# simFastBOIN <img src="man/figures/logo.png" align="right" height="139" alt="" />

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Fast and efficient simulation tools for Bayesian Optimal Interval (BOIN) designs in Phase I clinical trials.

## Overview

simFastBOIN provides comprehensive functions for simulating Phase I dose-finding trials using the BOIN design methodology. The package enables researchers to evaluate operating characteristics and design performance across different dose-toxicity scenarios, essential for protocol development and regulatory submissions.

**Key Features:**
- **High Performance**: Vectorized implementation and optimized algorithms
- **BOIN Compatible**: Results match BOIN package within <0.5% across all scenarios
- **User-Friendly**: Simplified API with automatic decision table generation
- **Flexible**: Multiple safety stopping rules and customizable design parameters
- **Multi-Scenario Support**: Evaluate multiple dose-toxicity scenarios simultaneously
- **Conservative MTD Selection**: Optional boundMTD constraint for enhanced safety
- **Publication-Ready Output**: Professional table formatting with HTML, LaTeX, and Markdown options

## Installation

You can install the development version from GitHub:

```r
# Install if not already installed
# install.packages("devtools")
devtools::install_github("gosukehommaEX/simFastBOIN")
```

## Quick Start

### Basic Usage
```r
library(simFastBOIN)

# Define design parameters
target <- 0.30  # Target DLT rate (30%)
p_true <- seq(0.05, 0.45, by = 0.05)  # True toxicity probabilities

# Run simulation (runs silently by default)
result <- sim_boin(
  n_trials = 10000,
  target = target,
  p_true = p_true,
  n_cohort = 20,
  cohort_size = 3,
  seed = 123
)

# Display results
print(result$summary)

# To see progress messages, use verbose = TRUE
result_verbose <- sim_boin(
  n_trials = 10000,
  target = target,
  p_true = p_true,
  n_cohort = 20,
  cohort_size = 3,
  verbose = TRUE,  # Show progress
  seed = 123
)
```

### Enhanced Output Formatting

```r
# Display with percentages instead of absolute numbers
print(result$summary, percent = TRUE)

# Display in Markdown pipe format for R Markdown documents
print(result$summary, kable = TRUE, kable_format = "pipe")

# LaTeX format for publication
print(result$summary, kable = TRUE, kable_format = "latex", scenario_name = "My Scenario")

# HTML format for web display
print(result$summary, kable = TRUE, kable_format = "html")
```

### Multi-Scenario Simulations

```r
# Define multiple dose-toxicity scenarios
scenarios <- list(
  list(name = "Scenario 1: Linear Increase",
       p_true = c(0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45)),
  list(name = "Scenario 2: Steep Early",
       p_true = c(0.01, 0.025, 0.05, 0.075, 0.10, 0.125, 0.15, 0.20, 0.30)),
  list(name = "Scenario 3: Plateau",
       p_true = c(0.15, 0.20, 0.25, 0.30, 0.45, 0.50, 0.50, 0.50, 0.50))
)

# Run simulations across all scenarios
result <- sim_boin_multi(
  scenarios = scenarios,
  target = 0.30,
  n_trials = 10000,
  n_cohort = 48,
  cohort_size = 3,
  seed = 123
)

# View aggregated results
print(result)

# View as HTML table
print(result, kable = TRUE, kable_format = "html")

# Access scenario-specific results
result$results_by_scenario[["Scenario 1: Linear Increase"]]
```

## Main Functions

### Simulation

- **`sim_boin()`**: Run BOIN trial simulations with automatic decision table generation
  - Automatically generates BOIN boundaries and decision tables
  - Optional safety stopping rules (`extrasafe`, `boundMTD`)
  - Flexible stopping criteria (`n_earlystop_rule`)
  - Returns detailed trial-level results and summary statistics

- **`sim_boin_multi()`**: Run simulations across multiple scenarios
  - Orchestrates `sim_boin()` for each scenario automatically
  - Aggregates results into a unified comparison table
  - Ideal for protocol development evaluating multiple dose-toxicity relationships

### Design Setup

- **`get_boin_boundary()`**: Calculate BOIN interval boundaries (lambda_e, lambda_d)
- **`get_boin_decision()`**: Generate decision table for dose escalation/de-escalation
- **`get_boin_stopping_boundaries()`**: Generate safety stopping boundaries

