Vignette for survAH package
Hajime Uno, Miki Horiguchi
January 16, 2023
1 Introduction
An important objective of clinical research investigating the safety
and efficacy of a new intervention is to provide quantitative
information about the intervention effect on clinical outcomes. Such
quantitative information is critical for informed treatment decision
making to balance the risks and benefits of the new intervention. In
those studies where time-to-event outcomes are clinical endpoints of
interest, the traditional Cox’s hazard ratio (HR) has been used for
estimating and reporting the treatment effect magnitude for many
decades. However, this traditional approach may not have provided the
sufficient quantitative information that is needed for informed decision
making in clinical practice for the following reasons. First, this
approach does not require calculating the absolute hazard in each group
in order to calculate the HR, which is a desirable feature from a
statistical point of view, but which makes the clinical interpretation
difficult. From the clinical point of view, the two numbers from the
treatment and control groups are necessary for interpreting a
between-group contrast measure (e.g., difference or ratio). Second, if
the proportional hazards assumption is not correct, the interpretation
of HR is not obvious because it is affected by the underlying
study-specific censoring time distribution.[1,2]
The average hazard with survival weight (AHSW), which can be
interpreted as the general censoring-free incidence rate (CFIR), is a
summary measure of the event time distribution and does not depend on
the underlying study-specific censoring time distribution. The approach
using AHSW (or CFIR) provides two numbers from the treatment and control
groups and allows us to summarize the treatment effect magnitude in both
absolute and relative terms, which would enhance the clinical
interpretation of the treatment effect on time-to-event outcomes.[3]
This vignette is a supplemental documentation for the survAH
package and illustrates how to use the functions in the package to
compare two groups with respect to the AHSW (or CFIR). The package was
made and tested on R version 4.2.2.
2 Installation
Open the R or RStudio applications. Then, copy and paste either of
the following scripts to the command line.
To install the package from the CRAN:
install.packages("survAH")
To install the development version:
install.packages("devtools") #-- if the devtools package has not been installed
devtools::install_github("uno1lab/survAH")
2 Sample Reconstructed Data
Throughout this vignette, we use sample reconstructed data of the
CheckMate214 study reported by Motzer et al. [4] The data consists of
847 patients with previously untreated clear-cell advanced renal-cell
carcinoma; 425 for the nivolumab plus ipilimumab group (treatment) and
422 for the sunitinib group (control).
The sample reconstructed data of the CheckMate214 study is available
on survAH package as cm214_pfs. To load the data, copy
and paste the following scripts to the command line.
library(survAH)
nrow(cm214_pfs)
#> [1] 847
head(cm214_pfs)
#> time status arm
#> 1 0.451 1 1
#> 2 0.451 1 1
#> 3 0.451 1 1
#> 4 0.451 1 1
#> 5 0.451 1 1
#> 6 0.710 1 1
Here, time is months from the registration to
progression-free survival (PFS), status is the
indicator of the event (1: event, 0: censor), and arm
is the treatment assignment indicator (1: Treatment group, 0: Control
group).
Below are the Kaplan-Meier estimates for the PFS for each treatment
group.
The two survival curves showed similar trajectories up to six months,
but after that, a difference appeared between the two groups. This is
the so-called delayed difference pattern often seen in immunotherapy
trials. The HR based on the traditional Cox’s method was 0.82 (0.95CI:
0.68 to 0.99, p-value=0.037). Since the validity of the proportional
hazards assumption was not clear in this study, there is no clear
interpretation on the reported HR. Even if the proportional hazards
assumption seemed to be reasonable, the lack of a group-specific
absolute value regarding hazard makes the clinical interpretation of the
treatment effect difficult. For example, if the baseline absolute hazard
is very low, the reported HR (0.82) may indicate a clinically ignorable
treatment effect magnitude. If it is high, even an HR that is closer to
1 (e.g., 0.98) may indicate a clinically significant treatment effect
magnitude.
3 Average Hazard with Survival Weight (AHSW)
For a given \(\tau,\) a general form
of the average hazard (AH) is denoted by \[
\eta(t) = \frac{\int_0^\tau h(u)w(u)du}{\int_0^\tau w(u)du},\]
where \(h(t)\) and \(w(t)\) are the hazard function for the
event time \(T,\) and a non-negative
weight function, respectively. Let \(S(t)\) be the survival function for \(T.\) We use \(S(t)\) as the weight, which gives the AHSW
\[ \eta(t) = \frac{\int_0^\tau
h(u)S(u)du}{\int_0^\tau S(u)du}.\] The detailed motivation for
using \(S(t)\) as \(w(t)\) was discussed in Uno and Horiguchi
[3]. The AHSW has a clear interpretation as the average person-time
incidence rate on a given time window \([0,\tau].\) It can also be called as the
general censoring-free incidence rate (CFIR) in contrast to the
conventional person-time incidence rate that potentially depends on an
underlying study-specific censoring time distribution.
From now on, we simply call the AHSW (or CFIR) the average hazard
(AH). The AH is denoted by the ratio of cumulative incidence probability
and restricted mean survival time at \(\tau
< \infty\): \[ \eta(\tau) =
\frac{1-S(\tau)}{\int_0^\tau S(t)dt}.\]
Let \(\hat{S}(t)\) denote the
Kaplan-Meier estimator for \(S(t).\) A
natural estimator for \(\eta(\tau)\) is
then given by
\[ \hat{\eta}(\tau) =
\frac{1-\hat{S}(\tau)}{\int_0^\tau \hat{S}(t)dt}.\] The large
sample properties and a standard error formula of \(\hat{\eta}(\tau)\) are given in Uno and
Horiguchi [3].
