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The sample size of a clinical trial has to be large enough to ensure sufficient power for achieving the aim the study. On the other side, for ethical and economical reasons it should not be larger than necessary. The sample size allocation is one of the parameters that influences the required total sample size. For two-arm superiority and non-inferiority trials with binary endpoints, we performed extensive computations over a wide range of scenarios to determine the optimal allocation ratio that minimizes the total sample size if all other parameters are fixed. The results demonstrate, that for both superiority and non-inferiority trials the optimal allocation may deviate considerably from the case of equal sample size in both groups. However, the saving in sample size when allocating the total sample size optimally as compared to balanced allocation is typically small., (© 2024 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Due to the dependency structure in the sampling process, adaptive trial designs create challenges in point and interval estimation and in the calculation of P-values. Optimal adaptive designs, which are designs where the parameters governing the adaptivity are chosen to maximize some performance criterion, suffer from the same problem. Various analysis methods which are able to handle this dependency structure have already been developed. In this work, we aim to give a comprehensive summary of these methods and show how they can be applied to the class of designs with planned adaptivity, of which optimal adaptive designs are an important member. The defining feature of these kinds of designs is that the adaptive elements are completely prespecified. This allows for explicit descriptions of the calculations involved, which makes it possible to evaluate different methods in a fast and accurate manner. We will explain how to do so, and present an extensive comparison of the performance characteristics of various estimators between an optimal adaptive design and its group-sequential counterpart., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

If design parameters are chosen appropriately, group sequential trial designs are known to be able to reduce the expected sample size under the alternative hypothesis compared to single-stage designs. The same holds true for the so-called 'gold-standard' design for non-inferiority trials, a design involving an experimental group, an active control group, and a placebo group. However, choosing design parameters that maximize the advantages of a two-stage approach for the three-arm gold-standard design for non-inferiority trials is not a straightforward task. In particular, optimal choices of futility boundaries for this design have not been thoroughly discussed in existing literature. We present a variation of the hierarchical testing procedure, which allows for the incorporation of binding futility boundaries at interim analyses. We show that this procedure maintains strong control of the family-wise type I error rate. Within this framework, we consider the futility and efficacy boundaries as well as the sample size allocation ratios as optimization parameters. This allows the investigation of the efficiency gain from including the option to stop for futility in addition to the ability to stop for efficacy. To analyze the extended designs, optimality criteria that include the design's performance under the alternative as well as the null hypothesis are introduced. On top of this, we discuss methods to limit the allocation of placebo patients in the trial while maintaining relatively good operating characteristics. The results of our numerical optimization procedure are discussed and a comparison of different approaches to designing a three-arm gold-standard non-inferiority trial is provided., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Adaptive planning of clinical trials allows modifying the entire trial design at any time point mid-course. In this paper, we consider the case when a trial-external update of the planning assumptions during the ongoing trial makes an unforeseen design adaptation necessary. We take up the idea to construct adaptive designs with defined features by solving an optimization problem and apply it to the situation of unplanned design reassessment. By using the conditional error principle, we present an approach on how to optimally modify the trial design at an unplanned interim analysis while at the same time strictly protecting the type I error rate. This linking of optimal design planning and the conditional error principle allows sound reactions to unforeseen events that make a design reassessment necessary., (© 2022 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

In early clinical development, randomized controlled trials (RCT) or single-arm trials with external controls (SATwEC) are design options, which allow adjustment for confounding: RCT via design, SATwEC via analysis using propensity score methods. SATwEC requires less investment than RCT. However, if the confounder space substantially differs between the experimental and external control group, the SATwEC might lead to inappropriate decisions for further development. We develop an adaptive two-stage design (ATD) for early clinical development that reduces the risk of unreliable decision-making at the end of a SATwEC. In Stage I, subjects are solely assigned to the experimental group. If at the interim the propensity score distributions of internal and external data are comparable based on the preference score, the subjects in stage II will again be solely assigned to the experimental arm; if not, a randomized stage II will be conducted. In a simulation study guided by a motivating example, data is generated using a time-to-event model with observable and unobservable confounders. The confounder space is varied to investigate the impact on false go/stop probabilities as well as a loss function, which reflects the quality of treatment effect estimates and decision-making. The proposed ATD provides a compromise between optimizing quality (as expressed by false go/stop probabilities and the loss function) and investment (defined by sample size and trial duration)., (© 2022 John Wiley & Sons Ltd.)

