When individual observations are normally distributed, stage-wise Z-
or T-test statistics fulfill these criteria. In practice, however, there
are many situations where one cannot reasonably assume that individual
observations are sampled from a normal distribution. It is common for
trials to be conducted for binary or time-to-event endpoints, e.g. if
one is interested in investigating the impact of a drug on mortality.
One way to deal with this situation is by transforming the data to a
test-statistic which is approximately normal. In the following, we will
explain how to do so for binary and for time-to-event endpoints.
Binary endpoints
Binary endpoints are endpoints where individual observations follow a
Bernoulli distribution, i.e. \(X_i^T \sim
Bin(1,p_T)\) and \(X_i^C\sim
Bin(1,p_C)\). Our goal is to compare the probability \(p_T\) of an event in the treatment group
with a fixed value (single-arm trial) or with the probability \(p_C\) of an event in the control group
(two-armed trial). Thus, assuming that larger probabilities are
favorable, we have
\[
H_0: p_T \leq p_C\quad \text{vs.} \quad H_1:p_T > p_C.
\]
To test this hypothesis, one could use the test statistic \[
U=\sqrt{\frac{n}{2}}\frac{\hat{p}_T-\hat{p}_C}{\sqrt{\hat{p}_0(1-\hat{p}_0)}},
\]
where \(\hat{p}_T=\frac{1}{n}\sum_i^{n}X_i^T\) and
\(\hat{p}_C=\frac{1}{n}\sum_i^{n}X_i^C\) are
the maximum likelihood estimators of \(p_T\) and \(p_C\) and where \(\hat{p}_0=\frac{\frac{1}{n}\sum_i^{n}X_i^T+\frac{1}{n}\sum_i^{n}X_i^C}{2}\).
The outcome of \(U\) is then
compared to \(c_f\) and \(c_e\), the first stage boundaries. If \(U\in [c_f,c_e]\), we continue the trial and
compute a new value for \(U_2\) in the
second stage, where we reject the null if \(U_2>c_2\).
It is a well-known fact that this test statistic is asymptotically
normal, and we will give a proof of this in the next section.
Asymptotic distribution of the test statistic
We begin with the difference \(\hat{p}_T-\hat{p}_C\). Using the de
Moivre-Laplace theorem, we get that \[
\frac{n\hat{p}_T-np_T}{\sqrt{n}} \overset{d}{\to}
\mathcal{N}(0,p_T(1-p_T)).
\] After defining \(\sigma_A^2=p_T(1-p_T)+p_C(1-p_C)\), we
obtain \[
\frac{n\hat{p}_T-np_T}{\sqrt{n}}-\frac{n\hat{p}_C-np_C}{\sqrt{n}}=\sqrt{n}(\hat{p}_T-\hat{p}_C-(p_T-p_C))\overset{d}{\to}\mathcal{N}(0,\sigma_A^2),
\] so it follows \[
\sqrt{n}(\hat{p}_T-\hat{p}_C)\overset{d}{\to}\mathcal{N}(\sqrt{n}(p_T-p_c),\sigma_A^2).
\] Applying the continuous mapping theorem, it results that \(\hat{\sigma}_0:=\sqrt{2\hat{p}_0(1-\hat{p}_0)}
\overset{P}{\to}\sqrt{2p_0(1-p_0)}:=\sigma_0\), so by Slutzky’s
theorem, we get \[
\frac{\sqrt{n}(\hat{p}_T-\hat{p}_C)}{\sigma_0}=\sqrt{\frac{n}{2}}\frac{\hat{p}_T-\hat{p}_C}{\sqrt{\hat{p}_0(1-\hat{p}_0)}}\overset{d}{\to}\mathcal{N}\left(\sqrt{n}\frac{p_T-p_C}{\sigma_0},\frac{\sigma_A^2}{\sigma_0^2}\right).
\] Hence, for sufficiently large \(n\), \(U\)
is approximately normal.
