---
title: "Sample size calculation for standard win ratio test"
author: "Lu Mao (lmao@biostat.wisc.edu)"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Sample size calculation for standard win ratio test}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## INTRODUCTION
This vignette demonstrates the use of the `WR` package in
sample size calculation for standard win ratio test of death and nonfatal event
using component-wise hazard ratios as effect size (Mao et al., 2021, *Biometrics*).
### Data and the test
Let $D^{(a)}$ denote the survival time and $T^{(a)}$ the first nonfatal event time
of a patient in group $a$,
where $a=1$ indicates the active treatment and $a=0$ indicates the control.
Likewise, use $C^{(a)}$ to denote the independent censoring time.
In the standard win ratio of Pocock et al. (2012), the "win" indicator at time $t$
can be written as
\[\mathcal W(D^{(a)},T^{(a)}; D^{(1-a)},T^{(1-a)})(t)=
I(D^{(1-a)}<D^{(a)}\wedge t)+I(D^{(a)}\wedge D^{(1-a)}>t, T^{(1-a)}<T^{(a)}\wedge t),\]
where $b\wedge c=\min(b, c)$.
So the winner goes to the longer overall survivor or, if both survive
past $t$, the longer event-free survivor.
Tweaks to this rule to incorporate recurrent event are considered in Mao et al. (2022).
Using this notation, Pocock's win ratio statistic becomes
\begin{equation}\tag{1}
S_n=\frac{\sum_{i=1}^{n_1}\sum_{j=1}^{n_0}\mathcal W(D_i^{(1)},T_i^{(1)}; D_j^{(0)},T_j^{(0)})
(C_i^{(1)}\wedge C_j^{(0)})}
{\sum_{i=1}^{n_1}\sum_{j=1}^{n_0}\mathcal W(D_j^{(0)},T_j^{(0)}; D_i^{(1)},T_i^{(1)})
(C_i^{(1)}\wedge C_j^{(0)})},
\end{equation}
where the $(D_i^{(a)}, T_i^{(a)}, C_i^{(a)})$ $(i=1,\ldots, n_a)$ are a random $n_a$-sample
of $(D^{(a)}, T^{(a)}, C^{(a)})$ $(a=1, 0)$
(the right hand side of (1) is indeed computable with censored data).
A two-sided level-$\alpha$ win test of group difference rejects the null if
$n^{1/2}|\log S_n|/\widehat\sigma_n>z_{1-\alpha/2}$,
where $n=n_1+n_0$, $\widehat\sigma_n^2$ is
a consistent variance estimator, and $z_{1-\alpha/2}$ is the $(1-\alpha/2)$th quantile of the standard
normal distribution. Mao (2019) showed that this test is powerful in large samples
if the treatment stochastically delays death and the nonfatal event jointly.
### Methods for sample size calculation
To simplify sample size calculation, we posit
a Gumbel--Hougaard copula model with marginal proportional hazards structure for $D^{(a)}$ and $T^{(a)}$:
\begin{equation}\tag{2}
{P}(D^{(a)}>s, T^{(a)}>t) =\exp\left(-\left[\left\{\exp(a\xi_1)\lambda_Ds\right\}^\kappa+
\left\{\exp(a\xi_2)\lambda_Ht\right\}^\kappa\right]^{1/\kappa}\right),
\end{equation}
where $\lambda_D$ and $\lambda_H$ are the baseline hazards for death and the nonfatal
event, respectively, and $\kappa\geq 1$ controls their correlation (with Kendall's concordance
$1-\kappa^{-1}$). The parameters $\boldsymbol\xi:=(\xi_1,\xi_2)^{\rm T}$
are the component-wise *log*-hazard ratios comparing the treatment
to control, and will be used an the effect size in sample size calculation.
Further assume that patients are recruited to the trial uniformly in
an initial period $[0, \tau_b]$ and followed up until time $\tau$ $(\tau\geq \tau_b)$,
during which they randomly drop out with an exponential hazard rate of $\lambda_L$.
This leads to $C^{(a)}\sim\mbox{Unif}[\tau-\tau_b,\tau]\wedge\mbox{Expn}(\lambda_L)$.
The outcome parameters $\lambda_D, \lambda_H$, and $\kappa$ may be estimated from pilot study data
if available (see Section 3.2 of Mao et al. (2021)), whereas the design parameters
$\tau_b, \tau,$ and perhaps $\lambda_L$ are best elicited from investigators of the new trial.
The basic sample size formula is
\begin{equation}\tag{3}
n=\frac{\zeta_0^2(z_{1-\beta}+z_{1-\alpha/2})^2}{q(1-q)(\boldsymbol\delta_0^{\rm T}\boldsymbol\xi)^2},
\end{equation}
where $q=n_1/n$, $1-\beta$ is the target power,
$\zeta_0$ is a noise parameter similar to the standard deviation in the $t$-test,
and $\boldsymbol\delta_0$ is a bivariate vector containing the derivatives of the
true win ratio with respect to $\xi_1$ and $\xi_2$.
