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Background: Studies focusing on prediction models are widespread in medicine. There is a trend in applying machine learning (ML) by medical researchers and clinicians. Over the years, multiple ML algorithms have been adapted to censored data. However, the choice of methodology should be motivated by the real-life data and their complexity. Here, the predictive performance of ML techniques is compared with statistical models in a simple clinical setting (small/moderate sample size and small number of predictors) with Monte-Carlo simulations., Methods: Synthetic data (250 or 1000 patients) were generated that closely resembled 5 prognostic factors preselected based on a European Osteosarcoma Intergroup study (MRC BO06/EORTC 80931). Comparison was performed between 2 partial logistic artificial neural networks (PLANNs) and Cox models for 20, 40, 61, and 80% censoring. Survival times were generated from a log-normal distribution. Models were contrasted in terms of the C-index, Brier score at 0-5 years, integrated Brier score (IBS) at 5 years, and miscalibration at 2 and 5 years (usually neglected). The endpoint of interest was overall survival., Results: PLANNs original/extended were tuned based on the IBS at 5 years and the C-index, achieving a slightly better performance with the IBS. Comparison with Cox models showed that PLANNs can reach similar predictive performance on simulated data for most scenarios with respect to the C-index, Brier score, or IBS. However, Cox models were frequently less miscalibrated. Performance was robust in scenario data where censored patients were removed before 2 years or curtailing at 5 years was performed (on training data)., Conclusion: Survival neural networks reached a comparable predictive performance with Cox models but were generally less well calibrated. All in all, researchers should be aware of burdensome aspects of ML techniques such as data preprocessing, tuning of hyperparameters, and computational intensity that render them disadvantageous against conventional regression models in a simple clinical setting., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2021 Georgios Kantidakis et al.)

Aim: Evidence-based medicine uses high-quality methods, such as randomization and blinding prcedures, to support and inform clinical practice. The investigation of trials registered in clinical research databases may help understand the main characteristics of studies conducted in countries, such as Brazil. This study aimed to investigate the characteristics of Brazilian clinical trials registered in ClinicalTrials.gov in the last decade., Methods: Cross-sectional study performed in ClinicalTrials.gov database with clinical studies registered in Brazil between 2010 and 2020. A search was conducted in the database considering this period using filters for country (Brazil), start date (01 January 2010) and end date (date of the search: 07 May 2020). Descriptive statistics were used to characterize the studies. All analyses were conducted on Stata 14.2., Results: In total, 5368 studies were retrieved, which were mostly randomized (93.2%), blinded (63.2%), Phase III (48.7%) interventional studies (89.9%) with small sample sizes (57.2%) conducted with adults (93.0%) using drugs (42.7%) with a treatment purpose (77.3%), with funding from institutions other than the industry, the NIH, and the US Federal (71.3%)., Conclusions: The majority of Brazilian clinical studies used blinding and randomization procedures in the last 10 years. However, the predominance of trials with small sample sizes and with a focus on adult patients indicate the need of larger studies conducted with the pediatric population., (© 2020 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.)

Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs., (© 2019 The International Biometric Society.)

As biomarker information from early-phase trials can be unreliable due to high variability, it is logical to take a prospective two-stage approach when designing a late-phase confirmatory trial, ie, refining the target population at the first stage and performing the hypothesis testing at the second stage. The use of a reliable intermediate endpoint at the first stage can further improve the trial efficiency from both time and cost perspectives. Nevertheless, there are needs for expanding such two-stage confirmatory designs to more stages for monitoring efficacy on the refined population. There is limited literature on this matter, particularly for two popular designs with population selection midway, ie, the biomarker enrichment design and the basket design. In this manuscript, we focus on these two popular designs and discuss how to implement the interim efficacy analyses after population refinement while controlling type I error. Power and stopping probability are also explored for the two designs., (© 2019 John Wiley & Sons, Ltd.)

Interim analyses are routinely used to monitor accumulating data in clinical trials. When the objective of the interim analysis is to stop the trial if the trial is deemed futile, it must ideally be conducted as early as possible. In trials where the clinical endpoint of interest is only observed after a long follow-up, many enrolled patients may therefore have no information on the primary endpoint available at the time of the interim analysis. To facilitate earlier decision-making, one may incorporate early response data that are predictive for the primary endpoint (eg, an assessment of the primary endpoint at an earlier time) in the interim analysis. Most attention so far has been given to the development of interim test statistics that include such short-term endpoints, but not to decision procedures. Existing tests moreover perform poorly when the information is scarce, eg, due to rare events, when the cohort of patients with observed primary endpoint data is small, or when the short-term endpoint is a strong but imperfect predictor. In view of this, we develop an interim decision procedure based on the conditional power approach that utilizes the short-term and long-term binary endpoints in a framework that is expected to provide reliable inferences, even when the primary endpoint is only available for a few patients, and has the added advantage that it allows the use of historical information. The operational characteristics of the proposed procedure are evaluated for the phase III clinical trial that motivated this approach, using simulation studies., (© 2019 John Wiley & Sons, Ltd.)

