \beta_j)$ pairs as groups; a variable
enters (or leaves) both sub-models simultaneously:

```{r group, eval = FALSE}
fit_group <- stagewise_fit(Data_jfm, model = "jfm", penalty = "group")
set.seed(3)
cv_group <- cv_stagewise(Data_jfm, model = "jfm", penalty = "group", K = 3L)
summary(cv_group)
```

### 6.3 Comparing Penalties

The cooperative lasso typically achieves better variable selection than the
standard lasso when the true signal is sparse and shared between outcomes.  The
group lasso is a good alternative when you expect all relevant predictors to
affect both outcomes with comparable magnitude.

---

## 7. Joint Scale-Change Model (JSCM) Workflow

The JSCM workflow mirrors the JFM workflow with two differences:

- The default step size is smaller (`eps = 0.01`) and more iterations are
  needed (`max_iter = 5000`).
- The EE is rank-based (implemented in C++ via RcppArmadillo).
- Survival curves are computed via a **Nelson-Aalen estimator on the
  accelerated time scale**.  For subject $i$ with linear predictor
  $\hat\alpha^\top z_i$, the recurrence survival function is
  $S_{\text{re}}(t \mid z_i) = \exp\!\bigl(-\hat\Lambda_0^r(t\,
  e^{\hat\alpha^\top z_i})\bigr)$, where $\hat\Lambda_0^r$ is estimated by
  pooling all accelerated event times $t_{ij}\,e^{\hat\alpha^\top z_i}$.

### 7.1 Fit the Stagewise Path

```{r fit-jscm}
set.seed(456)
dat_jscm  <- generate_data(n = 500, p = 10, scenario = 1, model = "jscm")
Data_jscm <- dat_jscm$data

fit_jscm <- stagewise_fit(Data_jscm, model = "jscm", penalty = "coop")
fit_jscm
```

### 7.2 Cross-Validation

```{r cv-jscm, cache = TRUE}
lambda_path_jscm <- fit_jscm$lambda
dec_idx_jscm     <- swjm:::extract_decreasing_indices(lambda_path_jscm)
lambda_seq_jscm  <- lambda_path_jscm[dec_idx_jscm]

set.seed(10)
cv_jscm <- cv_stagewise(
  Data_jscm,
  model      = "jscm",
  penalty    = "coop",
  lambda_seq = lambda_seq_jscm,
  K          = 3L
)
cv_jscm
```

### 7.3 Results

Selected alpha (readmission): `r paste(which(cv_jscm$alpha != 0), collapse = " ")`

Selected beta (death): `r paste(which(cv_jscm$beta != 0), collapse = " ")`

True nonzero alpha: `r paste(which(dat_jscm$alpha_true != 0), collapse = " ")`

True nonzero beta: `r paste(which(dat_jscm$beta_true != 0), collapse = " ")`

```{r plot-cv-jscm}
plot(cv_jscm)
```

```{r summary-jscm}
summary(cv_jscm)
```

### 7.4 Baseline Hazard (JSCM)

`baseline_hazard()` works for the JSCM as well.  The baseline is estimated via
Nelson-Aalen on the accelerated time scale: each subject's event times are
multiplied by their acceleration factor $e^{\hat\alpha^\top z_i}$ before
pooling, so the resulting $\hat\Lambda_0^r$ is on the common (baseline)
time scale.

```{r baseline-jscm}
bh_jscm <- baseline_hazard(cv_jscm, times = c(0.5, 1.0, 2.0, 3.0, 4.0))
print(bh_jscm)
```

### 7.5 Survival Prediction and AFT Interpretation

`predict()` returns subject-specific survival curves for both processes via:
$$
S_{\text{re}}(t \mid z_i) = \exp\!\bigl(-\hat\Lambda_0^r(t\,e^{\hat\alpha^\top z_i})\bigr),
\qquad
S_{\text{de}}(t \mid z_i) = \exp\!\bigl(-\hat\Lambda_0^d(t\,e^{\hat\beta^\top z_i})\bigr).
$$

