The one of the principles described in ICH E9 is that only results obtained from pre-specified statistical methods in a protocol are regarded as confirmatory evidence. However, in multi-regional clinical trials, even when results obtained from pre-specified statistical methods in protocol are significant, it does not guarantee that the test treatment is approved by regional regulatory agencies. In other words, there is no so-called global approval, and each regional regulatory agency makes its own decision in the face of the same set of data from a multi-regional clinical trial. Under this situation, there are two natural methods a regional regulatory agency can use to estimate the treatment effect in a particular region. The first method is to use the overall treatment estimate, which is to extrapolate the overall result to the region of interest. The second method is to use regional treatment estimate. If the treatment effect is completely identical across all regions, it is obvious that the overall treatment estimator is more efficient than the regional treatment estimator. However, it is not possible to confirm statistically that the treatment effect is completely identical in all regions. Furthermore, some magnitude of regional differences within the range of clinical relevance may naturally exist for various reasons due to, for instance, intrinsic and extrinsic factors. Nevertheless, if the magnitude of regional differences is relatively small, a conventional method to estimate the treatment effect in the region of interest is to extrapolate the overall result to that region. The purpose of this paper is to investigate the effects produced by this type of extrapolation via estimations, followed by hypothesis testing of the treatment effect in the region of interest. This paper is written from the viewpoint of regional regulatory agencies.
Currently, multi-regional clinical trials (MRCTs) play a major role in providing evidence of the efficacy and safety of new drugs. MRCTs are studies planned with the objective of the world-scale development and approval of new drugs. In MRCTs, study sites at multiple countries and regions participate in a single study based on a common protocol and the trials are conducted simultaneously (ICH, 2006, 2017; MHLW, 2007). In the planning stages of a MRCT, an important premise is that there can be no, or reasonably small, regional variation. This premise of MRCTs is usually supported by biological and medical knowledge about the disease; the mechanism of action of the compound; the medical practice characteristics in the individual regions (e.g., treatment guidelines and the availability of concomitant therapies); the health systems, and the available nonclinical, preclinical, and early clinical data. Usually, a small difference in the magnitude of the treatment effect is acceptable. If a substantial regional variation is expected, a global development strategy using MRCTs may not be appropriate (Chen
After a MRCT is finished, there are two main objectives in a conventional analysis of the MRCT. The primary is to demonstrate the efficacy of a drug in all participating regions. The second is, after the overall effect is shown, to evaluate the possibility of applying the overall trial results to each region by checking the consistency of the treatment effect across regions. Hence, an assessment of the consistency of the treatment effect across regions is a key issue in relation to MRCTs (Chen
In the analysis of data from MRCTs there is a very important thing to keep in mind, which there is no a so-called “global approval” of a medical product. From a regulatory standpoint, an approval decision is regional; that is, each regional regulatory agency makes its own decision given an identical set of data from a MRCT. This means that the same set of data from a MRCT may be analyzed differently to obtain approval from different regional regulatory agencies. The challenge is how to utilize the entire dataset to assess individual region effects, and how to leverage the data from outside the local region in a MRCT to make approval decisions in the local region. Therefore, it is very important to analyze MRCTs data from the viewpoint of regional regulatory agencies (Chen
For convenience, let the target region denote the region of interest. Let the treatment effects in both the target region and the non-target region be denoted by
In this case, the treatment effects in both the target region and the non-target region are completely identical. The target regulatory agency can make accurate statistical inference for the treatment effect in the target region (
However, the question arises of how we can be sure that the treatment effects in both the target region and the non-target region are completely identical (
If
In this case, a global development strategy using MRCTs may not be appropriate, and conducting separate clinical trials in each region may be a better strategy. If the fact that the difference in the treatment effect between the target region and the non-target region is substantial comes to light after the MRCT is conducted, it is obvious that the target regulatory agency cannot borrow information from the non-target region. In such a case, an estimate of the treatment effect only based on the target region is a reliable estimate of the treatment effect in the target region (
In this case, it may be possible for the target regulatory agency to borrow information from the non-target region, as the difference in the treatment effect between the target region and the non-target region is small and clinically not meaningful. Hence, the overall treatment estimator may be more reliable than the regional treatment estimator, i.e., the treatment effect estimator only based on the target region.
