---
title: "Propensity Score-Integrated Survival Inference in Randomized Controlled Trials (RCTs) with Augmenting Control Arm"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Propensity Score-Integrated Survival Inference in Randomized Controlled Trials (RCTs) with Augmenting Control Arm}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r, eval=T, echo=FALSE}
suppressMessages(require(psrwe, quietly = TRUE))
org_digits <- options(digits = 3)
set.seed(1000)
```
<br>


## Introduction

In the **psrwe**, PS-integrated survival analyses
in randomized controlled trials (RCTs) with an augmented control arm
(Chen, et al., to be submitted)
are also implemented in three functions:

* `psrwe_survkm()` for treatment effect test (Com-Nougue, et al., 1993).
* `psrwe_survlrk()` for log-rank test (Klein and Moeschberger, 2003; Peto and Peto, 1972).
* `psrwe_survrmst()` for restricted mean survival time (RMST) test (Royston and Parmar, 2013; Uno, et al., 2014).

These tests are non-parametric approaches for comparing two treatments
with time-to-event endpoints.
Therefore, these tests are only implemented for RCTs with
augmenting control arm.

Similar to the approaches: PSPP (Wang, et al., 2019),
PSCL (Wang, et al., 2020), and PSKM (Chen, et al., 2022),
the PS-integrated study design functions, `psrwe_est()` and `psrwe_borrow()`,
below estimate the PS model, set borrowing parameters, and determine
discounting parameters for borrowing information
for a two-arm RCT with an augmented control arm from RWD.

```{r, eval=T, echo=TRUE}
data(ex_dta_rct)
dta_ps_rct <- psrwe_est(ex_dta_rct,
                        v_covs = paste("V", 1:7, sep = ""),
                        v_grp = "Group", cur_grp_level = "current",
                        v_arm = "Arm", ctl_arm_level = "control",
                        ps_method = "logistic", nstrata = 5,
                        stra_ctl_only = FALSE)
ps_bor_rct <- psrwe_borrow(dta_ps_rct, total_borrow = 30)
```
<br>


## PS-integrated treatment effect test

Similar to the single arm study example
(in **psrwe/demo/sec_4_4_ex.r** and `demo("sec_4_5_ex", package = "psrwe")`),
the code below evaluates a two-arm RCT.
The results show the treatment effect which is
the survival difference between the two arms at 1 year (365 days).

```{r, eval=T, echo=TRUE}
rst_km_rct <- psrwe_survkm(ps_bor_rct,
                           pred_tp = 365,
                           v_time = "Y_Surv",
                           v_event = "Status")
rst_km_rct
```

The estimated PSKM curves with confidence intervals are shown below.

```{r, echo=TRUE, fig.width=6, fig.height=5}
plot(rst_km_rct, xlim = c(0, 730))
```

The inference is based on the treatment effect
$S_{trt}(\tau) - S_{ctl}(\tau)$ at $\tau = 365$ days
where $S_{trt}$ and $S_{ctl}$ are the
survival probabilities of the treatment and control arms, respectively.
In other words, the example tests
$$
H_0: S_{trt}(\tau) - S_{ctl}(\tau) \leq 0 \quad \mbox{vs.} \quad
H_a: S_{trt}(\tau) - S_{ctl}(\tau) > 0 .
$$
The outcome analysis can be summarized below.
Note that this is a one-sided test.

```{r, eval=T, echo=TRUE}
oa_km_rct <- psrwe_outana(rst_km_rct, alternative = "greater")
oa_km_rct
```

The details of the estimates for each arm can be printed via the `print()`
function with the option `show_rct = TRUE`.

```{r, eval=T, echo=TRUE}
print(oa_km_rct, show_rct = TRUE)
```

As the **survival** package, the results of other time points can be also
predicted via the `summary()` with the option `pred_tps`.

```{r, eval=T, echo=TRUE}
summary(oa_km_rct, pred_tps = c(180, 365))
```
<br>


## PS-integrated log-rank test

The log-rank test is another way to compare two treatments of
time-to-event endpoint.
Similar to the PSKM for the two-arm test above, the function
`psrwe_survlrk()` computes the statistic for each distinctive time point
beased on the observed data, then it returns all necessary results for
the downstream analyses, such as tests and confidence intervals.

```{r, eval=T, echo=TRUE}
rst_lrk <- psrwe_survlrk(ps_bor_rct,
                         pred_tp = 365,
                         v_time = "Y_Surv",
                         v_event = "Status")
rst_lrk
```

The inference is based on the log-rank method to test whether two survival
distributions are different from each other.
The example tests
$$
H_0: S_{trt}(t) = S_{ctl}(t) \quad \mbox{vs.} \quad
H_a: S_{trt}(t) \neq S_{ctl}(t) 0
$$
for all $t \leq \tau$ where $\tau = 365$ days.
The outcome analysis can be summarized below.

```{r, eval=T, echo=TRUE}
oa_lrk <- psrwe_outana(rst_lrk)
oa_lrk
```

The details of the estimates for each arm can be printed via the `print()`
function with the option `show_rct = TRUE`.