Sample size calculations in clinical trials need to be based on profound parameter assumptions. Wrong parameter choices may lead to too small or too high sample sizes and can have severe ethical and economical consequences. Adaptive group sequential study designs are one solution to deal with planning uncertainties. Here, the sample size can be updated during an ongoing trial based on the observed interim effect. However, the observed interim effect is a random variable and thus does not necessarily correspond to the true effect. One way of dealing with the uncertainty related to this random variable is to include resampling elements in the recalculation strategy. In this paper, we focus on clinical trials with a normally distributed endpoint. We consider resampling of the observed interim test statistic and apply this principle to several established sample size recalculation approaches. The resulting recalculation rules are smoother than the original ones and thus the variability in sample size is lower. In particular, we found that some resampling approaches mimic a group sequential design. In general, incorporating resampling of the interim test statistic in existing sample size recalculation rules results in a substantial performance improvement with respect to a recently published conditional performance score., (© 2021 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Adaptive designs are playing an increasingly important role in the planning of clinical trials. While there exists various research on the optimal determination of a two-stage design, non-optimal versions still are frequently applied in clinical research. In this article, we strive to motivate the application of optimal adaptive designs and give guidance on how to determine them. It is demonstrated that optimizing a trial design with respect to particular objective criteria can have a substantial benefit over the application of conventional adaptive sample size recalculation rules. Furthermore, we show that in many practical situations, optimal group-sequential designs show an almost negligible performance loss compared to optimal adaptive designs. Finally, we illustrate how optimal designs can be tailored to specific operational requirements by customizing the underlying optimization problem., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

In clinical development, there is a trade-off between investment and level of confidence in the potential of the drug before going into phase III. Reduced investment requires the use of short-term endpoints. On new compounds, only limited information about the relationship between treatment effects of short- and long-term endpoints is usually available. Therefore, decision-making solely based on short-term endpoints does not seem desirable. Our goal is to plan an efficient development program, which uses short- and long-term endpoints data for decision-making. We found that with limited prior information and restrictions on maximum sample size, decision-making after phase II cannot be substantially improved. We follow the concept of a "phase 2+" design where after a go-to-phase-III-decision, further follow-up data from phase II are employed to make interim decisions on phase III. The program will be stopped early when additional phase II and/or available phase III data lead to a low probability of success (PoS). We utilize information from a multi-categorical short-term endpoint (response status) and a long-term endpoint (overall survival (OS)) to determine the PoS in phase III with OS as the primary endpoint. Optimal combinations of decision boundaries and time points are demonstrated in a simulation study. Our results show that the proposed second look using additional follow-up data from phase II/III improves PoS estimates compared to the first look, especially when prior data about the control arm is available. The proposed planning strategy allows a customized compromise between the quality of decision-making and program duration., (© 2020 John Wiley & Sons Ltd.)

The feasibility of a new clinical trial may be increased by incorporating historical data of previous trials. In the particular case where only data from a single historical trial are available, there exists no clear recommendation in the literature regarding the most favorable approach. A main problem of the incorporation of historical data is the possible inflation of the type I error rate. A way to control this type of error is the so-called power prior approach. This Bayesian method does not "borrow" the full historical information but uses a parameter 0 ≤ δ ≤ 1 to determine the amount of borrowed data. Based on the methodology of the power prior, we propose a frequentist framework that allows incorporation of historical data from both arms of two-armed trials with binary outcome, while simultaneously controlling the type I error rate. It is shown that for any specific trial scenario a value δ > 0 can be determined such that the type I error rate falls below the prespecified significance level. The magnitude of this value of δ depends on the characteristics of the data observed in the historical trial. Conditionally on these characteristics, an increase in power as compared to a trial without borrowing may result. Similarly, we propose methods how the required sample size can be reduced. The results are discussed and compared to those obtained in a Bayesian framework. Application is illustrated by a clinical trial example., (© 2020 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

