- Research article
- Open Access
- Open Peer Review
Testing non-inferiority of a new treatment in three-arm clinical trials with binary endpoints
- Nian-Sheng Tang^{1}Email author,
- Bin Yu^{1} and
- Man-Lai Tang^{2}
https://doi.org/10.1186/1471-2288-14-134
© Tang et al.; licensee BioMed Central. 2014
- Received: 4 August 2014
- Accepted: 12 December 2014
- Published: 18 December 2014
Abstract
Background
A two-arm non-inferiority trial without a placebo is usually adopted to demonstrate that an experimental treatment is not worse than a reference treatment by a small pre-specified non-inferiority margin due to ethical concerns. Selection of the non-inferiority margin and establishment of assay sensitivity are two major issues in the design, analysis and interpretation for two-arm non-inferiority trials. Alternatively, a three-arm non-inferiority clinical trial including a placebo is usually conducted to assess the assay sensitivity and internal validity of a trial. Recently, some large-sample approaches have been developed to assess the non-inferiority of a new treatment based on the three-arm trial design. However, these methods behave badly with small sample sizes in the three arms. This manuscript aims to develop some reliable small-sample methods to test three-arm non-inferiority.
Methods
Saddlepoint approximation, exact and approximate unconditional, and bootstrap-resampling methods are developed to calculate p-values of the Wald-type, score and likelihood ratio tests. Simulation studies are conducted to evaluate their performance in terms of type I error rate and power.
Results
Our empirical results show that the saddlepoint approximation method generally behaves better than the asymptotic method based on the Wald-type test statistic. For small sample sizes, approximate unconditional and bootstrap-resampling methods based on the score test statistic perform better in the sense that their corresponding type I error rates are generally closer to the prespecified nominal level than those of other test procedures.
Conclusions
Both approximate unconditional and bootstrap-resampling test procedures based on the score test statistic are generally recommended for three-arm non-inferiority trials with binary outcomes.
Keywords
- Approximate unconditional test
- Bootstrap-resampling test
- Non-inferiority trial
- Rate difference
- Saddlepoint approximation
- Three-arm design
Background
The objective of a non-inferiority trial is to demonstrate the efficacy of an experimental treatment not being inferior to a reference treatment by some pre-specified non-inferiority margin. Many authors considered two-arm non-inferiority trials without a placebo since the comparison between the experimental and reference treatments is direct and the potential ethical problems encountered in traditional placebo-controlled trials are avoided (for example, see Dunnett and Gent [1], Tango [2], and Tang et al. [3]). However, there are two major concerns for two-arm non-inferiority trials [4]. The first issue is the choice of the non-inferiority margin, which is the clinically acceptable amount or a combination of statistical reasoning and clinical judgement. The other issue is the evaluation of assay sensitivity, which refers to the ability of a trial to differentiate an effective treatment from a less effective or ineffective treatment [5]. Without a placebo arm, the assay sensitivity of a trail is not demonstrable from the trial data and ones must rely on some external information (e.g., historical placebo trails) for the reference treatment [4]. Without the trial assay sensitivity, any non-inferiority testing results from the comparison of the experimental and reference treatments will become unconvincing. There are some indications where it is considered ethically acceptable to continue to randomize patients to placebo despite the fact that an effective treatment exists and there is interest in seeing not only whether the new treatment works at all but also how it measures up to accepted therapy. In this case, a three-arm non-inferiority clinical trail including the experimental treatment, an active reference treatment and a placebo is usually conducted to assess assay sensitivity and internal validation of a trail [6]. Indeed, three-arm trials are recommended in the guidelines of the ICH (The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use) and EMEA/CPMP (European Medicines Agency/Committee for Proprietary Medical Products) as a useful approach to the assessment of assay sensitivity and internal validation (e.g., see [7]).
Statistical inference based on three-arm non-inferiority clinical trials with normally distributed outcomes has received considerable attention in recent years. For example, Koch and Tangen [8] and Pigeot et al. [9] considered the problem of three-arm non-inferiority testing for normally distributed endpoints with a common but unknown variance. Koti [10] presented a new approach for normally distributed endpoints based on the Fieller-Hinkley distribution. Hasler, Vonk and Hothorn [11] proposed the usage of the t-distribution in the presence of heteroscedasticity. Hida and Tango [7] proposed a test procedure for assessing the assay sensitivity with a pre-specified margin defined as a difference between treatments in the presence of homoscedasticity. Ghosh, Nathoo, Gönen and Tiwari [12] developed a Bayesian approach in the presence of heteroscedasticity by incorporating both parametric and semi-parametric models. Gamalo, Muthukumarana, Ghosh and Tiwari [13] extended the existing generalized p-value approach for assessing the non-inferiority of a new treatment in a three-arm trial.
