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1.
Multi‐country randomised clinical trials (MRCTs) are common in the medical literature, and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in an MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over‐interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this paper, we consider some exploratory analysis tools to address this issue. We present three measures derived using the theory of order statistics, which can be used to understand the magnitude and the nature of the variation in treatment effects that can arise merely as an artefact of chance. These measures are not intended to replace a formal test of interaction but instead provide non‐inferential visual aids, which allow comparison of the observed and expected differences between regions or other subgroups and are a useful supplement to a formal test of interaction. We discuss how our methodology differs from recently published methods addressing the same issue. A case study of our approach is presented using data from the Study of Platelet Inhibition and Patient Outcomes (PLATO), which was a large cardiovascular MRCT that has been the subject of controversy in the literature. An R package is available that implements the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
2.
Michael Kunz 《Pharmaceutical statistics》2015,14(1):34-43
In this paper, three analysis procedures for repeated correlated binary data with no a priori ordering of the measurements are described and subsequently investigated. Examples for correlated binary data could be the binary assessments of subjects obtained by several raters in the framework of a clinical trial. This topic is especially of relevance when success criteria have to be defined for dedicated imaging trials involving several raters conducted for regulatory purposes. First, an analytical result on the expectation of the ‘Majority rater’ is presented when only the marginal distributions of the single raters are given. The paper provides a simulation study where all three analysis procedures are compared for a particular setting. It turns out that in many cases, ‘Average rater’ is associated with a gain in power. Settings were identified where ‘Majority significant’ has favorable properties. ‘Majority rater’ is in many cases difficult to interpret. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
3.
Feng-shou Ko 《统计学通讯:理论与方法》2013,42(20):3648-3665
A bridging study defined by ICH E5 is usually conducted in the new region after the test product has been approved for commercial marketing in the original region due to its proven efficacy and safety. However, extensive duplication of clinical evaluation in the new region not only requires valuable development resources but also delay availability of the test product to the needed patients in the new regions. To shorten the drug lag or the time lag for approval, simultaneous drug development, submission, and approval in the world may be desirable. Recently, multi-regional trials have attracted much attention from sponsors as well as regulatory authorities. On September 28, 2007, the Ministry of Health, Labour and Welfare (MHLW) in Japan published the “Basic Principles on Global Clinical Trials” guidance related to the planning and implementation of global clinical studies. The 11th Q & A for the ICH E5 guideline also comments the concept of a multi-regional trial. Both guidelines have established a framework on how to demonstrate the efficacy of a drug in all participating regions while also evaluating the possibility of applying the overall trial results to each region by conducting a multi-regional trial. Kawai et al. (2008) developed an approach to rationalize partitioning the total sample size among the regions so that a high probability of observing a consistent trend under the assumptions of the positive treatment effect and uniform across regions in a confirmatory multi-regional trial. Ko et al. (2010) focused on a specific region and establish statistical criteria for consistency between the region of interest and overall results. The sample size calculation for a specific region was also provided. These methods were based on the assumption that true effect size is uniform across regions. In this article, we address the issue that the treatment effects are different among regions to design a multi-regional trial. The random effect model is employed to deal with the heterogeneous effect size among regions. The test statistic for the overall treatment effect is also established and the consistent trend and the proposed criteria are used to rationalize partition sample size to each region. 相似文献
4.
Cornelia Ursula Kunz Tim Friede Nick Parsons Susan Todd Nigel Stallard 《Pharmaceutical statistics》2014,13(4):238-246
Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow‐up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods. © 2014 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd. 相似文献
5.
In this note, we highlight the fact that the choice of type I and type II error rates should not simply be set at traditional levels in the phase II clinical trial setting when considering the relative success rate of previous trials in a given disease setting. For diseases in which it is rare that a new compound is active, we argue that more stringent type I error rates in the phase II setting may be more important relative to relaxing the type II error rates. The paper itself is more of a 'thought' experiment on this topic such that specific clinical trial settings will require specific applications of this approach. This is due in part to the fact that the real-world setting is more complex relative to overall decision process in terms of moving from phase II to phase III trials than our basic illustrative model. 相似文献
6.
Song JX 《Pharmaceutical statistics》2006,5(4):295-304
In the longitudinal studies with binary response, it is often of interest to estimate the percentage of positive responses at each time point and the percentage of having at least one positive response by each time point. When missing data exist, the conventional method based on observed percentages could result in erroneous estimates. This study demonstrates two methods of using expectation-maximization (EM) and data augmentation (DA) algorithms in the estimation of the marginal and cumulative probabilities for incomplete longitudinal binary response data. Both methods provide unbiased estimates when the missingness mechanism is missing at random (MAR) assumption. Sensitivity analyses have been performed for cases when the MAR assumption is in question. 相似文献
7.
