---
title: "Core Conversions: Rates, Odds, and Time Rescaling"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Core Conversions: Rates, Odds, and Time Rescaling}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

## Overview

Health economic models require transition **probabilities**, but clinical literature often reports **rates** or **odds**. These are different quantities and cannot be used interchangeably. This vignette explains the conversions and demonstrates them with realistic scenarios.

## Tutorial 1: Rate to Probability

### The Scenario  --  Anticoagulant Safety (RE-LY Trial)

You are building a Markov model comparing Dabigatran vs Warfarin for atrial fibrillation. The RE-LY trial (Connolly et al., NEJM 2009) reports the incidence rate of major bleeding as:

> **3.36 events per 100 patient-years** in the Warfarin arm

### Why Simple Division is Wrong

Dividing 3.36 by 100 gives 0.0336. But this ignores the continuous nature of risk  --  patients who bleed early in the year are removed from the at-risk pool, meaning the remaining patients face a slightly different risk.

### The Formula

$$p = 1 - e^{-rt}$$

where $r$ is the instantaneous rate and $t$ is the time horizon.

### Worked Example

```{r}
# RE-LY trial: Warfarin arm major bleeding
rate_per_100 <- 3.36
r <- rate_per_100 / 100   # Convert to per-person rate
t <- 1                     # 1-year model cycle

p <- 1 - exp(-r * t)
cat("Rate (per person-year):", r, "\n")
cat("Annual probability:", round(p, 5), "\n")
cat("Naive division would give:", r, "(overestimates by", round((r - p)/p * 100, 2), "%)\n")
```

### In ParCC

1. Navigate to **Converters > Rate <-> Probability**
2. Input Rate = `3.36`, Multiplier = `Per 100`
3. Time = `1`
4. Result: `0.03304`

## Tutorial 2: Time Rescaling

### The Scenario  --  UKPDS Risk Engine

You are building a Diabetes model with **1-year cycles**. The **UKPDS Risk Engine** (Clarke et al., Diabetologia 2004) predicts the 10-year probability of coronary heart disease as **20%** for a 55-year-old male with HbA1c 8%.

### Why Simple Division is Wrong

Dividing 0.20 by 10 gives 0.02 (2% per year). But risk compounds: if you survive Year 1, you face risk again in Year 2. The correct conversion accounts for this compounding.

### The Formula

$$p_{new} = 1 - (1 - p_{old})^{t_{new}/t_{old}}$$

### Worked Example

```{r}
p_10yr <- 0.20
t_old <- 10
t_new <- 1

p_1yr <- 1 - (1 - p_10yr)^(t_new / t_old)
cat("10-year probability:", p_10yr, "\n")
cat("Correct 1-year probability:", round(p_1yr, 5), "\n")
cat("Naive (divide by 10):", p_10yr / 10, "\n")
cat("Correct value is", round((p_1yr - 0.02)/0.02 * 100, 1), "% higher\n")
```

### In ParCC

1. Navigate to **Converters > Time Rescaling**
2. Input Probability = `0.20`, Original Time = `10 Years`
3. New Time = `1 Year`
4. Result: `0.02206`

## Tutorial 3: Odds to Probability

### The Scenario  --  Logistic Regression Output

A logistic regression predicting post-surgical infection reports an **odds ratio of 2.5** for patients with diabetes (vs no diabetes). The baseline infection probability (no diabetes) is 8%.

### The Conversion

$$p = \frac{\text{Odds}}{1 + \text{Odds}}$$

### Worked Example

```{r}
p_baseline <- 0.08
odds_baseline <- p_baseline / (1 - p_baseline)
odds_diabetes <- odds_baseline * 2.5
p_diabetes <- odds_diabetes / (1 + odds_diabetes)

cat("Baseline odds:", round(odds_baseline, 4), "\n")
cat("Diabetes odds (OR=2.5):", round(odds_diabetes, 4), "\n")
cat("Diabetes probability:", round(p_diabetes, 4), "\n")
```

## References

- Sonnenberg FA, Beck JR. Markov models in medical decision making: a practical guide. *Med Decis Making*. 1993;13(4):322-338.
- Fleurence RL, Hollenbeak CS. Rates and probabilities in economic modelling. *Pharmacoeconomics*. 2007;25(1):3-12.
- Connolly SJ, et al. Dabigatran versus warfarin in patients with atrial fibrillation. *N Engl J Med*. 2009;361(12):1139-1151.
- Clarke PM, et al. A model to estimate the lifetime health outcomes of patients with Type 2 diabetes. *Diabetologia*. 2004;47(10):1747-1759.