The linear predictor $\hat\alpha^\top z_i$ is a **log time-acceleration
factor**: $e^{\hat\alpha^\top z_i} > 1$ means events are expected sooner than
baseline; $< 1$ means later.  Each term $e^{\hat\alpha_j z_{ij}}$ is the
multiplicative contribution of predictor $j$:

| Value of $e^{\hat\alpha_j z_{ij}}$ | Interpretation |
|------|------|
| $> 1$ | predictor $j$ accelerates events — shorter time to readmission |
| $= 1$ | no effect on this subject's timing |
| $< 1$ | predictor $j$ decelerates events — longer time to readmission |

```{r predict-jscm}
set.seed(7)
newz_jscm <- matrix(runif(30, -1, 1), nrow = 3, ncol = 10)
rownames(newz_jscm) <- paste0("Patient_", 1:3)

pred_jscm <- predict(cv_jscm, newdata = newz_jscm)
pred_jscm
```

Recurrence time-acceleration factors (total per subject):

```{r predict-jscm-accel}
round(pred_jscm$time_accel_re, 3)
```

`plot()` produces the same four-panel layout as for the JFM: survival curves
for both processes (all subjects in grey, highlighted subject in color) plus
bar charts of log time-acceleration contributions.  The survival panel titles
show each subject's total acceleration factor.

```{r plot-pred-jscm, fig.height = 8}
plot(pred_jscm, which_subject = 1)
```

---

## 8. Interpreting Output

### 8.1 Alpha and Beta Conventions

In both JFM and JSCM, `alpha` governs the recurrence (readmission) process
and `beta` governs the terminal (death) process.  The interpretation of the
coefficients differs by model:

**JFM (proportional hazards):**

- `alpha[j] > 0`: covariate $j$ increases the recurrence hazard — more
  frequent readmissions for higher values of $x_j$.
- `beta[j] > 0`: covariate $j$ increases the death hazard.
- The subject-specific contribution $\hat\alpha_j z_{ij}$ is an additive
  log-hazard-ratio contribution.  Positive = higher risk; negative = lower risk.

**JSCM (scale-change / AFT-type):**

- `alpha[j] > 0`: covariate $j$ accelerates the recurrence process — events
  happen sooner for higher values of $x_j$.
- `beta[j] > 0`: covariate $j$ accelerates the terminal process.
- The subject-specific contribution $\hat\alpha_j z_{ij}$ is an additive
  **log time-acceleration** contribution.  Exponentiating gives the
  multiplicative factor on the time scale: $e^{\hat\alpha_j z_{ij}} > 1$
  means shorter event times (acceleration); $< 1$ means longer times
  (deceleration).

The combined coefficient vector `coef(cv)` returns `c(alpha, beta)`, the
first $p$ elements being readmission and the last $p$ being death.

### 8.2 Cooperative Lasso and Variable Grouping

The cooperative lasso categorizes selected variables into groups:

| Pattern | Interpretation |
|---------|---------------|
| `alpha[j] != 0`, `beta[j] == 0` | Readmission-only predictor |
| `alpha[j] == 0`, `beta[j] != 0` | Death-only predictor |
| `alpha[j] != 0`, `beta[j] != 0`, same sign | Shared predictor (cooperating) |
| `alpha[j] != 0`, `beta[j] != 0`, opposite sign | Shared predictor (competing) |

Variables with the same nonzero sign in both $\alpha$ and $\beta$ indicate
factors that simultaneously increase risk for both readmission and death —
clinically meaningful when seeking joint risk factors.

```{r interpret}
a <- cv_jfm$alpha
b <- cv_jfm$beta

nz_a <- which(a != 0)
nz_b <- which(b != 0)
shared <- intersect(nz_a, nz_b)

same_sign <- if (length(shared) > 0) shared[sign(a[shared]) == sign(b[shared])] else integer(0)
opp_sign  <- if (length(shared) > 0) shared[sign(a[shared]) != sign(b[shared])] else integer(0)
```