Recently, some statistical methods have also been developed for three-arm non-inferiority testing with binary endpoints. For example, Tang and Tang [14] proposed two asymptotic approaches for testing three-arm non-inferiority via rate difference based on Wald-type and score test statistics. Kieser and Friede (2007) revisited the performance of Tang and Tang’s [14] asymptotic test statistics via simulation studies and derived approximate sample size formulae for achieving the desired power. Munk, Mielke, Skipka and Freitag [15] developed likelihood ratio tests. Li and Gao [4] used the closed testing principle to establish the hierarchical testing procedure and proposed a group sequential type design. Liu, Tzeng and Tsou [16] presented a three-step testing procedure and derived an optimal sample size allocation rule in an ethical and reliable manner that minimizes the total sample size.
All aforementioned approaches for testing non-inferiority of a new treatment in a three-arm clinical trial with binary endpoints are based on large sample theory, and their accuracy has long been suspected and criticized when sample sizes are small or the data structure is sparse. To the best of our knowledge, limited work have been done to address these issues. Motivated by Jensen [17], we derive saddlepoint approximations to the cumulative distribution functions of Wald-type, score and likelihood ratio test statistics. Inspired by Tang and Tang [18], we also propose the exact unconditional, approximate unconditional and Bootstrap-resampling p-value calculation procedures for testing three-arm non-inferiority with small sample sizes.
The rest of this article is organized as follows. We first review three test statistics for assessing non-inferiority of a new treatment in three-arm clinical trials with binary endpoints. We also propose saddlepoint approximation, exact and approximate unconditional, and bootstrap-resampling approaches for calculating p-values. Simulation studies are conducted to investigate the performance of all test statistics based on different p-value calculation approaches in terms of type I error rate and power. An example is analyzed to demonstrate our methodologies. Finally, we discuss the performance of our proposed methodologies and present some conclusions.
Methods
Model
It can be easily shown from Equation (2.1) that the maximum likelihood estimates (MLEs) of π _{ T }, π _{ R } and π _{ P } are given by ${\widehat{\pi}}_{T}={x}_{T}/{n}_{T}$, ${\widehat{\pi}}_{R}={x}_{R}/{n}_{R}$ and ${\widehat{\pi}}_{P}={x}_{P}/{n}_{P}$, respectively.
Test statistics
It is possible that there is no point (π _{ P },π _{ R }) ∈Θ such that it satisfies the above equations, which implies that the likelihood function given in Equation (2.1) attains its maximum on the boundary of the parameter space Θ.
which are asymptotically distributed as the standard normal distribution under H _{0} as n _{ T }, n _{ R } and n _{ P } are sufficiently large. Hence, non-inferiority can be claimed if T _{ W }>z _{1−α } (or T _{ R }>z _{1−α }), where z _{1−α } is the (1−α)-quantile of the standard normal distribution. When π _{ P }=0, T _{ W } is the Wald-type statistic proposed in Blackwelder [20] and T _{ R } is the test statistic given by Farrington and Manning [21] for two-arm noninferiority trials.
which is asymptotically distributed as the standard normal distribution under H _{0} as n _{ T }, n _{ R } and n _{ P } are sufficiently large, where $\ell \left(\pi \right)={x}_{T}\text{log}\left({\pi}_{T}\right)+({n}_{T}-{x}_{T})\text{log}(1-{\pi}_{T})+{x}_{R}\text{log}\left({\pi}_{R}\right)+({n}_{R}-{x}_{R})\text{log}(1-{\pi}_{R})+{x}_{P}\text{log}\left({\pi}_{P}\right)+({n}_{P}-{x}_{P})\text{log}(1-{\pi}_{P})+\mathcal{C}$ with $\mathcal{C}=log\{{n}_{T}!{n}_{R}!{n}_{P}!\}-log\{{x}_{T}!{x}_{R}!{x}_{P}!({n}_{T}-{x}_{T})!({n}_{R}-{x}_{R})!({n}_{P}-{x}_{P})!\}$. Thus, non-inferiority can be claimed if T _{ L }>z _{1−α }.
p-value calculation methods
The non-inferiority hypothesis (2.2) can be claimed via the p-value method with the rule: H _{0} is rejected if the p-value is less than or equal to the prespecified significance level α. In what follows, we introduce five approaches for calculating p-values based on ${t}_{j}^{0}$, which is the observed value of test statistic T _{ j } (j=W,R,L) for the observed value $\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)$ of (X _{ T },X _{ R },X _{ P }).