In standard clinical trial designs, the required sample size is fixed in the planning stage based on initial parameter assumptions. It is intuitive that the correct choice of the sample size is of major importance for an ethical justification of the trial. The required parameter assumptions should be based on previously published results from the literature. In clinical practice, however, historical data often do not exist or show highly variable results. Adaptive group sequential designs allow a sample size recalculation after a planned unblinded interim analysis in order to adjust the sample size during the ongoing trial. So far, there exist no unique standards to assess the performance of sample size recalculation rules. Single performance criteria commonly reported are given by the power and the average sample size; the variability of the recalculated sample size and the conditional power distribution are usually ignored. Therefore, the need for an adequate performance score combining these relevant performance criteria is evident. To judge the performance of an adaptive design, there exist two possible perspectives, which might also be combined: Either the global performance of the design can be addressed, which averages over all possible interim results, or the conditional performance is addressed, which focuses on the remaining performance conditional on a specific interim result. In this work, we give a compact overview of sample size recalculation rules and performance measures. Moreover, we propose a new conditional performance score and apply it to various standard recalculation rules by means of Monte-Carlo simulations., (© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.)

Recalculating the sample size in adaptive two-stage designs is a well-established method to gain flexibility in a clinical trial. Jennison and Turnbull (2015) proposed an "optimal" adaptive two-stage design based on the inverse normal combination test, which minimizes a mixed criterion of expected sample size under the alternative and conditional power. We demonstrate that the use of a combination test is not necessary to control the type one error rate and use variational techniques to develop a general adaptive design that is globally optimal under predefined optimality criteria. This approach yields to more efficient designs and furthermore allows to investigate the efficiency of the inverse normal method and the relation between local (interim-based) recalculation rules and global (unconditional) optimality of adaptive two-stage designs., (© 2019 John Wiley & Sons, Ltd.)

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data-generating models (DGMs) are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multiarm trials with binary outcome. The only one of the common DGMs used in the pairwise case, which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example., (© 2019 John Wiley & Sons, Ltd.)

Biostatisticians play an important role in medical research. They are co-responsible for an appropriate and efficient study design, they are involved in monitoring the study conduct, they plan and perform the data analysis, and they are involved in interpreting and publishing the results. However, how are the biostatisticians prepared for their tasks and responsibilities? Graduate programs in biostatistics are being offered, but some practicing biostatisticians completed their studies in a mathematical or epidemiological program, or obtained their degree in subject-specific fields (such as medicine or biology). Therefore, the expertise and the competencies can vary widely between the individual biostatisticians, also depending on the application field. In this article, focusing on European and US practices, we discuss the required professional expertise for the main areas of applications in the medical field as well as the necessary soft skill competencies of a biostatistician., (© 2018 John Wiley & Sons, Ltd.)

Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the "all-or-none" and "at-least-one" win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology., (Copyright © 2018 John Wiley & Sons, Ltd.)

Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all-cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all-cause hazard within each group is given by the sum of the cause-specific hazards corresponding to the individual components. A natural extension of the standard all-cause hazard ratio can be defined by a "weighted all-cause hazard ratio" where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all-cause hazard ratio then corresponds to the standard all-cause hazard ratio. To identify the cause-specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all-cause hazard ratio by theoretical considerations, Monte-Carlo simulations, and by means of a real clinical trial example., (Copyright © 2017 John Wiley & Sons, Ltd.)