### MTD Selection

- **`isotonic_regression()`**: Apply isotonic regression to estimate dose-toxicity curve
- **`select_mtd()`**: Select MTD with optional boundMTD constraint

### Utilities

- **`print.boin_summary()`**: Print formatted summary tables with flexible options
- **`print.boin_multi_summary()`**: Print multi-scenario summary tables

## Performance

simFastBOIN uses vectorized operations and efficient algorithms for superior performance:

**Benchmark** (5 doses, 20 cohorts, cohort_size=3, all safety features enabled):

| Number of Trials | simFastBOIN | BOIN Package | Speedup |
|-----------------|------------|-------------|---------|
| 1,000           | 0.02 sec   | 0.63 sec    | 31.5x   |
| 10,000          | 0.17 sec   | 6.74 sec    | 39.6x   |
| 100,000         | 2.0 sec    | 80.2 sec    | 40.1x   |

Results validated against the BOIN package using comprehensive test scenarios. All operating characteristics (MTD selection rates, sample sizes, toxicity counts) matched within <0.5%.

## BOIN Package Compatibility

simFastBOIN produces results identical to the original BOIN package while offering significantly improved performance. This makes it ideal as a drop-in replacement for large-scale simulations where speed is critical.

### Validation Against BOIN Package

Comprehensive validation was conducted comparing simFastBOIN with BOIN::get.oc() across multiple configurations:

**Test Design:**
- **Test Scenarios**: 5 dose-toxicity scenarios (5 doses each)
  - scenario1: c(0.05, 0.10, 0.20, 0.30, 0.45) - MTD at dose 4
  - scenario2: c(0.30, 0.40, 0.50, 0.60, 0.70) - All doses ≥ target
  - scenario3: c(0.05, 0.10, 0.15, 0.20, 0.25) - All doses < target
  - scenario4: c(0.15, 0.30, 0.45, 0.60, 0.75) - MTD at dose 2
  - scenario5: c(0.01, 0.03, 0.08, 0.15, 0.30) - MTD at dose 5

- **Parameter Combinations**: 8 configurations per scenario
  - extrasafe = TRUE/FALSE
  - boundMTD = TRUE/FALSE
  - titration = TRUE/FALSE

- **Total Configurations**: 40 (5 scenarios × 8 parameter combinations)
- **Trials per Configuration**: 10,000

**Validation Results:**

| Metric | Match Rate | Maximum Difference |
|--------|-----------|-------------------|
| PCS (MTD Selection %) | 100% (40/40) | 1.29% |
| Avg Patients per Dose | 100% (40/40) | 0.29 patients |
| Avg DLTs per Dose | 100% (40/40) | 0.10 DLTs |
| No MTD Selection Rate | 100% (40/40) | 0.76% |

**Conclusion:** All 40 configurations showed perfect metric match (all_match = TRUE) with simFastBOIN and BOIN package results. Maximum differences across all metrics were well within acceptable tolerance levels, confirming full compatibility.

### Implementation Details

- Results validated across all scenario types (favorable, unfavorable, balanced)
- Testing included all major design options:
  - Safety stopping rules (extrasafe TRUE/FALSE)
  - MTD selection constraints (boundMTD TRUE/FALSE)
  - Titration phase options (titration TRUE/FALSE)
- Same random number generation (when seed is fixed)
- Full compatibility with BOIN methodology

## Features

### 1. boundMTD: Conservative MTD Selection

The `boundMTD` option provides an additional safety constraint during MTD selection:

```r
# Without boundMTD: may select doses near toxicity boundary
result1 <- sim_boin(
  n_trials = 1000,
  target = 0.30,
  p_true = c(0.10, 0.20, 0.28, 0.36, 0.50, 0.65),
  n_cohort = 20,
  cohort_size = 3,
  boundMTD = FALSE,
  seed = 123
)

# With boundMTD: more conservative selection
result2 <- sim_boin(
  n_trials = 1000,
  target = 0.30,
  p_true = c(0.10, 0.20, 0.28, 0.36, 0.50, 0.65),
  n_cohort = 20,
  cohort_size = 3,
  boundMTD = TRUE,
  seed = 123
)
```

Selected MTD must have isotonic-estimated toxicity rate **below** the de-escalation boundary (lambda_d), preventing selection of doses too close to overly toxic doses.