Note that under the null hypothesis, \(\sigma_A^2=\sigma_0^2\) and \(p_T=p_C\). Thus, approximately, \(U\sim \mathcal{N}(0,1)\) under \(H_0\).
adoptr and binomial endpoints
Implementation details
Currently, adoptr only supports the specification of a
single fixed reference value fo \(p_C\), while general prior distributions
are supported for the effect size \(\theta\).
This is a limitation, as uncertainty about the control group rate
cannot be represented in this framework. However, in a trial comparing a
new treatment to an existing one, it is usually reasonable to assume
that some information about the event rate in the control group is
available beforehand.
Example
Assume we want to plan a two-armed trial with an assumed rate of
events in the control group of \(p_C=0.3\). These parameters are encoded in
the DataDistribution object.
datadist <- Binomial(0.3, two_armed = TRUE)
Let us furthermore postulate a normal prior distribution for \(\theta\) with expectation \(\mu=0.2\) and standard deviation \(\sigma=0.2\), which was truncated to the
interval \((-.29,0.69)\). It is
necessary to use a truncation to ensure that \(p_T \in (0,1)\).
H_0 <- PointMassPrior(.0, 1)
prior <- ContinuousPrior(function(x) 1 / (pnorm(0.69, 0.2, 0.2) -
pnorm(-0.29, 0.2, 0.2)) *
dnorm(x, 0.2, 0.2),
support = c(-0.29,0.69),
tighten_support = TRUE)
We require a maximal type one error of \(\alpha\leq 0.025\) and a minimum expected
power of \(\mathbb{E}[1-\beta]\geq
0.8\).
alpha <- 0.025
min_epower <- 0.8
toer_cnstr <- Power(datadist, H_0) <= alpha
epow_cnstr <- Power(datadist, condition(prior, c(0.0,0.69))) >= min_epower
Next, we need to choose an objective function, which will be the
expected sample size under the chosen prior distribution for \(\theta\) in this example. After having
chosen a starting point for the optimization procedure, we use the
minimize function to determine the optimal design
parameters.
ess <- ExpectedSampleSize(datadist,prior)
init <- get_initial_design(0.2,0.025,0.2)
opt_design <- minimize(ess,subject_to(toer_cnstr,epow_cnstr),
initial_design = init, check_constraints = TRUE)
plot(opt_design$design)
Time-to-event endpoints
Time-to-event endpoints are another common type of endpoint used in
clinical trials. Time-to-event data is two-dimensional and consists of
an indicator denoting the occurrence of an event or censoring, and a
time of the event or censoring.
A common effect measure for time-to-event endpoints is the so called
hazard-ratio, which is the ratio of hazard functions between two groups
or the ratio of the hazard of one group and a postulated baseline
hazard.
In the following, the hazard ratio will be denoted by \(\theta :=
\frac{\lambda_C(t)}{\lambda_T(t)}\), and we will assume that
\(\theta\) is constant over time.
Assuming that less hazard is favorable, the resulting hypotheses to be
tested are
\[
H_0: \theta\leq 1 \quad \text{vs.} \quad H_1: \theta >1.
\]
Let \(1,\dots,J\) be the distinct
times of observed events in either group and let \(n_{T,j}, n_{C,j}\) be the number of
subjects, who neither had an event nor have been censored. Additionally,
let \(O_{T,j},O_{C,j}\) denote the
observed number of events at time \(j\). In the following we assume that there
are no ties, so \(O_{T,j},O_{C,j}\in
\{0,1\}\). Define \(n_j:=n_{T,j}+n_{C,j}\) and \(O_j:= O_{T,j}+O_{C,j}\). Under the null,
the hazard functions of the groups are equal. Thus, for \(i \in \{T,C\}\), \(O_{i,j}\) can be regarded as the number of
events in a draw of size \(n_{i,j}\),
where the population has size \(n_j\).