Background and Purpose: Intravenous thrombolysis (IVT) is the only approved pharmacological treatment for acute ischemic stroke. Off-label IVT for ischemic stroke is common. We aimed to analyse its safety in a large database., Methods: This was a retrospective analysis of the safe implementation of treatments in stroke (SITS) thrombolysis registry with regard to 11 off-label criteria according to the European licence for alteplase. Symptomatic intracranial haemorrhage (SICH) according to SITS was defined as primary safety endpoint and SICH according to the European Cooperative Acute Stroke Study (ECASS II) definition and the National Institute of Neurological Disorders and Stroke definition as secondary safety endpoints. Multivariable logistic regression analyses after replacing missing values using multiple imputations were performed., Results: Patients from 793 centres in 44 countries were included, mainly (95%) in Europe. A total of 56 258 patients who were treated with intravenous alteplase were included. Median age was 71 (IQR 61-78) years and median National Institutes of Health Stroke Scale score was 12 (IQR 7-17). A total of 16 740 (30%) patients received off-label IVT and 1037 (1.8%) patients suffered from SICH according to the SITS definition (SICH SITS). Median percentage of missing values per variable was 0.4%. The only two off-label criteria constituting independent positive and negative predictors for SICH SITS were high blood pressure (odds ratio, 1.39; 95% confidence interval, 1.08-1.80; P = 0.012) and minor stroke (odds ratio, 0.51; 95% confidence interval, 0.33-0.78; P = 0.002). Very severe stroke, previous stroke and diabetes, age and high glucose levels were additional independent predictors of SICH according to the ECASS II and National Institute of Neurological Disorders and Stroke definitions., Conclusions: Thrombolysis appears to be safe with regard to SICH for most of the off-label criteria, especially for minor stroke, but is risky in patients with high blood pressure. Individual risk-benefit evaluation should be performed., (© 2017 EAN.)

Adaptive enrichment designs are an attractive option for clinical trials that aim at demonstrating efficacy of therapies, which may show different benefit for the full patient population and a prespecified subgroup. In these designs, based on interim data, either the subgroup or the full population is selected for further exploration. When selection is based on efficacy data, this introduces bias to the commonly used maximum likelihood estimator. For the situation of two-stage designs with a single prespecified subgroup, we present six alternative estimators and investigate their performance in a simulation study. The most consistent reduction of bias over the range of scenarios considered was achieved by a method combining the uniformly minimum variance conditionally unbiased estimator with a conditional moment estimator. Application of the methods is illustrated by a clinical trial example., (Copyright © 2017 John Wiley & Sons, Ltd.)

As part of the evaluation of phase II trials, it is common practice to perform exploratory subgroup analyses with the aim of identifying patient populations with a beneficial treatment effect. When investigating targeted therapies, these subgroups are typically defined by biomarkers. Promising results may lead to the decision to select the respective subgroup as the target population for a subsequent phase III trial. However, a selection based on a large observed treatment effect may potentially induce an upwards-bias leading to over-optimistic expectations on the success probability of the phase III trial. We describe how Approximate Bayesian Computation techniques can be used to derive a simulation-based bias adjustment method in this situation. Recommendations for the implementation of the approach are given. Simulation studies show that the proposed method reduces bias substantially compared with the maximum likelihood estimator. The procedure is illustrated with data from an oncology trial. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Clinical trials in phase II of drug development are frequently conducted as single-arm two-stage studies with a binary endpoint. Recently, adaptive designs have been proposed for this setting that enable a midcourse modification of the sample size. While these designs are elaborated with respect to hypothesis testing by assuring control of the type I error rate, the topic of point estimation has up to now not been addressed. For adaptive designs with a prespecified sample size recalculation rule, we propose a new point estimator that both assures compatibility of estimation and test decision and minimizes average mean squared error. This estimator can be interpreted as a constrained posterior mean estimate based on the non-informative Jeffreys prior. A comparative investigation of the operating characteristics demonstrates the favorable properties of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Phase II and phase III trials play a crucial role in drug development programs. They are costly and time consuming and, because of high failure rates in late development stages, at the same time risky investments. Commonly, sample size calculation of phase III is based on the treatment effect observed in phase II. Therefore, planning of phases II and III can be linked. The performance of the phase II/III program crucially depends on the allocation of the resources to phases II and III by appropriate choice of the sample size and the rule applied to decide whether to stop the program after phase II or to proceed. We present methods for a program-wise phase II/III planning that aim at determining optimal phase II sample sizes and go/no-go decisions in a time-to-event setting. Optimization is based on a utility function that takes into account (fixed and variable) costs of the drug development program and potential gains after successful launch. The proposed methods are illustrated by application to a variety of scenarios typically met in oncology drug development., (Copyright © 2015 John Wiley & Sons, Ltd.)