(1) Asymptotic method (AM)
It follows from the above arguments that all statistics T _{ j }’s (j=W,R,L) asymptotically follow the standard normal distribution under the null hypothesis H _{0}:ψ≤0. Thus, the asymptotic p-value for testing hypothesis (2.2) via statistic T _{ j } (j=W,R,L) based on $\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)$ can be calculated by ${p}_{j}^{\mathit{\text{AM}}}\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)=P\left({T}_{j}\ge {t}_{j}^{o}|{H}_{0}\right)=1-\Phi \left({t}_{j}^{o}\right)$, where Φ(·) is the standard normal distribution function.
The above asymptotic approach for calculating p-value of testing hypothesis (2.2) via statistic T _{ j } (j=W,R W,L) is established under the large sample theory. Its accuracy has long been suspected and criticized, especially when n _{ T }, n _{ R } and/or n _{ P } are small since the skewness of the underlying binomial distributions is not taken into consideration. Some higher order corrections such as the saddlepoint approximation [17] have been proposed to improve the accuracy of the normal approximation. In what follows, we will derive saddlepoint approximations to distributions of the three test statistics.
(2) Saddlepoint approximation method (SAM)
where ${\omega}_{j}^{o}=\text{sgn}\left({\xc2}_{j}\right)\sqrt{2\left\{{\xc2}_{j}{t}_{j}^{o}-K({\xc2}_{j}/{B}_{j})\right\}}$ and ${\upsilon}_{j}^{o}={\xc2}_{j}{B}_{j}^{-1}\sqrt{\stackrel{\u0308}{K}({\xc2}_{j}/{B}_{j})}$, ${\xc2}_{j}$ is the unique solution to equation: $\stackrel{\u0307}{K}({\xc2}_{j}/{B}_{j})={t}_{j}^{o}{B}_{j}$ for j=W,R with ${B}_{W}=\sigma \left(\widehat{\pi}\right)$ and ${B}_{R}=\sigma \left(\stackrel{~}{\pi}\right)$, ${\omega}_{L}^{o}=\text{sgn}\left(\widehat{\psi}\right)\sqrt{2\left\{\ell \right(\widehat{\pi})-\ell (\stackrel{~}{\pi}\left)\right\}}$ and ${\upsilon}_{L}^{o}=\widehat{\psi}\sqrt{{n}_{T}{\mathcal{\mathscr{H}}}_{1}/{\mathcal{\mathscr{H}}}_{2}}$ with ${\mathcal{\mathscr{H}}}_{1}={n}_{T}{n}_{R}{n}_{P}(\theta {\widehat{\pi}}_{R}+(1-\theta \left){\widehat{\pi}}_{P}\right)(1-\theta {\widehat{\pi}}_{R}-(1-\theta \left){\widehat{\pi}}_{P}\right){\widehat{\pi}}_{R}(1-{\widehat{\pi}}_{R}){\widehat{\pi}}_{P}(1-{\widehat{\pi}}_{P})$, and ${\mathcal{\mathscr{H}}}_{2}={n}_{R}{n}_{P}{\stackrel{~}{\pi}}_{R}(1-{\stackrel{~}{\pi}}_{R}){\stackrel{~}{\pi}}_{P}(1-{\stackrel{~}{\pi}}_{P})$.
(3) Exact unconditional method (EUM)
and $I\left\{{T}_{j}({x}_{T},{x}_{R},{x}_{P})\ge {t}_{j}^{o}\right\}$ is 1 if ${T}_{j}({x}_{T},{x}_{R},{x}_{P})\ge {t}_{j}^{o}$ and 0 otherwise.