- Readmission-only: `r paste(setdiff(nz_a, nz_b), collapse = ", ")`
- Death-only: `r paste(setdiff(nz_b, nz_a), collapse = ", ")`
- Shared (same sign): `r paste(same_sign, collapse = ", ")`
- Shared (opp. sign): `r paste(opp_sign, collapse = ", ")`

### 8.3 Survival Curve Interpretation

The survival curves from `predict()` answer:

- **`S_re(t | z)`**: probability that subject $z$ has not been readmitted
  by time $t$.
- **`S_de(t | z)`**: probability that subject $z$ has not died by time $t$.

For JFM these use Breslow cumulative baselines; for JSCM they use
Nelson-Aalen baselines on the accelerated time scale.

The predictor contribution matrices (`contrib_re`, `contrib_de`) show the
additive contribution of each covariate to the log-hazard (JFM) or log
time-acceleration (JSCM) for that subject.  For JFM, positive contributions
increase risk; negative reduce it.  For JSCM, positive contributions
accelerate events; negative decelerate them.

```{r contrib-example}
c1_re <- pred$contrib_re[1, ]
c1_de <- pred$contrib_de[1, ]
```

Readmission log-hazard contributions for Patient\_1 (nonzero):

```{r contrib-re}
round(c1_re[c1_re != 0], 4)
```

Death log-hazard contributions for Patient\_1 (nonzero):

```{r contrib-de}
round(c1_de[c1_de != 0], 4)
```

---

## 9. Default Parameters

| Parameter | JFM default | JSCM default | Description |
|-----------|-------------|--------------|-------------|
| `eps` | 0.1 | 0.01 | Step size (smaller for JSCM for numerical stability) |
| `max_iter` | 5000 | 5000 | Maximum stagewise iterations |
| `pp` | `max_iter` | `max_iter` | Early-stopping window (checks every `pp` steps) |

Early stopping triggers when a single coordinate dominates every step in the
last `pp` iterations.  Both models disable early stopping by default
(`pp = max_iter`) so that weaker true signals have time to accumulate before
the path terminates.  Both models use `max_iter = 5000` by default: for
JSCM the small step size (`eps = 0.01`) requires many iterations to
accumulate coefficients, and for JFM a long path is needed for the
cross-validated score to reach its minimum within the path rather than
at the boundary.

---

## 10. Model Evaluation

### 10.1 Coefficient Recovery

Compare CV-optimal estimates to the true generating coefficients.
Variables that are truly nonzero or were selected are shown; all others
were correctly excluded.

```{r coef-compare}
p <- 10

show_jfm <- sort(which(dat_jfm$alpha_true != 0 | cv_jfm$alpha != 0 |
                        dat_jfm$beta_true  != 0 | cv_jfm$beta  != 0))

coef_df <- data.frame(
  variable   = paste0("x", show_jfm),
  true_alpha = round(dat_jfm$alpha_true[show_jfm], 3),
  est_alpha  = round(cv_jfm$alpha[show_jfm],       3),
  true_beta  = round(dat_jfm$beta_true[show_jfm],  3),
  est_beta   = round(cv_jfm$beta[show_jfm],        3)
)
colnames(coef_df) <- c("variable", "alpha_true", "alpha_est",
                        "beta_true", "beta_est")
print(coef_df, row.names = FALSE)
```