Patient heterogeneity may complicate dose‐finding in phase 1 clinical trials if the dose‐toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively,it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem,we consider a generalization of the continual reassessment method on the basis of a hierarchical Bayesian dose‐toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup‐specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to 3 alternative approaches,on the basis of nonhierarchical models,that make different types of assumptions about within‐subgroup dose‐toxicity curves. The simulations show that the hierarchical model‐based method is recommended in settings where the dose‐toxicity curves are exchangeable between subgroups. We present practical guidelines for application and provide computer programs for trial simulation and conduct. 相似文献
8.
Jianrong Wu 《Pharmaceutical statistics》2015,14(3):226-232
The current practice of designing single‐arm phase II survival trials is limited under the exponential model. Trial design under the exponential model may not be appropriate when a portion of patients are cured. There is no literature available for designing single‐arm phase II trials under the parametric cure model. In this paper, a test statistic is proposed, and a sample size formula is derived for designing single‐arm phase II trials under a class of parametric cure models. Extensive simulations showed that the proposed test and sample size formula perform very well under different scenarios. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
9.
Stephan Koehne‐Voss Heinz Schmidli David M. Smith Iris Pigeot 《Pharmaceutical statistics》2011,10(1):45-49
For first‐time‐in‐human studies with small molecules alternating cross‐over designs are often employed and at study end are analyzed using linear models. We discuss the impact of including a period effect in the model on the precision with which dose level contrasts can be estimated and quantify the bias of least squares estimators if a period effect is inherent in the data that is not accounted for in the model. We also propose two alternative designs that allow a more precise estimation of dose level contrasts compared with the standard design when period effects are included in the model. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
10.
In this note we outline 15 years of Gynecologic Oncology Group (GOG) experience conducting a series of phase II second-line intraperitoneal trials in the treatment of ovarian cancer. Using this information, the goal is to define a new permutation approach to historical control phase II trials in ovarian cancer. We utilize seven previous phase II GOG trials in our database to illustrate our methodology. 相似文献
11.
Phase II clinical trials are usually designed to measure efficacy, but safety is also an important end point. Previous authors recommended a method to monitor toxic events after each patient is enrolled, which is also known as continuously monitoring the toxicity. In this work, we investigate combining the usual Simon two-stage design to monitor response with the continuous toxicity monitoring methodology. Theoretical justification is given for the nominal size, probability of early termination, and average sample size under the null hypothesis of the combined testing procedure. A series of simulations are performed to investigate the performance of the combined procedure. 相似文献
12.
Dropout is a persistent problem for a longitudinal study. We exhibit the shortcomings of the last observation carried forward method. It produces biased estimates of change in an outcome from baseline to study endpoint under informative dropout. We developed a theoretical quantification of the effect of such bias on type I and type II error rates. We present results for a setup where a subject either completes the study or drops out during one particular interval, and also under the setup in which subjects could drop out at any time during the study. The type I error rate steadily increases when time to dropout decreases or the common sample size increases. The inflation in type I error rate can be substantially high when reasons for dropout in the two groups differ; when there is a large difference in dropout rates between the control and treatment groups and when the common sample size is large; even when dropout subjects have one or two fewer observations than the completers. Similar results are also observed for type II error rates. A study can have very low power when early recovered patients in the treatment group and worsening patients in the control group drop out even near the end of the study. 相似文献
13.
14.
Ian Barton 《Pharmaceutical statistics》2004,3(3):205-212
There is debate within the osteoporosis research community about the relationship between the risk of osteoporotic fracture and the surrogate measures of fracture risk. Meta‐regression analyses based on summary data have shown a linear relationship between fracture risk and surrogate measures, whereas analyses based on individual patient data (IPD) have shown a nonlinear relationship. We investigated the association between changes in a surrogate measure of fracture incidence, in this case a bone turnover marker for resorption assessed in the three risedronate phase III clinical programmes, and incident osteoporosis‐related fracture risk using regression models based on patient‐level and trial‐level information. The relationship between osteoporosis‐related fracture risk and changes in bone resorption was different when analysed on the basis of IPD than when analysed on the basis of a meta‐analytic approach (i.e., meta‐regression) using summary data (e.g., treatment effect based on treatment group estimates). This inconsistency in our findings was consistent with those in the published literature. Meta‐regression based on summary statistics at the trial level is not expected to reflect causal relationships between a clinical outcome and surrogate measures. Analyses based on IPD make possible a more comprehensive analysis since all relevant data on a patient level are available. Copyright © 2004 John Wiley & Sons Ltd. 相似文献
15.
Qizhai Li 《Pharmaceutical statistics》2011,10(3):277-279
Two‐stage design is very useful in clinical trials for evaluating the validity of a specific treatment regimen. When the second stage is allowed to continue, the method used to estimate the response rate based on the results of both stages is critical for the subsequent design. The often‐used sample proportion has an evident upward bias. However, the maximum likelihood estimator or the moment estimator tends to underestimate the response rate. A mean‐square error weighted estimator is considered here; its performance is thoroughly investigated via Simon's optimal and minimax designs and Shuster's design. Compared with the sample proportion, the proposed method has a smaller bias, and compared with the maximum likelihood estimator, the proposed method has a smaller mean‐square error. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
16.