```{r, eval=T, echo=TRUE}
print(oa_lrk, show_details = TRUE)
```

As the **survival** package, the results of other time points can be also
predicted via the `summary()` with the option `pred_tps`.

```{r, eval=T, echo=TRUE}
summary(oa_lrk, pred_tps = c(180, 365))
```
<br>


## PS-integrated restricted mean survival time (RMST) test

The restricted means survival time (RMST) tests whether areas under
two survival distributions (AUC) are different from each other.
Similar to the log-rank test above, the function
`psrwe_survrmst()` computes the statistic for each distinctive time point
beased on the observed data, then it returns all necessary results for
the downstream analyses, such as tests and confidence intervals.

```{r, eval=T, echo=TRUE}
rst_rmst <- psrwe_survrmst(ps_bor_rct,
                           pred_tp = 365,
                           v_time = "Y_Surv",
                           v_event = "Status")
rst_rmst
```

The inference is based on comparing whether AUCs are different from each other.
The example tests
$$
H_0: \int_0^{\tau} S_{trt}(t) dt = \int_0^{\tau} S_{ctl}(t) dt
\quad \mbox{vs.} \quad
H_a: \int_0^{\tau} S_{trt}(t) dt \neq \int_0^{\tau} S_{ctl}(t) dt
$$
where $\tau = 365$ days.
The outcome analysis can be summarized below.
Note that this is a two-sided test.

```{r, eval=T, echo=TRUE}
oa_rmst <- psrwe_outana(rst_rmst)
oa_rmst
```

The details of the estimates for each arm can be printed via the `print()`
function with the option `show_rct = TRUE`.

```{r, eval=T, echo=TRUE}
print(oa_rmst, show_details = TRUE)
```

As the **survival** package, the results of other time points can be also
predicted via the `summary()` with the option `pred_tps`.

```{r, eval=T, echo=TRUE}
summary(oa_rmst, pred_tps = c(180, 365))
```
<br>


## Demo examples

The scripts in
"**psrwe/demo/sec_4_5_ex.r**",
"**psrwe/demo/sec_4_6_ex.r**", and
"**psrwe/demo/sec_4_7_ex.r**"
source files have
the full examples for the PS-integrated survival analyses, which can be run via
the `demo("sec_4_5_ex", package = "psrwe")`,
`demo("sec_4_6_ex", package = "psrwe")`, and
`demo("sec_4_7_ex", package = "psrwe")`, respectively.

Two Jackknife standard errors are also demonstrated for each test method.
Note that Jackknife standard errors may take a while to finish.
<br>


## References

1. Chen, W.-C., Lu, N., Wang, C., Li, H., Song, C., Tiwari, R., Xu, Y., and
Yue, L.Q. (to be submitted).
Propensity Score-Integrated Statistical Tests for Survival Analysis:
Leveraging External Evidence for Augmenting the Control Arm of a
Randomized Controlled Trial.

2. Chen, W.-C., Lu, N., Wang, C., Li, H., Song, C., Tiwari, R., Xu, Y., and
Yue, L.Q. (2022).
Propensity Score-Integrated Approach to Survival Analysis: Leveraging External
Evidence in Single-Arm Studies.
Journal of Biopharmaceutical Statistics, 32(3), 400-413.

3. Com-Nougue, C., Rodary, C. and Patte, C. (1993).
How to establish equivalence when data are censored: A randomized trial of
treatments for B non-Hodgkin lymphoma.
Statist. Med., Volume 12, pp. 1353-1364.

4. Klein, J. and Moeschberger, M. (2003).
Survival Analysis: Techniques for Censored and Truncated Data.
2nd ed. New York: Springer.

5. Peto, R. and Peto, J. (1972).
Asymptotically Efficient Rank Invariant Test Procedures.
Journal of the Royal Statistical Society, Series A, 135(2), 185-207.

6. Royston, P. and Parmar, M. K. (2013).
Restricted mean survival time: an alternative to the hazard ratio for the
design and analysis of randomized trials with a time-to-event outcome.
BMC Med Res Methodol, 13(152).

7. Uno, H., et al., (2014).
Moving beyond the hazard ratio in quantifying the between-group difference
in survival analysis.
Journal of clinical oncology, Volume 32, 2380-2385.

8. Wang, C., Li, H., Chen, W. C., Lu, N., Tiwari, R., Xu, Y., and Yue, L.Q.
(2019).
Propensity score-integrated power prior approach for incorporating
real-world evidence in single-arm clinical studies.
Journal of Biopharmaceutical Statistics, 29(5), 731-748.

9. Wang, C., Lu, N., Chen, W. C., Li, H., Tiwari, R., Xu, Y., and Yue, L.Q.
(2020).
Propensity score-integrated composite likelihood approach for
incorporating real-world evidence in single-arm clinical studies.
Journal of Biopharmaceutical Statistics, 30(3), 495-507.

<br>

```{r, eval=T, echo=FALSE}
## Reset to user's options.
options(org_digits)
```
<br>