---
title: "OLE Analysis Workflow"
author: "Lei Shi, Matthew Secrest"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{OLE Analysis Workflow}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, message=FALSE, warning=FALSE, include=FALSE}
library(rdborrow)
```


## OLE phase

This vignette demonstrates the open-label extension (OLE) phase analysis
workflow using the difference-in-differences (DID) and synthetic control
method (SCM) estimators proposed in
[Zhou et al. (2024)](https://doi.org/10.1080/10543406.2024.2444222)
for estimating long-term treatment effects when the control group
switches to treatment.

The `SyntheticData` dataset has outcomes `y1`, `y2`, `y3`, `y4` measured
at four time points, and `T_cross = 2`. This means `y1` and `y2` are
from the placebo-controlled phase (Period I) and `y3` and `y4` are from
the open-label extension (Period II). `T_cross` is the last column index
of Period I in `outcome_col_name`.

```{r}
head(SyntheticData[, c("A", "S", "y1", "y2", "y3", "y4")])
```

### 1 DID methods

#### 1.1 DID-EC-IPW
```{r message=FALSE, warning=FALSE}
method <- did_ec_ipw(
  ps_formula = "S ~ x1 + x2 + x3 + x4 + x5",
  trt_formula = "A ~ x1 + x2 + x3 + x4 + x5",
  bootstrap = 50
)

analysis <- setup_analysis_OLE(
  data = SyntheticData,
  trial_status_col_name = "S",
  treatment_col_name = "A",
  outcome_col_name = c("y1", "y2", "y3", "y4"),
  covariates_col_name = c("x1", "x2", "x3", "x4", "x5"),
  T_cross = 2,
  method_OLE_obj = method
)

run_analysis(analysis)
```

#### 1.2 DID-EC-AIPW
```{r message=FALSE, warning=FALSE}
model_forms <- c(
  "y1 ~ x1 + x2 + x3 + x4 + x5",
  "y2 ~ x1 + x2 + x3 + x4 + x5",
  "y3 ~ x1 + x2 + x3 + x4 + x5",
  "y4 ~ x1 + x2 + x3 + x4 + x5"
)

method <- did_ec_aipw(
  ps_formula = "S ~ x1 + x2 + x3 + x4 + x5",
  trt_formula = "A ~ x1 + x2 + x3 + x4 + x5",
  outcome_formula = model_forms,
  bootstrap = 50
)

analysis <- setup_analysis_OLE(
  data = SyntheticData,
  trial_status_col_name = "S",
  treatment_col_name = "A",
  outcome_col_name = c("y1", "y2", "y3", "y4"),
  covariates_col_name = c("x1", "x2", "x3", "x4", "x5"),
  T_cross = 2,
  method_OLE_obj = method
)

run_analysis(analysis)
```

#### 1.3 DID-EC-OR
```{r message=FALSE, warning=FALSE}
model_forms <- c(
  "y1 ~ x1 + x2 + x3 + x4 + x5",
  "y2 ~ x1 + x2 + x3 + x4 + x5",
  "y3 ~ x1 + x2 + x3 + x4 + x5",
  "y4 ~ x1 + x2 + x3 + x4 + x5"
)

method <- did_ec_or(
  outcome_formula_ext = model_forms,
  outcome_formula_rct_ctrl = model_forms,
  outcome_formula_rct_trt = model_forms,
  bootstrap = 50
)

analysis <- setup_analysis_OLE(
  data = SyntheticData,
  trial_status_col_name = "S",
  treatment_col_name = "A",
  outcome_col_name = c("y1", "y2", "y3", "y4"),
  covariates_col_name = c("x1", "x2", "x3", "x4", "x5"),
  T_cross = 2,
  method_OLE_obj = method
)

run_analysis(analysis)
```


### 2 Synthetic control method
```{r message=FALSE, warning=FALSE}
method <- scm(
  lambda_min = 0,
  lambda_max = 1e-3,
  nlambda = 2,
  bootstrap = 3,
  bootstrap_ci_type = "perc"
)

analysis <- setup_analysis_OLE(
  data = SyntheticData,
  trial_status_col_name = "S",
  treatment_col_name = "A",
  outcome_col_name = c("y1", "y2", "y3", "y4"),
  covariates_col_name = c("x1", "x2", "x3", "x4", "x5"),
  T_cross = 2,
  method_OLE_obj = method
)

run_analysis(analysis)
```

## References

- Zhou X, Pang H, Drake C, Burger HU, Zhu J (2024). "Estimating treatment effect in randomized trial after control to treatment crossover using external controls." *Journal of Biopharmaceutical Statistics*. doi: [10.1080/10543406.2024.2444222](https://doi.org/10.1080/10543406.2024.2444222).
- Shi L, Pang H, Chen C, Zhu J (2025). "rdborrow: an R package for causal inference incorporating external controls in randomized controlled trials with longitudinal outcomes." *Journal of Biopharmaceutical Statistics*, 35(6), 1043-1066. doi: [10.1080/10543406.2025.2489283](https://doi.org/10.1080/10543406.2025.2489283).