JFM alpha: TP=`r sum(cv_jfm$alpha != 0 & dat_jfm$alpha_true != 0)` FP=`r sum(cv_jfm$alpha != 0 & dat_jfm$alpha_true == 0)` FN=`r sum(cv_jfm$alpha == 0 & dat_jfm$alpha_true != 0)` | beta: TP=`r sum(cv_jfm$beta != 0 & dat_jfm$beta_true != 0)` FP=`r sum(cv_jfm$beta != 0 & dat_jfm$beta_true == 0)` FN=`r sum(cv_jfm$beta == 0 & dat_jfm$beta_true != 0)`

```{r coef-compare-jscm}
show_jscm <- sort(which(dat_jscm$alpha_true != 0 | cv_jscm$alpha != 0 |
                         dat_jscm$beta_true  != 0 | cv_jscm$beta  != 0))

coef_jscm <- data.frame(
  variable   = paste0("x", show_jscm),
  true_alpha = round(dat_jscm$alpha_true[show_jscm], 3),
  est_alpha  = round(cv_jscm$alpha[show_jscm],        3),
  true_beta  = round(dat_jscm$beta_true[show_jscm],  3),
  est_beta   = round(cv_jscm$beta[show_jscm],         3)
)
colnames(coef_jscm) <- c("variable", "alpha_true", "alpha_est",
                          "beta_true", "beta_est")
print(coef_jscm, row.names = FALSE)
```

JSCM alpha: TP=`r sum(cv_jscm$alpha != 0 & dat_jscm$alpha_true != 0)` FP=`r sum(cv_jscm$alpha != 0 & dat_jscm$alpha_true == 0)` FN=`r sum(cv_jscm$alpha == 0 & dat_jscm$alpha_true != 0)` | beta: TP=`r sum(cv_jscm$beta != 0 & dat_jscm$beta_true != 0)` FP=`r sum(cv_jscm$beta != 0 & dat_jscm$beta_true == 0)` FN=`r sum(cv_jscm$beta == 0 & dat_jscm$beta_true != 0)`

### 10.2 Time-Varying AUC

We use the `timeROC` package (Blanche et al., 2013) to compute
cause-specific time-varying AUC in the competing-risk framework.
Each subject contributes at most a first-readmission event (cause 1)
and a death event (cause 2).  Each sub-model is assessed with its own
linear predictor: $\hat\alpha^\top z_i$ for readmission,
$\hat\beta^\top z_i$ for death.