Therefore, \(O_{i,j}\) follows a
hypergeometric distribution, i.e. \(O_{i,j}\sim h(n_j,O_j,n_{i,j})\). This
distribution has an expected value of \(E_{i,j}:=\mathbb{E}[O_{i,j}]=n_{i,j}\frac{O_j}{n_j}\)
and variance \(V_{i,j}:=\text{Var}(O_{i,j})=E_{i,j}\left(\frac{n_j-O_j}{n_j}\right)\left(\frac{n_j-n_{i,j}}{n_j-1}\right)\).
Using these definitions, we can define the so-called log-rank test
statistic as
\[
L=L_T:=\frac{\sum_{j=1}^J O_{T,j}-E_{T,j}}{\sqrt{\sum_{j=1}^J V_{T,j}}}.
\] Note that it does not matter whether we consider \(L_T\) or the analogously defined statistic
\(L_C\), both of them yield the same
results, so we abbreviate \(L_T\) to
\(L\). By the central limit theorem, it
is easy to see that \(L \overset{d}{\to}
\mathcal{N}(0,1)\). Under the alternative, it can be shown that,
approximately, \(L \sim
\mathcal{N}(\text{log}(\theta)\frac{1}{2}\sqrt{J},1)\). If \(\psi\) is the average probability of an
event per recruit in both arms (also known as the event rate), \(J\) can be replaced by \(2 \cdot n \cdot \psi\) (where \(n\) denotes, like in the previous sections,
the number of recruits per group). Thus, by by postulating an event
probability, we can calculate the number of recruits per groups that
would be required to achieve a specific number of events, and by
extensions, a specific power. In adoptr, the parameter
\(\psi\) needs to be pre-specified in
order to obtain a one-parametric distribution (the other parameter being
\(\theta\)), and \(\psi\) is assumed to be fixed over time.
This is similar to the case of binary endpoints, where the response rate
in the control group \(p_C\) had to be
a constant value.
Survival analysis and adaptive designs
Notation
We expand the previous notation by an index \(k\in \{1,2\}\) denoting the current stage.
We observe \(d_1\) events until the
interim analysis at times \(\{1,\dots,J_1\}\), and we observe \(d_2\) events in total until the final
analysis at times \(\{1,\dots,J_2\}\).
We stress that, in this notation, \(d_2\) denotes the cumulative number of
events in both stages. Furthermore, let \(O_{i,j,k}\) be the number of observed
events in arm \(i\in \{T,C\}\) at time
\(j \in \{1,\dots,J_k\}\) in stage
\(k \in \{1,2\}\). Like in the previous
section, let \(E_{i,j,k}=
\mathbb{E}[O_{i,j,k}]=n_{i,j,k}\frac{O_{j,k}}{n_{j,k}}\), where
\(n_{i,j,k}\) is the number of patients
that have not had an event until the end of stage \(k\), and let \(O_{j,k}=O_{T,j,k}+O_{C,j,k}\) and \(n_{j,k}=n_{T,j,k}+n_{C,j,k}\). Likewise, we
can define \(V_{i,j,k}\) analogously to
the previous section. The cumulative log-rank test statistic up to stage
\(k \in \{1, 2\}\) can then be defined
as \[
L_k=\frac{\sum_{j=1}^{J_k} O_{T,j,k}-E_{T,j,k}}{\sqrt{\sum_{j=1}^{J_k}
V_{T,j,k}}}.
\]
Dependency issues and solutions
For normal distributed endpoints, the stage-wise test statistics come
from independent cohorts, which makes the calculation of their joint
distributions straightforward.
In survival analysis, it is possible to have patients that have been
recruited during the first stage, but have not had an event at the point
of the interim analysis. Those provide information for both stages: in
the first stage, we know that they survived, and in the second stage,
they might die or even survive until the end of the trial. This makes
the construction of a pair of suitable test statistics more
challenging.
In the following, we will present two methods to avoid these
issues.