The purpose of this paper is to provide a statistical rationale which applies when the overall treatment estimator is better than the regional treatment estimator in order to assess the treatment effect in the target region (
This paper considers only the viewpoint of the target regulatory agency, as approval decisions are regional. We consider parallel designs for comparing a test product and a placebo. Let
We also assume the following:
The primary hypothesis for testing the overall treatment effect is given by
where
However, the target regulatory agency is more interested in the regional treatment effect in the target region; the hypotheses of interest are expressed as
where
where
A commonly used overall treatment estimator (
where
The regional treatment estimator (
As discussed in Section 2.1, if
In such cases, it is well-known that the power of interaction test to detect treatment-by-region interaction is low. With the help of SAS PROC GLMPOWER, the power of interaction test is plotted in Figure 1 when
In Section 4, we compare
In this section, we compare the performance capabilities of the two estimators (
It is clear that
the overall treatment estimator (
Given that the regional treatment effect estimator (
the MSE of
Similarly, because
we have
Therefore, the MSE of
When |
If we compare the two MSEs analytically, we have
The inequality in (
In this section, we compare the type I error rates of the two estimators (
The hypothesis of interest for the target regulatory agency is expressed as (
where
Although
In this subsection, we test the treatment effect in the target region (
where
In most MRCTs, we assume
However, the main purpose of this paper is to investigate what would occur if
Therefore, the type I error rate is greater than the nominal level when
Table 1 shows the type I error rate of
In this section, we compare the powers of the two estimators (
In order to investigate the power for testing the hypotheses in (
The main purpose of this paper is to investigate what would occur if
Therefore, we compare the powers of
In this subsection, the powers of
However, when
In this subsection, the powers of
From Figure 5, we find that the powers of
One of the problems associated with MRCTs is that there is no a global approval despite the fact that there is a pre-specified confirmatory analysis in the protocol. Therefore, each regional regulatory agency must make its own decisions based on its own analyses given an identical set of data from a MRCT. Under this situation, in this paper we compare two estimators (
If the treatment effects in both the target region and the non-target region are completely identical (
On the other hand, if
Another important issue is to distinguish “substantial” in the Section 2.2 from “small and clinically not meaningful” in the Section 2.3. This requires clinical judgement. For convenience of explanation, let us consider a hypothetical weight loss test treatment. Suppose that this weight loss test treatment reduces weight by 0.5 kg compared to placebo. Since 0.5 kg is not mathematically zero, it is true that the test treatment definitely reduces weight. However, the amount of weight lost is so small that no medical doctor will prescribe the test treatment. In other words, the test treatment has no clinical significance. Therefore, a new weight loss test treatment would have clinical significance only if weight reduction was sufficient. The question is how much weight should a new weight loss test treatment reduce in order to have clinical significance. This reference point is determined by clinicians, not statisticians. The problem is that the opinions of clinicians may differ.
Now, let us consider the problem of comparing
Anyhow, suppose that there is a positive value
If the value of
In addition to the fixed-effect model, the random-effect model has been studied for MRCTs (Chen
It is very difficult to accept that regions in MRCTs are randomly sampled from infinitely many regions. Regions in MRCTs are usually chosen, for instance, by considering disease prevalence and economic factors such as market sizes and the cost of clinical trials.
It is the treatment effect in the target region (
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1A09916819).
The type I error rate for
0.1 | 10.59 | 17.01 | 3.46 | 3.93 |
0.2 | 9.22 | 14.33 | 3.34 | 3.74 |
0.3 | 7.98 | 11.95 | 3.22 | 3.57 |
0.4 | 6.87 | 9.87 | 3.11 | 3.39 |
0.5 | 5.88 | 8.06 | 3.00 | 3.23 |
0.6 | 5.01 | 6.52 | 2.89 | 3.07 |
0.7 | 4.25 | 5.21 | 2.79 | 2.92 |
0.8 | 3.58 | 4.13 | 2.69 | 2.77 |
0.9 | 3.00 | 3.23 | 2.59 | 2.63 |
1.0 | 2.50 | 2.50 | 2.50 | 2.50 |