Under model (2) for the outcomes and the specified follow-up design,
we can calculate
$\zeta_0^2$ and $\boldsymbol\delta_0$ as functions of $\lambda_D, \lambda_H, \kappa, \tau_b, \tau$,
and $\lambda_L$ by numerical means.
Note in particular that they do *not* depend on the effect size $\boldsymbol\xi$.
## BASIC SYNTAX
The function that implements formula (3) is
`WRSS()`. We need to supply at least
two arguments: `xi` for the bivariate effect size $\boldsymbol\xi$
(*log*-hazard ratios) and a list `bparam`
containing `zeta2` for $\zeta_0^2$ and `delta` for $\boldsymbol\delta_0$.
That is,
```{r,eval=F}
obj<-WRSS(xi,bparam)
```
The calculated $n$ can be extracted from `obj$n`.
The default configurations for $q$, $\alpha$ and $1-\beta$ are
0.5, 0.05, and 0.8 but can nonetheless be overridden
through optional arguments `q`, `alpha`, and `power`,
respectively. You can also change the default two-sided test
to one-sided by specifying `side=1`.
The function `WRSS()` itself is almost unremarkable given the simplicity
of the underlying formula. What takes effort is the computation of
$\zeta_0^2$ and $\boldsymbol\delta_0$ needed for `bparam`.
If you have the parameters $\lambda_D, \lambda_H, \kappa, \tau_b, \tau$, and
$\lambda_L$ ready, you can do so by using the `base()` function:
```{r,eval=F}
bparam<-base(lambda_D,lambda_H,kappa,tau_b,tau,lambda_L)
```
where the arguments follow the order of the said parameters. The returned object
`bparam` can be directly used as argument for `WRSS()` (it is precisely a list containing
`zeta2` and `delta` for the computed $\zeta_0^2$ and $\boldsymbol\delta_0$, respectively).
Due to the numerical complexity, `base()` will typically require some wait time.
Finally, if you have a pilot dataset to estimate $\lambda_D$,
$\lambda_H$, and $\kappa$ for the baseline outcome distribution,
you can use the `gumbel.est()` function:
```{r,eval=F}
gum<-gumbel.est(id, time, status)
```
where `id` is a vector containing the unique patient identifiers,
`time` a vector of event times,
and `status` a vector of event type labels: `status=2` for nonfatal
event, `=1` for death, and `=0` for censoring.
The returned object is a list containing real
numbers `lambda_D`, `lambda_H`, and `kappa` for $\lambda_D$,
$\lambda_H$, and $\kappa$ respectively. These can then be fed into
`base()` to get `bparam`.
## A REAL EXAMPLE
We demonstrate the use of the above functions in
calculating sample size for win ratio analysis of
death and hospitalization using
baseline parameters estimated from a previous cardiovascular trial.
### Pilot data description
The Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION)
trial was conducted on a cohort of over two thousand
heart failure patients recruited between 2003--2007 across the USA, Canada, and France (O'Connor
et al., 2009). The study aimed to assess the effect of adding aerobic exercise
training to usual care on the patient's composite endpoint of all-cause death and all-cause hospitalization.
We consider a high-risk subgroup consisting of 451 study patients. For detailed information
about this subgroup, refer to the vignette *Two-sample win ratio tests of recurrent event and death*.
We first load the package and clean up the baseline dataset for use.
```{r setup}
## load the package
library(WR)
## load the dataset
data(hfaction_cpx9)
dat<-hfaction_cpx9
head(dat)
## subset to the control group (usual care)
pilot<-dat[dat$trt_ab==0,]
```
### Use pilot data to estimate baseline parameters
Now, we can use the `gumbel.est()` functions to estimate $\lambda_D$,
$\lambda_H$, and $\kappa$.
```{r}
id<-pilot$patid
## convert time from month to year
time<-pilot$time/12
status<-pilot$status
## compute the baseline parameters for the Gumbel--Hougaard
## copula for death and hospitalization
gum<-gumbel.est(id, time, status)
gum
lambda_D<-gum$lambda_D
lambda_H<-gum$lambda_H
kappa<-gum$kappa
```
This gives us $\widehat\lambda_D=0.11$, $\widehat\lambda_H=0.68$, and
$\widehat\kappa=1.93$.
Suppose that we are to launch a new trial that lasts $\tau=4$ years,
with an initial accrual period of $\tau_b=3$ years. Further suppose
that the loss to follow-up rate is $\lambda_L=0.05$ (about half of the
baseline death rate).
Combining this set-up with the estimated outcome parameters,
we can calculate $\zeta_0^2$ and $\boldsymbol\delta_0$ using the `base()` function.