Solution 1: Independent increments
It can be shown that the statistic \[
Z_2:= \frac{\sqrt{d_2}L_2-\sqrt{d_1}L_1}{\sqrt{d_2-d_1}}
\] is approximately distributed according to \(\mathcal{N}(\text{log}(\theta)\frac{1}{2}\sqrt{d_2-d_1},1)\),
and that \(L_1\) and \(Z_2\) are approximately independent.
The recruitment and testing procedure is now as follows: During the
accrual time, recruit approximately \(\frac{d_1}{2\psi}\) patients per group and
conduct the interim analysis after having observed \(d_1\) events overall.
Assume, \(q\) patients have not had
an event until the interim analysis. These patients are censored for the
computation of \(L_1\). \(L_1\) is then compared to the futility and
efficacy boundaries \(c_f\) and \(c_e\), and recruitment is stopped if \(L_1 < c_f\) or \(L_1 > c_e\). If \(c_f \leq L_1 \leq c_e\), recruitment of
patients for the second stage will continue.
The number of patients to be recruited in the second stage is
calculated so that \(d_2(L_1)\) events
are expected to be observed in the second stage. Similarly to the normal
case, the required number of events is a function of the first-stage
test statistic. The observation of these \(d_2(L_1)\) events will trigger the conduct
of the final analysis, where \(Z_2\) is
compared to \(c_2(L_1)\).
Solution 2: Left truncation at the second stage
Another approach uses left truncation and right censoring. In the
following, \(R_i\) stands for the
calendar time of entry for individual \(i\). Furthermore, let \(T_i\) be their time from entry to the event
and \(C_i\) their time from entry to
censoring. Assume that these values are independent and let \(r_i,t_i,c_i\) be realizations of \(R_i,T_i\) and \(C_i\). Additionally, we denote the calendar
time of the interim analysis by \(t_{Int}\) and the calender time of the
final analysis by \(t_{Fin}\).
Let individual \(i\) be recruited
before the interim analysis. Define \(y_{i,1}:=\min \{t_i,c_i,t_{Int}\}\),
i.e. \(y_{i,1}\) can be interpreted as
the minimum time until “something” happens to individual \(i\) in the first stage (either the interim
analysis is conducted, \(i\) had an
event or they were censored). The risk interval for the first-stage test
statistic is now defined to be \((0,y_{i,1})\), which means that individual
\(i\) belongs to the risk set at the
event time \(t_j\) if \(\min \{c_i,t_{Int}-r_i\}\geq x_j\).
If \(i\) has not had an event yet in
the first stage, was not lost to follow-up yet in the first stage, or if
they were recruited in the second stage, we define \(y_{i,2}:=\min\{t_i,c_i,t_{Fin}\}\). As
before, \(y_{i,2}\) can be interpreted
as the minimum time until something happens to individual \(i\) in the second stage: Either the final
analysis is conducted, \(i\) had an
event or was censored. The risk interval for an individual \(i\) for the second stage test-statistic is
then defined as \((\max(t_{Int}-r_i,
0),y_{i,2})\). This definition takes into account that that a
patient \(j\) who was recruited in the
first stage, but had no event until the interim analysis, has already
provided the information of “no event” in the time span \((0, t_{Int}-r_i)\) for the interim
analysis. To avoid double-counting in the second analysis, we say that
this patient is not in the risk set until this time span is over.
Using this idea, one can conduct the analyses of the trial according
to the following instructions: For the interim analysis, the procedure
is the same as for the independent increment solution, i.e. \(L_1\) is computed in the usual way and
compared to the futility and efficacy boundaries \(c_f\) and \(c_e\). If \(L_1
\in [c_e,c_f]\) (i.e. the trial is not stopped for early efficacy
or futility), we continue the observation and recruit new patients, such
that \(d_2(L_1)\) events are expected
to be observed in the second stage.
The second-stage test statistic is where the two methods differ.