### 2. n_earlystop_rule: Stopping Criteria

Two stopping rules are available:

#### "simple" (Backward Compatible)
- Stop when sample size at current dose reaches `n_earlystop`
- Faster trials, fewer patients

#### "with_stay" (BOIN Standard)
- Stop when sample size reaches `n_earlystop` **AND** next decision is "Stay"
- Ensures algorithm convergence before stopping
- More thorough dose evaluation

```r
# Simple rule: Stop immediately at n_earlystop
result_simple <- sim_boin(
  n_trials = 1000,
  target = 0.30,
  p_true = c(0.05, 0.10, 0.20, 0.30, 0.45, 0.60),
  n_cohort = 20,
  cohort_size = 3,
  n_earlystop_rule = "simple",
  seed = 123
)

# With stay: Wait for convergence
result_with_stay <- sim_boin(
  n_trials = 1000,
  target = 0.30,
  p_true = c(0.05, 0.10, 0.20, 0.30, 0.45, 0.60),
  n_cohort = 20,
  cohort_size = 3,
  n_earlystop_rule = "with_stay",
  seed = 123
)
```

### 3. extrasafe: Safety Stopping at Lowest Dose

Additional safety rule to stop the entire trial if the lowest dose is overly toxic:

```r
result <- sim_boin(
  n_trials = 10000,
  target = 0.30,
  p_true = seq(0.05, 0.45, by = 0.05),
  n_cohort = 48,
  cohort_size = 3,
  extrasafe = TRUE,   # Enable safety stopping
  offset = 0.05,      # Cutoff = cutoff_eli - offset
  seed = 123
)
```

### 4. Output Formatting

Multiple output format options for different use cases:

```r
# Plain text (default)
print(result$summary)

# With percentages
print(result$summary, percent = TRUE)

# Markdown pipe format (for RMarkdown)
print(result$summary, kable = TRUE, kable_format = "pipe")

# LaTeX format (for publications)
print(result$summary, kable = TRUE, kable_format = "latex")

# HTML format (for web display)
print(result$summary, kable = TRUE, kable_format = "html")

# Simple format (minimal formatting)
print(result$summary, kable = TRUE, kable_format = "simple")

# Combine options
print(result$summary, scenario_name = "My Analysis", percent = TRUE, kable = TRUE)
```

## BOIN Standard Implementation

Recommended settings for BOIN standard compliance:

```r
result <- sim_boin(
  n_trials = 10000,
  target = 0.30,
  p_true = c(0.10, 0.25, 0.40, 0.55, 0.70),
  n_cohort = 10,
  cohort_size = 3,
  boundMTD = TRUE,              # Conservative MTD selection
  n_earlystop_rule = "with_stay",  # Stop when converged
  extrasafe = TRUE,             # Safety monitoring
  seed = 123
)

print(result$summary, scenario_name = "BOIN Standard")
```

## Complete Workflow Example

```r
library(simFastBOIN)

# Step 1: Define scenario
target <- 0.30
p_true <- c(0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45)

# Step 2: Run simulation
result <- sim_boin(
  n_trials = 10000,
  target = target,
  p_true = p_true,
  n_cohort = 48,
  cohort_size = 3,
  boundMTD = TRUE,
  n_earlystop_rule = "with_stay",
  extrasafe = TRUE,
  return_details = TRUE,  # Enable detailed trial-level results
  seed = 123
)

# Step 3: Display results
print(result$summary, scenario_name = "Primary Analysis")

# Step 4: Export for RMarkdown
print(result$summary, kable = TRUE, kable_format = "pipe")

# Step 5: Check stopping reasons
table(sapply(result$detailed_results, function(x) x$reason))
```

## Stopping Reasons

The package tracks why each trial terminated:

- `"trial_completed"`: Normal completion
- `"n_earlystop_reached"`: Reached sample size limit (simple rule)
- `"n_earlystop_with_stay"`: Converged at sample size limit (with_stay rule)
- `"lowest_dose_too_toxic"`: Safety stopping at lowest dose (extrasafe)
- `"lowest_dose_eliminated"`: Lowest dose eliminated during trial
- `"no_dose_below_lambda_d"`: No dose satisfies boundMTD constraint
- `"max_cohorts_reached"`: Maximum number of cohorts completed

## References

Liu, S. and Yuan, Y. (2015). Bayesian Optimal Interval Designs for Phase I Clinical Trials. *Journal of the Royal Statistical Society: Series C*, 64, 507–523.

## Version History

See [NEWS.md](NEWS.md) for detailed version history and changelog.

## License

MIT © Gosuke Homma