James Matcham Steven Julious Stephen Pyke Michael O'Kelly Susan Todd Jorgen Seldrup Simon Day 《Pharmaceutical statistics》2011,10(1):70-73
In this paper we set out what we consider to be a set of best practices for statisticians in the reporting of pharmaceutical industry‐sponsored clinical trials. We make eight recommendations covering: author responsibilities and recognition; publication timing; conflicts of interest; freedom to act; full author access to data; trial registration and independent review. These recommendations are made in the context of the prominent role played by statisticians in the design, conduct, analysis and reporting of pharmaceutical sponsored trials and the perception of the reporting of these trials in the wider community. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
17.
Steven Sun Grace Liu Tianmeng Lyu Fubo Xue Tzu‐Min Yeh Sudhakar Rao 《Pharmaceutical statistics》2018,17(2):94-104
For clinical trials with time‐to‐event as the primary endpoint, the clinical cutoff is often event‐driven and the log‐rank test is the most commonly used statistical method for evaluating treatment effect. However, this method relies on the proportional hazards assumption in that it has the maximal power in this circumstance. In certain disease areas or populations, some patients can be curable and never experience the events despite a long follow‐up. The event accumulation may dry out after a certain period of follow‐up and the treatment effect could be reflected as the combination of improvement of cure rate and the delay of events for those uncurable patients. Study power depends on both cure rate improvement and hazard reduction. In this paper, we illustrate these practical issues using simulation studies and explore sample size recommendations, alternative ways for clinical cutoffs, and efficient testing methods with the highest study power possible. 相似文献
18.
Often, single‐arm trials are used in phase II to gather the first evidence of an oncological drug's efficacy, with drug activity determined through tumour response using the RECIST criterion. Provided the null hypothesis of ‘insufficient drug activity’ is rejected, the next step could be a randomised two‐arm trial. However, single‐arm trials may provide a biased treatment effect because of patient selection, and thus, this development plan may not be an efficient use of resources. Therefore, we compare the performance of development plans consisting of single‐arm trials followed by randomised two‐arm trials with stand‐alone single‐stage or group sequential randomised two‐arm trials. Through this, we are able to investigate the utility of single‐arm trials and determine the most efficient drug development plans, setting our work in the context of a published single‐arm non‐small‐cell lung cancer trial. Reference priors, reflecting the opinions of ‘sceptical’ and ‘enthusiastic’ investigators, are used to quantify and guide the suitability of single‐arm trials in this setting. We observe that the explored development plans incorporating single‐arm trials are often non‐optimal. Moreover, even the most pessimistic reference priors have a considerable probability in favour of alternative plans. Analysis suggests expected sample size savings of up to 25% could have been made, and the issues associated with single‐arm trials avoided, for the non‐small‐cell lung cancer treatment through direct progression to a group sequential randomised two‐arm trial. Careful consideration should thus be given to the use of single‐arm trials in oncological drug development when a randomised trial will follow. Copyright © 2015 The Authors. Pharmaceutical Statistics published by JohnWiley & Sons Ltd. 相似文献
19.
A complete two‐period experimental design has been defined as one in which subjects are randomized to treatment, observed for the occurrence of an event of interest, re‐randomized, and observed again for the event in a second period. A 4‐year vaccine efficacy trial was planned to compare a high‐dose vaccine with a standard dose vaccine. Subjects would be randomized each year, and subjects who had participated in a previous year would be allowed to re‐enroll in a subsequent year and would be re‐randomized. A question of interest is whether positive correlation between observations on subjects who re‐enrolled would inflate the variance of test statistics. The effect of re‐enrollment and correlation on type 1 error in a 4‐year trial is investigated by simulation. As conducted, the trial met its power requirements after two years. Subjects therefore included some who participated for a single year and others who participated in both years. Those who participated in both years constituted a complete two‐period design. An algebraic expression for the variance of the treatment difference in a complete two‐period design is derived. It is shown that under a ‘no difference’ null, correlation does not result in variance inflation in this design. When there is a treatment difference, there is variance inflation but it is small. In the vaccine efficacy trial, the effect of correlation on the statistical inference was negligible. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
20.
Takanori Tanase 《Pharmaceutical statistics》2020,19(2):126-136
Progression‐free survival is recognized as an important endpoint in oncology clinical trials. In clinical trials aimed at new drug development, the target population often comprises patients that are refractory to standard therapy with a tumor that shows rapid progression. This situation would increase the bias of the hazard ratio calculated for progression‐free survival, resulting in decreased power for such patients. Therefore, new measures are needed to prevent decreasing the power in advance when estimating the sample size. Here, I propose a novel calculation procedure to assume the hazard ratio for progression‐free survival using the Cox proportional hazards model, which can be applied in sample size calculation. The hazard ratios derived by the proposed procedure were almost identical to those obtained by simulation. The hazard ratio calculated by the proposed procedure is applicable to sample size calculation and coincides with the nominal power. Methods that compensate for the lack of power due to biases in the hazard ratio are also discussed from a practical point of view. 相似文献