(4) Approximate unconditional method (AUM)
According to Tang and Tang [18] and Tang, Tang and Rosner [23], the exact unconditional test is always conservative, i.e., its corresponding type I error rate is always less than or equal to the prespecified significance level. Following Tang and Tang [18], these nuisance parameters can be eliminated by evaluating their values at their corresponding RMLEs under ψ=0. The approximate unconditional p-value for testing H _{0}:ψ≤0 via statistic T _{ j } (j=W,R,L) based on $\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)$ can be defined as ${p}_{j}^{\text{AU}}\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)=P\left({T}_{j}\ge {t}_{j}^{o}|\psi =0,{\pi}_{R}={\stackrel{~}{\pi}}_{R},{\pi}_{P}={\stackrel{~}{\pi}}_{P}\right)$.
(5) Bootstrap-resampling method (BTM)
Hypothesis testing based on the bootstrap-resampling method is usually recommended when sample sizes (i.e., n _{ T }, n _{ R } and n _{ P }) are small [24] or data structure is sparse (e.g., x _{ T } or x _{ R } or x _{ P } is close to zero or n _{ T }, n _{ R } and n _{ P }, respectively). Given the observation $\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)$, we compute the RMLEs ${\stackrel{~}{\pi}}_{T},{\stackrel{~}{\pi}}_{R}$ and ${\stackrel{~}{\pi}}_{P}$ of parameters π _{ T },π _{ R } and π _{ P }, and calculate the observed value ${t}_{j}^{0}$ of statistic T _{ j } (j=W,R,L). Based on the RMLEs ${\stackrel{~}{\pi}}_{T},{\stackrel{~}{\pi}}_{R}$ and ${\stackrel{~}{\pi}}_{P}$, we generate B bootstrap samples $\left\{\left({x}_{T}^{b},{x}_{R}^{b},{x}_{P}^{b}\right):b=1,\dots ,B\right\}$ from the following distribution: ${x}_{k}^{b}\sim \text{Bin}({n}_{k},{\stackrel{~}{\pi}}_{k})$ for k=T,R and P. For each of the B bootstrap samples, we compute the observed value ${t}_{j}^{b}$ of statistic T _{ j } (j=W,R,L). Hence, an approximate p-value for testing H _{0}:ψ≤0 via statistic T _{ j } based on $\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)$ is given by ${\widehat{p}}_{j}^{\mathit{\text{BT}}}\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)=\frac{1}{B}\sum _{b=1}^{B}I\left({t}_{j}^{b}\ge {t}_{j}^{0}\right)$.
For any given observation $\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)$, test statistic T _{ j } (j=W,R,L) and p-value calculation method, we reject the null hypothesis H _{0} at the significance level α if ${p}_{j}^{k}\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}\right)\le \alpha $ for k=AM, SA, EU, AU and BT.
Simulation study
for k=A M,S A M,E U M,A U M and BTM, whilst the corresponding power can be evaluated by replacing H _{0} in $f\left({x}_{T}^{o},{x}_{R}^{o},{x}_{P}^{o}|{\pi}_{T},{\pi}_{R},{\pi}_{P},{H}_{0}\right)$ by H _{1}.
Results
Simulation study
Exact powers ( % ) of various test procedures together with three statistics when π _{ T } = π _{ R } with n =30 and 60, θ =0 . 6and α =0 . 05
AM | SAM | EUM | AUM | BTM | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
n | λ _{ P }: λ _{ R }: λ _{ T } | π _{ P } | π _{ R } | T _{ W } | T _{ R } | T _{ L } | T _{ W } | T _{ R } | T _{ L } | T _{ W } | T _{ R } | T _{ L } | T _{ W } | T _{ R } | T _{ L } | T _{ W } | T _{ R } | T _{ L } |