> **Note**: AUC is evaluated on the training data for illustration.
> In practice use held-out or cross-validated predictions.

```{r auc-prep}
# Construct competing-risk dataset:
# Keep first readmission (event==1 & t.start==0) + death/censor (event==0).
# Status: 1 = first readmission, 2 = death, 0 = censored.
.cr_data <- function(Data) {
  d3 <- Data[Data$event == 0 | (Data$event == 1 & Data$t.start == 0), ]
  d3 <- d3[order(d3$id, d3$t.start, d3$t.stop), ]
  status <- ifelse(d3$event == 1 & d3$status == 0, 1L,
             ifelse(d3$event == 0 & d3$status == 0, 0L, 2L))
  list(data = d3, status = status)
}

cr_jfm  <- .cr_data(Data_jfm)
cr_jscm <- .cr_data(Data_jscm)

# Baseline covariates (one row per subject)
Z_jfm  <- as.matrix(Data_jfm[!duplicated(Data_jfm$id),   paste0("x", 1:p)])
Z_jscm <- as.matrix(Data_jscm[!duplicated(Data_jscm$id), paste0("x", 1:p)])

# Markers expanded to row level: alpha^T z for readmission, beta^T z for death
M_re_jfm  <- drop(Z_jfm  %*% cv_jfm$alpha)[cr_jfm$data$id]
M_de_jfm  <- drop(Z_jfm  %*% cv_jfm$beta)[cr_jfm$data$id]
M_re_jscm <- drop(Z_jscm %*% cv_jscm$alpha)[cr_jscm$data$id]
M_de_jscm <- drop(Z_jscm %*% cv_jscm$beta)[cr_jscm$data$id]
```

```{r auc, cache = TRUE}
if (!requireNamespace("timeROC", quietly = TRUE))
  install.packages("timeROC")
library(survival)
library(timeROC)

# Evaluation grid: 20 points spanning the 10th-85th percentile of event times
.tgrid <- function(t_vec, status, n = 20) {
  t_ev <- t_vec[status > 0]
  seq(quantile(t_ev, 0.10), quantile(t_ev, 0.85), length.out = n)
}

t_jfm  <- .tgrid(cr_jfm$data$t.stop,  cr_jfm$status)
t_jscm <- .tgrid(cr_jscm$data$t.stop, cr_jscm$status)

# Readmission AUC: alpha^T z marker, cause = 1
roc_re_jfm <- timeROC(T = cr_jfm$data$t.stop, delta = cr_jfm$status,
                       marker = M_re_jfm, cause = 1, weighting = "marginal",
                       times = t_jfm, ROC = FALSE, iid = FALSE)
roc_re_jscm <- timeROC(T = cr_jscm$data$t.stop, delta = cr_jscm$status,
                        marker = M_re_jscm, cause = 1, weighting = "marginal",
                        times = t_jscm, ROC = FALSE, iid = FALSE)

# Death AUC: beta^T z marker, cause = 2
roc_de_jfm <- timeROC(T = cr_jfm$data$t.stop, delta = cr_jfm$status,
                       marker = M_de_jfm, cause = 2, weighting = "marginal",
                       times = t_jfm, ROC = FALSE, iid = FALSE)
roc_de_jscm <- timeROC(T = cr_jscm$data$t.stop, delta = cr_jscm$status,
                        marker = M_de_jscm, cause = 2, weighting = "marginal",
                        times = t_jscm, ROC = FALSE, iid = FALSE)
```

```{r auc-plot, fig.height = 5, fig.width = 8}
.get_auc <- function(roc, cause) {
  auc <- roc[[paste0("AUC_", cause)]]
  if (is.null(auc)) auc <- roc$AUC
  if (is.null(auc) || !is.numeric(auc)) return(rep(NA_real_, length(roc$times)))
  if (length(auc) == length(roc$times) + 1) auc <- auc[-1]
  as.numeric(auc)
}

old_par <- par(mfrow = c(1, 2), mar = c(4.5, 4, 3, 1))

plot(t_jfm, .get_auc(roc_re_jfm, 1), type = "l", lwd = 2, col = "steelblue",
     xlab = "Time", ylab = "AUC(t)", main = "JFM", ylim = c(0.4, 1))
lines(t_jfm, .get_auc(roc_de_jfm, 2), lwd = 2, col = "tomato", lty = 2)
abline(h = 0.5, lty = 3, col = "grey60")
legend("bottomleft", c("Readmission", "Death"),
       col = c("steelblue", "tomato"), lwd = 2, lty = c(1, 2),
       bty = "n", cex = 0.85)

plot(t_jscm, .get_auc(roc_re_jscm, 1), type = "l", lwd = 2, col = "steelblue",
     xlab = "Time", ylab = "AUC(t)", main = "JSCM", ylim = c(0.4, 1))
lines(t_jscm, .get_auc(roc_de_jscm, 2), lwd = 2, col = "tomato", lty = 2)
abline(h = 0.5, lty = 3, col = "grey60")
legend("bottomleft", c("Readmission", "Death"),
       col = c("steelblue", "tomato"), lwd = 2, lty = c(1, 2),
       bty = "n", cex = 0.85)

par(old_par)
```

---

## 11. References

Blanche, P., Dartigues, J.-F., and Jacqmin-Gadda, H. (2013).
Estimating and comparing time-dependent areas under receiver operating
characteristic curves for censored event times with competing risks.
*Statistics in Medicine*, **32**(30), 5381–5397.

Kalbfleisch, J. D., Schaubel, D. E., Ye, Y., and Gong, Q. (2013).
An estimating function approach to the analysis of recurrent and terminal
events. *Biometrics*, **69**(2), 366–374.

Xu, G., Chiou, S. H., Huang, C.-Y., Wang, M.-C., and Yan, J. (2017).
Joint scale-change models for recurrent events and failure time.
*Journal of the American Statistical Association*, **112**(518), 794–805.

Huo, L., Jiang, Z., Hou, J., and Huling, J. D. (2025). A stagewise
selection framework for joint models for semi-competing risk prediction.
*Manuscript*.