In recent years, high failure rates in phase III trials were observed. One of the main reasons is overoptimistic assumptions for the planning of phase III resulting from limited phase II information and/or unawareness of realistic success probabilities. We present an approach for planning a phase II trial in a time-to-event setting that considers the whole phase II/III clinical development programme. We derive stopping boundaries after phase II that minimise the number of events under side conditions for the conditional probabilities of correct go/no-go decision after phase II as well as the conditional success probabilities for phase III. In addition, we give general recommendations for the choice of phase II sample size. Our simulations show that unconditional probabilities of go/no-go decision as well as the unconditional success probabilities for phase III are influenced by the number of events observed in phase II. However, choosing more than 150 events in phase II seems not necessary as the impact on these probabilities then becomes quite small. We recommend considering aspects like the number of compounds in phase II and the resources available when determining the sample size. The lower the number of compounds and the lower the resources are for phase III, the higher the investment for phase II should be., (Copyright © 2015 John Wiley & Sons, Ltd.)

The topic of applying two-stage designs in the field of bioequivalence studies has recently gained attention in the literature and in regulatory guidelines. While there exists some methodological research on the application of group sequential designs in bioequivalence studies, implementation of adaptive approaches has focused up to now on superiority and non-inferiority trials. Especially, no comparison of the features and performance characteristics of these designs has been performed, and therefore, the question of which design to employ in this setting remains open. In this paper, we discuss and compare 'classical' group sequential designs and three types of adaptive designs that offer the option of mid-course sample size recalculation. A comprehensive simulation study demonstrates that group sequential designs can be identified, which show power characteristics that are similar to those of the adaptive designs but require a lower average sample size. The methods are illustrated with a real bioequivalence study example., (Copyright © 2015 John Wiley & Sons, Ltd.)

Phase II studies in oncology are frequently conducted as two-stage single-arm trials with a binary endpoint indicating tumor response. As a common feature of these designs, the sample sizes of the two stages and the decision rules for the interim and the final analysis have to be pre-specified and adhered to strictly during the course of the trial in order to assure control of the type I error rate. In practice, however, the attained sample sizes often deviate from the planned ones leading to the situation of overrunning or underrunning. The currently available approaches to deal with this problem are either based on assumptions that are rarely met in practice or do not guarantee that the significance level is kept. However, strict control of the type I error rate plays an important role also for single-arm cancer trials, as they are frequently a fundamental part of the registration information. We propose a general methodology that allows handling both unintentional and intentional overrunning and underrunning while strictly controlling the type I error rate. Application of the proposed procedure and some of its characteristics are illustrated with a real phase II oncology trial., (Copyright © 2015 John Wiley & Sons, Ltd.)