30 | 1:1:1 | 0.15 | 0.5 | 13.4 | 12.3 | 13.9 | 43.6 | 21.2 | 22.5 | 5.0 | 18.1 | 15.3 | 18.2 | 18.2 | 14.9 | 17.9 | 18.0 | 16.2 |
0.8 | 44.2 | 43.9 | 38.0 | 42.6 | 72.4 | 30.0 | 28.1 | 43.5 | 40.3 | 42.1 | 42.1 | 39.7 | 43.7 | 43.9 | 42.1 | |||
0.95 | 86.0 | 85.8 | 75.2 | 95.6 | 97.7 | 36.8 | 67.8 | 79.1 | 76.0 | 74.2 | 74.2 | 71.3 | 75.8 | 75.4 | 75.0 | |||
0.3 | 0.5 | 8.8 | 7.5 | 8.3 | 39.9 | 23.2 | 14.8 | 2.9 | 10.7 | 8.5 | 10.8 | 10.8 | 9.3 | 11.1 | 11.1 | 9.1 | ||
0.8 | 30.1 | 29.2 | 22.0 | 21.1 | 35.6 | 26.6 | 15.9 | 29.2 | 24.8 | 30.0 | 30.0 | 26.8 | 30.2 | 30.7 | 29.0 | |||
0.95 | 66.3 | 64.1 | 45.6 | 80.6 | 88.3 | 26.4 | 42.5 | 52.7 | 49.8 | 59.4 | 59.4 | 57.1 | 60.0 | 59.7 | 59.4 | |||
1:2:3 | 0.15 | 0.5 | 14.2 | 11.9 | 18.6 | 33.2 | 28.2 | 24.7 | 13.2 | 17.9 | 13.7 | 21.7 | 21.7 | 19.7 | 20.3 | 20.0 | 18.4 | |
0.8 | 43.6 | 41.4 | 48.3 | 58.9 | 81.3 | 29.0 | 43.4 | 45.4 | 36.3 | 53.1 | 52.3 | 44.8 | 51.5 | 51.1 | 44.4 | |||
0.95 | 83.0 | 82.9 | 83.9 | 97.1 | 98.8 | 36.0 | 85.6 | 79.6 | 82.6 | 85.9 | 84.3 | 79.9 | 85.4 | 84.9 | 80.6 | |||
0.3 | 0.5 | 8.6 | 7.2 | 10.3 | 36.2 | 25.0 | 18.7 | 7.9 | 10.8 | 7.6 | 12.4 | 12.2 | 10.5 | 11.9 | 11.7 | 9.8 | ||
0.8 | 29.7 | 27.4 | 30.1 | 26.1 | 57.9 | 25.9 | 28.9 | 31.9 | 22.7 | 35.0 | 33.6 | 28.2 | 34.4 | 33.7 | 29.5 | |||
0.95 | 62.4 | 61.8 | 62.8 | 85.5 | 95.3 | 36.9 | 66.4 | 63.0 | 60.9 | 65.8 | 64.5 | 62.3 | 66.3 | 65.9 | 64.0 | |||
60 | 1:1:1 | 0.15 | 0.5 | 19.0 | 19.0 | 18.4 | 33.7 | 47.5 | 24.3 | 10.7 | 11.3 | 14.2 | 29.3 | 29.4 | 28.3 | 28.0 | 28.1 | 27.2 |
0.8 | 65.8 | 67.8 | 59.6 | 85.2 | 92.4 | 38.9 | 55.0 | 56.3 | 48.7 | 71.4 | 71.4 | 71.4 | 71.1 | 71.1 | 70.7 | |||
0.95 | 97.9 | 98.6 | 96.6 | 96.6 | 96.7 | 50.3 | 95.9 | 96.7 | 89.3 | 97.7 | 97.7 | 97.7 | 97.7 | 97.7 | 97.7 | |||
0.3 | 0.5 | 9.7 | 9.4 | 9.5 | 43.2 | 21.5 | 16.9 | 4.7 | 5.3 | 4.2 | 17.0 | 17.2 | 15.3 | 14.1 | 14.3 | 13.1 | ||
0.8 | 46.5 | 47.1 | 39.8 | 54.6 | 79.8 | 33.2 | 35.4 | 36.9 | 37.1 | 50.9 | 50.9 | 50.8 | 49.7 | 50.3 | 50.0 | |||
0.95 | 91.3 | 93.3 | 85.7 | 95.3 | 96.0 | 47.0 | 85.9 | 87.8 | 71.3 | 88.0 | 88.0 | 88.0 | 89.6 | 89.3 | 89.5 | |||
1:2:3 | 0.15 | 0.5 | 20.5 | 20.2 | 22.2 | 29.6 | 53.8 | 27.9 | 24.1 | 22.1 | 24.2 | 31.0 | 30.8 | 28.3 | 31.7 | 31.1 | 28.2 | |
0.8 | 72.5 | 72.5 | 69.1 | 92.3 | 96.4 | 40.9 | 73.9 | 73.3 | 79.2 | 77.0 | 76.9 | 75.9 | 78.3 | 78.1 | 76.7 | |||
0.95 | 98.6 | 98.6 | 98.0 | 99.9 | 99.9 | 50.6 | 98.6 | 98.9 | 92.4 | 99.1 | 99.0 | 99.0 | 98.5 | 98.5 | 98.4 | |||
0.3 | 0.5 | 10.3 | 10.0 | 10.3 | 42.9 | 25.0 | 20.1 | 12.3 | 10.3 | 10.1 | 15.8 | 15.7 | 13.5 | 15.8 | 15.4 | 13.4 | ||
0.8 | 49.3 | 49.2 | 45.1 | 64.6 | 84.1 | 36.2 | 52.0 | 48.4 | 38.0 | 52.0 | 52.0 | 51.0 | 55.5 | 55.4 | 54.1 | |||
0.95 | 90.4 | 90.3 | 88.7 | 99.1 | 99.4 | 51.3 | 90.9 | 92.5 | 82.4 | 92.1 | 92.0 | 92.0 | 91.8 | 91.7 | 91.7 |
Real data example
Various p -values for the pharmacological data set at the nominal level α =5 %
θ=0.6 | θ=0.8 | ||||||
---|---|---|---|---|---|---|---|
Test method | T _{ W } | T _{ R } | T _{ L } | T _{ W } | T _{ R } | T _{ L } | |
AM | 0.173 | 0.162 | 0.164 | 0.234 | 0.229 | 0.230 | |
SAM | 0.494 | 0.494 | 0.140 | 0.497 | 0.497 | 0.162 | |