```{r}
## max follow-up 4 years
tau<-4
## 3 years of initial accrual
tau_b<-3
## loss to follow-up hazard rate
lambda_L=0.05
## compute the baseline parameters
bparam<-base(lambda_D,lambda_H,kappa,tau_b,tau,lambda_L)
bparam
```
### Using `WRSS()` to compute sample size
Now we can use the computed `bparam` to calculate sample size under different
combinations of component-wise hazard ratios. We consider target power
$1-\beta=80%$ and $90%$.
```{r}
## effect size specification
thetaD<-seq(0.6,0.95,by=0.05) ## hazard ratio for death
thetaH<-seq(0.6,0.95,by=0.05) ## hazard ratio for hospitalization
## create a matrix "SS08" for sample size powered at 80%
## under each combination of thetaD and thetaH
mD<-length(thetaD)
mH<-length(thetaH)
SS08<-matrix(NA,mD,mH)
rownames(SS08)<-thetaD
colnames(SS08)<-thetaH
## fill in the computed sample size values
for (i in 1:mD){
for (j in 1:mH){
## sample size under hazard ratios thetaD[i] for death and thetaH[j] for hospitalization
SS08[i,j]<-WRSS(xi=log(c(thetaD[i],thetaH[j])),bparam=bparam,q=0.5,alpha=0.05,
power=0.8)$n
}
}
## print the calculated sample sizes
print(SS08)
## repeating the same calculation for power = 90%
SS09<-matrix(NA,mD,mH)
rownames(SS09)<-thetaD
colnames(SS09)<-thetaH
## fill in the computed sample size values
for (i in 1:mD){
for (j in 1:mH){
## sample size under hazard ratios thetaD[i] for death and thetaH[j] for hospitalization
SS09[i,j]<-WRSS(xi=log(c(thetaD[i],thetaH[j])),bparam=bparam,q=0.5,alpha=0.05,
power=0.9)$n
}
}
## print the calculated sample sizes
print(SS09)
```
Powered at $80\%$, the sample size ranges from 193 at $\exp(\boldsymbol\xi)=(0.6, 0.6)^{\rm T}$
to 19,077 at $\exp(\boldsymbol\xi)=(0.95, 0.95)^{\rm T}$;
powered at $90\%$, the sample size ranges from 258 at $\exp(\boldsymbol\xi)=(0.6, 0.6)^{\rm T}$
to 25,539 at $\exp(\boldsymbol\xi)=(0.95, 0.95)^{\rm T}$.
We can even use a 3D plot to display the calculated sample size as a function of the
hazard ratios $\exp(\xi_1)$ and $\exp(\xi_2)$.
```{r, fig.height = 9, fig.width=7}
oldpar <- par(mfrow = par("mfrow"))
par(mfrow=c(2,1))
persp(thetaD, thetaH, SS08/1000, theta = 50, phi = 15, expand = 0.8, col = "gray",
ltheta = 180, lphi=180, shade = 0.75,
ticktype = "detailed",
xlab = "\n HR on Death", ylab = "\n HR on Hospitalization",
zlab=paste0("\n Sample Size (10e3)"),
main="Power = 80%",
zlim=c(0,26),cex.axis=1,cex.lab=1.2,cex.main=1.2
)
persp(thetaD, thetaH, SS09/1000, theta = 50, phi = 15, expand = 0.8, col = "gray",
ltheta = 180, lphi=180, shade = 0.75,
ticktype = "detailed",
xlab = "\nHR on Death", ylab = "\nHR on Hospitalization",
zlab=paste0("\n Sample Size (10e3)"),
main="Power = 90%",
zlim=c(0,26),cex.axis=1,cex.lab=1.2,cex.main=1.2
)
par(oldpar)
```
## References
* Mao, L. (2019). On the alternative hypotheses for the win ratio. *Biometrics*, 75, 347-351.
https://doi.org/10.1111/biom.12954.
* Mao, L., Kim, K., & Li, Y. (2022). On recurrent-event win ratio.
*Statistical Methods in Medical Research*, under review.
* Mao, L., Kim, K., & Miao, X. (2021). Sample size formula for general win ratio analysis.
*Biometrics*, https://doi.org/10.1111/biom.13501.
* O'Connor, C. M., Whellan, D. J., Lee, K. L., Keteyian, S. J., Cooper, L. S., Ellis, S. J.,
Leifer, E. S., Kraus, W. E., Kitzman, D. W., Blumenthal, J. A. et al. (2009). "Efficacy and
safety of exercise training in patients with chronic heart failure: HF-ACTION randomized
controlled trial". *Journal of the American Medical Association*, 301, 1439--1450.
* Pocock, S., Ariti, C., Collier, T., and Wang, D. (2012). The win ratio: a new approach
to the analysis of composite endpoints in clinical trials based on clinical priorities.
*European Heart Journal*, 33, 176--182.