---
title: "Scenario Guide"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Scenario Guide}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

Use this guide to pick a module and test for your study design.

## Implemented scenarios (48 tests)

```{r counts, echo=FALSE}
tests <- ggpower_tests()
knitr::kable(as.data.frame(table(tests$module)), col.names = c("Module", "Tests"))
```

### Power Workspace (30 tests)

Classical families: t, F, chi-square, exact proportions, z correlations/GLM,
Wilcoxon rank tests.

### Biomarker Discovery (7 tests)

ROC/AUC, diagnostic accuracy, log-rank, Cox, FDR screening, differential expression.

### Clinical Trials (11 tests)

Superiority, NI, equivalence (TOST), Simon two-stage, cluster RCT, multi-arm ANOVA,
Poisson counts, survival.

## Decision table

| Study question | Module | Example test |
|----------------|--------|--------------|
| Two-group mean difference | Power Workspace or Clinical | `t_two_sample`, `rct_superiority_continuous` |
| Paired / pre-post | Power Workspace | `t_paired` |
| One-way ANOVA | Power Workspace or Clinical | `f_anova_one_way`, `multi_arm_superiority` |
| Multiple regression $R^2$ | Power Workspace | `f_mreg_omnibus` |
| Two proportions (Fisher) | Power Workspace or Clinical | `exact_fisher`, `rct_superiority_binary` |
| McNemar paired proportions | Power Workspace | `exact_mcnemar` |
| Correlation difference | Power Workspace | `z_corr_independent` |
| Logistic / Poisson GLM | Power Workspace or Clinical | `z_logistic`, `count_endpoint_poisson` |
| Biomarker AUC | Biomarker | `roc_auc_one` |
| Diagnostic sens/spec | Biomarker | `diagnostic_acc` |
| Survival / HR | Biomarker or Clinical | `cox_regression`, `survival_pmu` |
| Non-inferiority trial | Clinical | `rct_noninferiority_continuous` |
| Bioequivalence (TOST) | Clinical | `rct_equivalence_continuous` |
| Oncology Phase II Simon | Clinical | `simon_two_stage` |
| Cluster RCT | Clinical | `cluster_rct` |
| Omics screening + FDR | Biomarker | `discovery_fdr` |

## Not yet implemented

Document for planning only — use external tools or custom simulation:

- Group sequential / interim analyses
- Crossover or repeated-measures RCT
- Dunnett pairwise multi-arm comparisons
- Paired / correlated ROC comparison
- Partial AUC, NPV/PPV as primary endpoints
- Time-varying accrual and dropout in survival
- Bayesian assurance
- Simon optimal design search (minimax / optimal)

## Related

- [Choosing a power analysis](choosing-a-power-analysis.html)
- [Support matrix](support-matrix.html)
- [Formula reference](https://yaoxiangli.github.io/ggpower/articles/formula-reference.html) (pkgdown only)