EUM | 0.185 | 0.181 | 0.192 | 0.233 | 0.202 | 0.210 | |
AUM | 0.166 | 0.165 | 0.186 | 0.232 | 0.230 | 0.249 | |
BTM | 0.504 | 0.502 | 0.519 | 0.516 | 0.514 | 0.530 |
Discussion
Computing time (minutes) of the Type I error rates for 11340 configurations of ( π _{ P } , π _{ R } , π _{ T } ) together with three test statistics under five test methods
λ _{ P }:λ _{ R }:λ _{ T } | θ | n | AM | SAM | EUM | AUM | BTM |
---|---|---|---|---|---|---|---|
1:2:3 | 0.6 | 30 | 3.3 | 269 | 2920 | 55.75 | 11700 |
60 | 3.8 | 356 | 130950 | 357.3 | 20700 |
In this article, we concentrate on a three-arm non-inferiority trial with binary endpoints in which the marginal is defined as a fraction of the unknown difference in response probabilities between reference and placebo. The corresponding hypothesis (i.e., ${H}_{0}:\frac{{\pi}_{T}-{\pi}_{P}}{{\pi}_{R}-{\pi}_{P}}\le \theta $ or H _{0}:π _{ T }−θ π _{ R }−(1−θ)π _{ P }≤0) is considered since it is simple and only one single hypothesis is involved (e.g., see [6, 9, 14]). However, three-arm non-inferiority hypotheses with the marginal defined as the prespecified difference between treatments have received a considerable attention in recent years (e.g., see [5, 7]). They can be generally classified as the union type hypotheses (i.e., H _{ U0}: π _{ R }≥h _{ P }(π _{ P }) or π _{ R }≥h _{ T }(π _{ T })) or the intersection type hypotheses (i.e., H _{ U0}: π _{ R }≥h _{ P }(π _{ P }) and π _{ R }≥h _{ T }(π _{ T })), where h _{ P }(.) and h _{ T }(.) are any functions [15]. For specific choices of h _{ P }(.) and h _{ R }(.), this includes, for examples, hypotheses on the differences, the relative risks or the odds ratio of the proportions. While the union type hypotheses are suitable for showing both the superiority of the standard treatment as compared to placebo and the inferiority of the test treatment as compared to the standard treatment, the intersection type hypotheses are suitable for showing the test treatment is as effective as the standard or placebo treatments. We are working on statistical inference on a three-arm non-inferiority trial with the margin being a prespecifided difference between treatments when the primary endpoints are binary.
Conclusions
According to the aforementioned observations, we can draw the following conclusions. In terms of type I error rates and powers, the approximate unconditional and bootstrap-resampling methods with score test statistic are recommended for hypothesis testing purpose when sample sizes are small in a three-arm non-inferiority trial. In terms of time-consuming and type I error rates and powers, the approximate unconditional method with score test statistic behaves the best among our considered p-value calculation procedures and test statistics.
Declarations
Acknowledgements
This work was supported by the grants from the National Science Foundation of China (11225103), and Research Fund for the Doctoral Program of Higher Education of China (20115301110004). The work of the third author was partially supported by the General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS14/P01/14).
Authors’ Affiliations
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