In drug development, bioequivalence studies are used to indirectly demonstrate clinical equivalence of a test formulation and a reference formulation of a specific drug by establishing their equivalence in bioavailability. These studies are typically run as crossover studies. In the planning phase of such trials, investigators and sponsors are often faced with a high variability in the coefficients of variation of the typical pharmacokinetic endpoints such as the area under the concentration curve or the maximum plasma concentration. Adaptive designs have recently been considered to deal with this uncertainty by adjusting the sample size based on the accumulating data. Because regulators generally favor sample size re-estimation procedures that maintain the blinding of the treatment allocations throughout the trial, we propose in this paper a blinded sample size re-estimation strategy and investigate its error rates. We show that the procedure, although blinded, can lead to some inflation of the type I error rate. In the context of an example, we demonstrate how this inflation of the significance level can be adjusted for to achieve control of the type I error rate at a pre-specified level. Furthermore, some refinements of the re-estimation procedure are proposed to improve the power properties, in particular in scenarios with small sample sizes., (Copyright © 2014 John Wiley & Sons, Ltd.)

Many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature. In case of binary or time-to-event variables, composite endpoints, which combine several event types within a single event or time-to-first-event analysis are often used to assess the overall treatment effect. A main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another. Recently, some authors proposed more general approaches based on a priority ranking of outcomes, which moreover allow to combine outcome variables of different scale levels. These new combined effect measures assign a higher impact to more important endpoints, which is meant to simplify the interpretation of results. Whereas statistical tests and models for binary and time-to-event variables are well understood, the latter methods have not been investigated in detail so far. In this paper, we will investigate the statistical properties of prioritized combined outcome measures. We will perform a systematical comparison to standard composite measures, such as the all-cause hazard ratio in case of time-to-event variables or the absolute rate difference in case of binary variables, to derive recommendations for different clinical trial scenarios. We will discuss extensions and modifications of the new effect measures, which simplify the clinical interpretation. Moreover, we propose a new method on how to combine the classical composite approach with a priority ranking of outcomes using a multiple testing strategy based on the closed test procedure., (Copyright © 2013 John Wiley & Sons, Ltd.)

This paper addresses statistical issues in non-inferiority trials where the primary outcome is a fatal event. The investigations are inspired by a recent Food and Drug Administration (FDA) draft guideline on treatments for nosocomial pneumonia. The non-inferiority margin suggested in this guideline for the endpoint all-cause mortality is defined on different distance measures (rate difference and odds ratio) and is discontinuous. Furthermore, the margin enables considerable power for the statistical proof of non-inferiority at alternatives that might be regarded as clinically unacceptable, that is, even if the experimental treatment is harmful as compared with the control. We investigated the appropriateness of the proposed non-inferiority margin as well as the performance of possible test statistics to be used for the analysis. A continuous variant of the margin proposed in the FDA guideline together with the unconditional exact test according to Barnard showed favorable characteristics with respect to type I error rate control and power. To prevent harmful new treatments from being declared as non-inferior, we propose to add a 'second hurdle'. We discuss examples and explore power characteristics when requiring both statistical significance and overcoming the second hurdle., (Copyright © 2012 John Wiley & Sons, Ltd.)

