---
title: "Introduction to splineCox"
author: "Ren Teranishi"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to splineCox}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Introduction

The `splineCox` package provides functions for fitting spline-based Cox regression models.
These models allow for flexible baseline hazard shapes and efficient model selection
based on log-likelihood.

# Loading the Package

```{r setup, echo = TRUE}
library(splineCox)
library(joint.Cox)  # Required for example data
```

# Example Dataset

The `dataOvarian` dataset from the `joint.Cox` package contains time-to-event data, event indicators, and covariates for ovarian cancer patients.

```{r example-data}
# Load the dataset
data(dataOvarian)

# Display the first few rows
head(dataOvarian)
```

# Fitting the Model

We fit a spline-based Cox regression model using three baseline hazard shapes: "constant", "increase", and "decrease".

```{r fit-model}
# Define variables
t.event <- dataOvarian$t.event
event <- dataOvarian$event
Z <- dataOvarian$CXCL12
M <- c("constant", "increase", "decrease")

# Fit the model
reg2 <- splineCox.reg2(t.event, event, Z, model = M)

# Display the results
print(reg2)
```

# Interpreting Results

The output of the model includes:
- The best-fitting baseline hazard shape.
- Estimates for the regression coefficients (`beta`) and the baseline hazard scale parameter (`gamma`).
- Log-likelihood, AIC and BIC for selected model.
- Log-likelihoods of other models.

Below are the results from the example:

```{r display-results}
# Print a summary of the results
print(reg2)