Here, the idea is to construct a log-rank test statistic \(L_{Trunc}\) comprised of the data of the
patients who were recruited after the interim analysis, who contribute
to the test statistic in the usual way, and the patients recruited
before the interim analysis who have not had an event yet, who
contribute as left (and possibly right) truncated datapoints. In theory,
this is can be achieved by appropriately adjusting \(n_j\) for the respective event timings
\(j\) in the definition of the log-rank
statistic, and in practice, the survival::coxph method can
handle datasets of the aforementioned structure.
Example
In adoptr, trial designs to investigate time-to-event
endpoints can be represented in objects of the class
TwoStageDesignSurvival. These designs consist of slots for
the first stage efficacy and futility boundaries cf and
ce, a slot for the required number of events in the first
stage n1, slots for the spline interpolation points for the
n2 and c2 functions, and a postulated event
rate \(\psi\).
Two things are important to note here: First, the functions
n1 and n2 will return the number of required
events, not the number of recruits that would be required to achieve
this number of events in expectation. Information on the latter is
instead provided in the summary output of a design. Second,
for one-armed trials, n1 and n2 are the
overall number of required events, while for two-armed trials,
n1 and n2 return half of the overall number of
required events. This is analogous to the case with normally distributed
outcomes, where n1 and n2 return the
group-wise sample sizes. However, calling n1 and
n2 the group-wise number of events would be a misnomer, as
the timings of the interim analyses are based on the overall number of
events, and the number of events are unlikely to be exactly equal across
the two groups.
Let us say we want to plan a two-armed trial trial, where we assume
an average rate of events of \(\psi=0.7\).
datadist <- Survival(0.7, two_armed = TRUE)
The postulated rate of events \(\psi\) is saved in the
DataDistribution object and will be handed down to any
design optimized with respect to this distribution. It will be used to
convert the required number of events \(d\) to the estimated number of required
recruits via \(\frac{d}{\psi}\), but it
has no other uses. All other design parameters are invariant with
respect to the choice of \(\psi\).
Effect sizes for time-to-event trials are formulated with respect to
a hazard ratio. For our example, we assume \(\theta=1\) for the null hypothesis, and a
point alternative hypothesis of \(\theta=1.7\).
H_0 <- PointMassPrior(1, 1)
H_1 <- PointMassPrior(1.7, 1)
Our desired design should have a maximal type I error \(\alpha\leq 0.025\) and a minimum power of
\((1-\beta)\geq 0.8\).
alpha <- 0.025
min_power <- 0.8
toer_con <- Power(datadist,H_0) <= alpha
pow_con <- Power(datadist,H_1) >= min_power
exp_no_events <- ExpectedNumberOfEvents(datadist, H_1)
init <- get_initial_design(1.7, 0.025, 0.2, dist=datadist)
opt_survival <- minimize(exp_no_events, subject_to(toer_con,pow_con),
initial_design = init, check_constraints=TRUE)
summary(opt_survival$design)
#> For two-armed trials: nevs denotes half of the overall number of required events. nrec denotes the resulting group-wise sample size.
#>
#> TwoStageDesignSurvival: nevs1= 32 --> nrec1= 46
#> futility | continue | efficacy
#> x1: 0.79 | 0.83 0.98 1.24 1.54 1.85 2.10 2.25 | 2.29
#> c2(x1): +Inf | +2.17 +2.02 +1.77 +1.43 +1.00 +0.52 +0.07 | -Inf
#> nevs2(x1): 0 | 45 42 38 32 24 17 12 | 0
#> nrec2(x1): 0 | 64 60 54 45 35 25 17 | 0
#>
It should be noted that the number of required events \(2\cdot\)nevs1 and \(2\cdot\) nevs2(x1) are the
important information thresholds to be adhered to for testing purposes.
nrec1 and and nrec2(x1) only serve as rough
estimates for the required number of recruits to achieve the target
number of events. If additional information about e.g. time-dependence
of baseline hazards is available, it may be worthwhile to use a more
sophisticated approach than the \(\frac{d}{\psi}\) formula to estimate the
number of required subjects.