Background: The outcome of patients with locally advanced gastroesophageal adenocarcinoma (adenocarcinoma of the esophagus, gastroesophageal (GE) junction, and stomach) is poor. There is conflicting evidence regarding the effects of perioperative chemotherapy on survival and other outcomes., Objectives: To assess the effect of perioperative chemotherapy for gastroesophageal adenocarcinoma on survival and other clinically relevant outcomes in the overall population of participants in randomized controlled trials (RCTs) and in prespecified subgroups., Search Methods: We performed computerized searches in the Cochrane Central Register of Controlled Trials (CENTRAL), Database of Abstracts of Review of Effectiveness (DARE), the Cochrane Database of Systematic Reviews (CDSR) from The Cochrane Library, MEDLINE (1966 to May 2011), EMBASE (1980 to May 2011), and LILACS (Literatura Latinoamericana y del Caribe en Ciencias de la Salud), combining the Cochrane highly sensitive search strategy with specific search terms. Moreover, we handsearched several online databases, conference proceedings, and reference lists of retrieved papers., Selection Criteria: We included RCTs which randomized patients with gastroesophageal adenocarcinoma, in the absence of distant metastases, to receive either chemotherapy with or without radiotherapy followed by surgery, or surgery alone., Data Collection and Analysis: Two independent review authors identified eligible trials. We solicited individual patient data (IPD) from all selected trials. We performed meta-analyses based on intention-to-treat populations using the two-stage method to combine IPD with aggregate data from RCTs for which IPD were unavailable. We combined data from all trials providing IPD in a Cox proportional hazards model to assess the effect of several covariables on overall survival., Main Results: We identified 14 RCTs with 2422 eligible patients. For eight RCTs with 1049 patients (43.3%), we were able to obtain IPD. Perioperative chemotherapy was associated with significantly longer overall survival (hazard ratio (HR) 0.81; 95% confidence interval (CI) 0.73 to 0.89). This corresponds to a relative survival increase of 19% or an absolute survival increase of 9% at five years. This survival advantage was consistent across most subgroups. There was a trend towards a more pronounced treatment effect for tumors of the GE junction compared to other sites, and for combined chemoradiotherapy as compared to chemotherapy in tumors of the esophagus and GE junction. Resection with negative margins was a strong predictor of survival. Multivariable analysis showed that tumor site, performance status, and age have an independent significant effect on survival. Moreover, there was a significant interaction of the effect of perioperative chemotherapy with age (larger treatment effect in younger patients). Perioperative chemotherapy also showed a significant effect on several secondary outcomes. It was associated with longer disease-free survival, higher rates of R0 resection, and more favorable tumor stage upon resection, while there was no association with perioperative morbidity and mortality., Authors' Conclusions: Perioperative chemotherapy for resectable gastroesophageal adenocarcinoma increases survival compared to surgery alone. It should thus be offered to all eligible patients. There is a trend to a larger survival advantage for tumors of the GE junction as compared to other sites and for chemoradiotherapy as compared to chemotherapy in esophageal and GE junction tumors. Likewise, there is an interaction between age and treatment effect, with younger patients having a larger survival advantage, and no survival advantage for elderly patients.

In drug development, it is well accepted that a successful study will demonstrate not only a statistically significant result but also a clinically relevant effect size. Whereas standard hypothesis tests are used to demonstrate the former, it is less clear how the latter should be established. In the first part of this paper, we consider the responder analysis approach and study the performance of locally optimal rank tests when the outcome distribution is a mixture of responder and non-responder distributions. We find that these tests are quite sensitive to their planning assumptions and have therefore not really any advantage over standard tests such as the t-test and the Wilcoxon-Mann-Whitney test, which perform overall well and can be recommended for applications. In the second part, we present a new approach to the assessment of clinical relevance based on the so-called relative effect (or probabilistic index) and derive appropriate sample size formulae for the design of studies aiming at demonstrating both a statistically significant and clinically relevant effect. Referring to recent studies in multiple sclerosis, we discuss potential issues in the application of this approach., (Copyright © 2012 John Wiley & Sons, Ltd.)

The internal pilot study design allows for modifying the sample size during an ongoing study based on a blinded estimate of the variance thus maintaining the trial integrity. Various blinded sample size re-estimation procedures have been proposed in the literature. We compare the blinded sample size re-estimation procedures based on the one-sample variance of the pooled data with a blinded procedure using the randomization block information with respect to bias and variance of the variance estimators, and the distribution of the resulting sample sizes, power, and actual type I error rate. For reference, sample size re-estimation based on the unblinded variance is also included in the comparison. It is shown that using an unbiased variance estimator (such as the one using the randomization block information) for sample size re-estimation does not guarantee that the desired power is achieved. Moreover, in situations that are common in clinical trials, the variance estimator that employs the randomization block length shows a higher variability than the simple one-sample estimator and in turn the sample size resulting from the related re-estimation procedure. This higher variability can lead to a lower power as was demonstrated in the setting of noninferiority trials. In summary, the one-sample estimator obtained from the pooled data is extremely simple to apply, shows good performance, and is therefore recommended for application., (Copyright © 2013 John Wiley & Sons, Ltd.)