---
title: "Power Calculation Using Max-Combo Tests"
author: "Kaifeng Lu"
date: "12/15/2021"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Power Calculation Using Max-Combo Tests}
  \usepackage[utf8]{inputenc}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
bibliography: references.bib
---

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

```{r setup}
library(lrstat)
```

This R Markdown document illustrates the power calculation using the
maximum of weighted log-rank statistics in a group sequential analysis
for the delayed effect example from @prior2020. The hazards in both arms
are 0.25 per month for the first 1.5 months, and the hazard of the
active arm drops to 0.125 per month thereafter, while the hazard of the
placebo arm remains at 0.25 per month. Assume that there is no
censoring. The enrollment rate is 25 patients per month, and the total
number of patients to enroll is 100. Suppose also that an interim
analysis occurs after observing 50 events and the final analysis takes
place after all 100 events have been observed. The conventional log-rank
test will be used for the interim analysis, and the maximum of the
standardized weighted log-rank test statistics of FH(0,0) and FH(0,1)
will be used at the final analysis. For a total one-sided 2.5%
significance level with an interim alpha of 0.0015, @prior2020 reports
that the resulting power is abut 73%. We now verify this through
analytic calculation and simulation.

First, we derive the critical values at the interim and final analyses.
For the interim analysis, only one log-rank test will be used, hence the
critical value is $u_1 = \Phi^{-1}(1-0.0015) = 2.968$. For the final
analysis, the critical value $u_2$ satisfies $$
  P_0(Z_{1,1} < u_1, \max(Z_{1,2}, Z_{2,2}) < u_2) = 1 - 0.025
$$ which is the same as $$
  P_0(Z_{1,1} < u_1, Z_{1,2} < u_2, Z_{2,2} < u_2) = 0.975 \; 
    (Eq. 1)
$$ Let $U_{FH(p,q),i}$ denote the numerator of the FH(p,q) weighted
log-rank test statistic at analysis $i$, and $V_{FH(p,q),i}$ its
variance. Then $W_{FH(p,q),i} = U_{FH(p,q),i}/{V_{FH(p,q),i}^{1/2}}$.
In addition, similar to @karrison2016, we can show that $$
  Cov(U_{FH(p_1,q_1),1}, U_{FH(p_2,q_2),2}) = 
  V_{FH\left(\frac{p_1+p_2}{2},\frac{q_1+q_2}{2}\right),1}
$$

First, we find the time when 50 and 99.9 events are expected to have
occurred.

```{r}
(time = caltime(nevents=c(50, 99.9), accrualIntensity=25,
                piecewiseSurvivalTime=c(0, 1.5), 
                lambda1=c(0.25, 0.125), lambda2=c(0.25, 0.25), 
                accrualDuration=4, followupTime=60))
```

Then, we obtain the the means and variances of weighted log-rank test
statistics at the interim and final analyses for relevant FH weights.

```{r}
(lr00 = lrstat(time=c(5.363, 50.324), accrualIntensity=25,
               piecewiseSurvivalTime=c(0, 1.5), 
               lambda1=c(0.25, 0.125), lambda2=c(0.25, 0.25), 
               accrualDuration=4, followupTime=60, 
               rho1=0, rho2=0, numSubintervals=10000))

(lr01 = lrstat(time=c(5.363, 50.324), accrualIntensity=25,
               piecewiseSurvivalTime=c(0, 1.5), 
               lambda1=c(0.25, 0.125), lambda2=c(0.25, 0.25), 
               accrualDuration=4, followupTime=60, 
               rho1=0, rho2=1, numSubintervals=10000))

(lr0h = lrstat(time=c(5.363, 50.324), accrualIntensity=25,
               piecewiseSurvivalTime=c(0, 1.5), 
               lambda1=c(0.25, 0.125), lambda2=c(0.25, 0.25), 
               accrualDuration=4, followupTime=60, 
               rho1=0, rho2=0.5, numSubintervals=10000))
```

It follows that the mean of $\mathbf{Z}=(Z_{1,1}, Z_{1,2}, Z_{2,2})$ is
$$
  \left(\frac{3.179}{\sqrt{12.464}}, \frac{10.545}{\sqrt{22.270}}, 
  \frac{6.608}{\sqrt{6.161}} \right) = (0.900, 2.234, 2.662)
$$ and the covariance matrix of $\mathbf{Z}$ is $$
  \left(\begin{array}{ccc} 1 & \sqrt{\frac{12.464}{22.270}} & \frac{3.241}{\sqrt{12.464\times 6.161}} \\
  \sqrt{\frac{12.464}{22.270}} & 1 & \frac{10.083}{\sqrt{22.270\times 6.161}} \\ 
  \frac{3.241}{\sqrt{12.464\times 6.161}}  & \frac{10.083}{\sqrt{22.270\times 6.161}} & 1 \end{array} \right) = \left(\begin{array}{ccc} 1 & 0.748 &  0.370 \\ 
  0.748 & 1 & 0.861 \\ 0.370 & 0.861 & 1 \end{array} \right)
$$ Now, we obtain the critical value $u_2$ by solving equation (1).

```{r}
library(mvtnorm)
mu = c(0.900, 2.234, 2.662)
sigma = matrix(c(1, 0.748, 0.370, 0.748, 1, 0.861, 0.370, 0.861, 1), 3, 3)
u1 = 2.968
alpha = 0.025
f <- function(u2, u1, sigma, alpha) {
  1 - pmvnorm(upper=c(u1, u2, u2), corr=sigma, algorithm="Miwa") - alpha
}
(u2 = uniroot(f, c(1,3), u1, sigma, alpha)$root)
```

The power can be estimated by plugging in the mean under the alternative
hypothesis.

```{r}
1 - pmvnorm(upper=c(u1, u2, u2), corr=sigma, mean=mu, algorithm="Miwa")
```

For the simulation study, we use very large critical values for the
FH(0,0) and FH(0,1) log-rank statistics at the interim and final
analyses for each iteration, and then construct the maximum combo test
statistic at the final analysis. Finally, we tally the rejections across
iterations to estimate the power. The same seed should be used to
produce identical simulated data.

```{r}
sim1 = lrsim(kMax = 2, informationRates = c(0.5, 1),
             criticalValues = c(6, 6),
             accrualIntensity = 25, 
             piecewiseSurvivalTime = c(0, 1.5),
             lambda1 = c(0.25, 0.125), lambda2 = c(0.25, 0.25),
             accrualDuration = 4,
             rho1 = 0, rho2 = 0,
             plannedEvents = c(50, 100), 
             maxNumberOfIterations = 10000,
             seed = 314159)

sim2 = lrsim(kMax = 2, informationRates = c(0.5, 1),
             criticalValues = c(6, 6),
             accrualIntensity = 25,
             piecewiseSurvivalTime = c(0, 1.5),
             lambda1 = c(0.25, 0.125), lambda2 = c(0.25, 0.25),
             accrualDuration = 4,
             rho1 = 0, rho2 = 1, 
             plannedEvents = c(50, 100),
             maxNumberOfIterations = 10000,
             seed = 314159)

w1max = subset(-sim1$sumdata$logRankStatistic, sim1$sumdata$stageNumber==1)

w2max = pmax(-sim1$sumdata$logRankStatistic, -sim2$sumdata$logRankStatistic) 
w2max = subset(w2max, sim1$sumdata$stageNumber==2)

mean((w1max > u1) | (w2max > u2))
```

The analytic method yielded a power of about 72%, while the simulation method 
yielded a power of about 73%, both are close to that reported by @prior2020.