4 Two-sample comparison using AH and its implementation
Let \(\eta_{1}(\tau)\) and \(\eta_{0}(\tau)\) denote the AH for
treatment group 1 and 0, respectively. Now, we compare the two survival
curves, using the AH. Specifically, we consider the following two
measures to capture the between-group contrast:
- Difference in AH (DAH) \[ \eta_{1}(\tau)
- \eta_{0}(\tau) \]
- Ratio of AH (RAH) \[ \eta_{1}(\tau) /
\eta_{0}(\tau) \]
These are estimated by simply replacing \(\eta_{1}(\tau)\) and \(\eta_{0}(\tau)\) by their empirical
counterparts (i.e., \(\hat{\eta_{1}}(\tau)\) and \(\hat{\eta_{0}}(\tau)\), respectively). For
the inference of the ratio type metrics, we use the delta method to
calculate the standard error. Specifically, we consider \(\log \{ \hat{\eta_{1}}(\tau)\}\) and \(\log \{ \hat{\eta_{0}}(\tau)\}\) and
calculate the standard error of log-AH. We then calculate a confidence
interval for the log-ratio of AH, and transform it back to the original
ratio scale; the detailed formula is given in [4].
The procedures below show how to use the function,
ah2, to implement these analyses.
time = cm214_pfs$time
status = cm214_pfs$status
arm = cm214_pfs$arm
ah2(time, status, arm, tau=21)
The first argument (time) is the time-to-event
vector variable. The second argument (status) is also a
vector variable with the same length as time, each of
the elements takes either 1 (if event) or 0 (if no event). The third
argument (arm) is a vector variable to indicate the
assigned treatment of each subject; the elements of this vector take
either 1 (if the active treatment arm) or 0 (if the control arm). The
fourth argument (tau) is a scalar value to specify the
truncation time point \({\tau}\) for
the AH calculation.
When \(\tau\) is not specified in
ah2, (i.e., when the code looks like below)
the default \(\tau\) (i.e., the
maximum time point where the size of risk set for both groups remains at
least 10) is used to calculate the AH. It is best to confirm that the
size of the risk set is large enough at the specified \(\tau\) in each group to make sure the
Kaplan-Meier estimates are stable.
The ah2 function returns AH on each group and the
results of the between-group contrast measures listed above. Note that
we chose 21 months for \(\tau\) in this
example.
obj = ah2(time, status, arm, tau=21)
print(obj, digits=3)
#>
#> The truncation time: tau = 21 was specified.
#>
#> Number of observations:
#> Total N Event by tau Censor by tau At risk at tau
#> arm0 422 225 163 34
#> arm1 425 219 160 46
#>
#>
#> Average Hazard (AH) by arm:
#> Est. Lower 0.95 Upper 0.95
#> AH (arm0) 0.066 0.057 0.076
#> AH (arm1) 0.049 0.042 0.057
#>
#>
#> Between-group contrast:
#> Est. Lower 0.95 Upper 0.95 P-value
#> Ratio of AH (arm1/arm0) 0.747 0.608 0.917 0.005
#> Difference of AH (arm1-arm0) -0.017 -0.029 -0.005 0.006
The estimated AHs were 0.049 (0.95CI: 0.042 to 0.057) and 0.066
(0.95CI: 0.057 to 0.076) for the treatment group and the control group,
respectively. The ratio and difference of AH were 0.747 (0.95CI: 0.608
to 0.917, p-value=0.005) and -0.017 (0.95CI: -0.029 to -0.005,
p-value=0.006), respectively.
5 Conclusions
As illustrated, the presented approach using AHSW provides more
robust and reliable quantitative information about the treatment effect
on time-to-event outcomes than the traditional Cox’s hazard ratio
approach. We hope the AHSW-based approach, along with this
survAH package, will be helpful to clinical researchers by
providing interpretable quantitative information about the treatment
effect magnitude, which will ultimately foster better informed decision
making.
References
[1] Uno H, Claggett B, Tian L, et al. (2014). Moving beyond the
hazard ratio in quantifying the between-group difference in survival
analysis. Journal of clinical oncology : official journal of the
American Society of Clinical Oncology 32,
2380-2385. https://doi.org/10.1200/JCO.2014.55.2208
[2] Horiguchi M, Hassett MJ, Uno H (2019). How do the accrual pattern
and follow-up duration affect the hazard ratio estimate when the
proportional hazards assumption is violated? Oncologist
24(7), 867-871. https://doi.org/10.1634/theoncologist.2018-0141
[3] Uno H and Horiguchi M (2023). Ratio and difference of average
hazard with survival weight: new measures to quantify survival benefit
of new therapy. Statistics in Medicine Online
first, 1-17. https://doi.org/10.1002/sim.9651
[4] Motzer RJ, Tannir NM, McDermott DF, et al. (2018). Nivolumab plus
ipilimumab versus sunitinib in advanced renal-cell carcinoma. New
England Journal of Medicine 378(14), 1277–1290. https://doi.org/10.1056/NEJMoa1712126