Purpose: Clinical Trials have emerged as the main force in driving the development of medicine. However, little is known about the current status of clinical trials regarding nasopharyngeal carcinoma (NPC). This study aimed at providing a comprehensive landscape of NPC-related trials on the basis of ClinicalTrials.gov database., Patients and Methods: We used the keyword "nasopharyngeal carcinoma" to search the ClinicalTrials.gov database and assessed the characteristics of these trials., Results: Up to December 30, 2016, 462 eligible trials in total were identified, of which 222 (48.0%) recruited only NPC (NPC trials) and the other 240 (52.0%) recruited both NPC and other cancers (multiple cancer trials). Moreover, 47 (10.2%) were Epstein-Barr virus (EBV)-related trials and 267 (57.8%) focused on metastatic/recurrent disease. Compared with NPC trials, the multiple cancer trials had a higher percentage of phase 1 (26.7% vs. 6.7%, P < 0.001) studies and more patients with metastatic/recurrent disease (72.5% vs. 41.9%, P < 0.001). Notably, non-EBV trials had more phase 2 or 3 (78.4% vs. 48.8%, P < 0.001) and interventional studies (89.5% vs. 70.7%, P = 0.002) than EBV trials. Obviously, more phase 2/3 or 3 trials were conducted in patients with non-metastatic/recurrent disease (29.4% vs. 4.9%, P < 0.001); however, metastatic/recurrent trials were more likely to be anticancer (94.6% vs. 63.6%, P < 0.001)., Conclusions: The role of plasma EBV DNA in clinical trials is underestimated, and high-level randomized clinical trials should be performed for patients with metastatic/recurrent disease.

Objectives: This study aims to comprehensively assess the publication of clinical trial results and factors associated with their publication., Study Design and Setting: Phase II and III trials of advanced breast cancer registered on ClinicalTrials.gov between October 1, 2000, and September 30, 2012, were identified. Publications were searched by using PubMed and reviewing those listed on the registry site. The main outcomes were publication rate, public availability of results, and time to publication., Results: Of 352 phase II and 74 phase III trials, 12.5% and 31.1% were published, whereas 46.9% and 58.1% had publicly available results, respectively. Compared to those with significant results, studies with nonsignificant results had delays in time to publication (P < 0.001). Even after adjusting for funding source and phase type, the significance of study outcomes was a significant factor that affected time to publication (hazard ratio = 6.02; 95% confidence interval: 3.59, 10.07; P < 0.001), with trials with significant outcomes taking less time to publish than those with nonsignificant outcomes., Conclusion: Underreporting of results and nonpublication or delays in the publication of negative results were identified in registered trials of advanced breast cancer. Thus, further initiatives appear necessary to urgently address such publication bias., (Copyright © 2017 Elsevier Inc. All rights reserved.)

We consider sample size re-estimation in a clinical trial, in particular when there is a significant delay before the measurement of patient response. Mehta and Pocock have proposed methods in which sample size is increased when interim results fall in a 'promising zone' where it is deemed worthwhile to increase conditional power by adding more subjects. Our analysis reveals potential pitfalls in applying this approach. Mehta and Pocock use results of Chen, DeMets and Lan to identify when increasing sample size, but applying a conventional level α significance test at the end of the trial does not inflate the type I error rate: we have found the greatest gains in power per additional observation are liable to lie outside the region defined by this method. Mehta and Pocock increase sample size to achieve a particular conditional power, calculated under the current estimate of treatment effect: this leads to high increases in sample size for a small range of interim outcomes, whereas we have found it more efficient to make moderate increases in sample size over a wider range of cases. If the aforementioned pitfalls are avoided, we believe the broad framework proposed by Mehta and Pocock is valuable for clinical trial design. Working in this framework, we propose sample size rules that apply explicitly the principle of adding observations when they are most beneficial. The resulting trial designs are closely related to efficient group sequential tests for a delayed response proposed by Hampson and Jennison., (Copyright © 2015 John Wiley & Sons, Ltd.)

Substantial heterogeneity in treatment effects across subgroups can cause significant findings in the overall population to be driven predominantly by those of a certain subgroup, thus raising concern on whether the treatment should be prescribed for the least benefitted subgroup. Because of its low power, a nonsignificant interaction test can lead to incorrectly prescribing treatment for the overall population. This article investigates the power of the interaction test and its implications. Also, it investigates the probability of prescribing the treatment to a nonbenefitted subgroup on the basis of a nonsignificant interaction test and other recently proposed criteria.

Clinical trials utilizing predictive biomarkers have become a research focus in personalized medicine. We investigate the effects of biomarker misclassification on the design and analysis of stratified biomarker clinical trials. For a variety of inference problems including marker-treatment interaction in particular, we show that marker misclassification may have profound adverse effects on the coverage of confidence intervals, power of the tests, and required sample sizes. For each inferential problem, we propose methods to adjust for the classification errors., (Copyright © 2014 John Wiley & Sons, Ltd.)

Multipopulation tailoring trials provide a trial design option that supports the realization of tailored therapeutics or personalized medicine. Several recent publications have focused on statistical and clinical considerations that arise in these trials that are designed to study the overall treatment effect in a population of interest as well as one or more prospectively defined subpopulations. Millen et al. (2012) introduced the influence and interaction conditions as part of a general framework to facilitate decision making in multipopulation trials. This article provides Bayesian methods for assessing the influence and interaction conditions. The methods introduced are illustrated using case studies based on clinical trials with biomarker-driven designs.

In phase III cancer clinical trials, overall survival is commonly used as the definitive endpoint. In phase II clinical trials, however, more immediate endpoints such as incidence of complete or partial response within 1 or 2 months or progression-free survival (PFS) are generally used. Because of the limited ability to detect change in overall survival with response, the inherent variability of PFS and the long wait for progression to be observed, more informative and immediate alternatives to overall survival are desirable in exploratory phase II trials. In this paper, we show how comparative trials can be designed and analysed using change in tumour size as the primary endpoint. The test developed is based on the framework of score statistics and will formally incorporate the information of whether patients survive until the time at which change in tumour size is assessed. Using an example in non-small cell lung cancer, we show that the sample size requirements for a trial based on change in tumour size are favourable compared with alternative randomized trials and demonstrate that these conclusions are robust to our assumptions., (Copyright © 2012 John Wiley & Sons, Ltd.)

We discuss sample size determination for clinical trials evaluating the joint effects of an intervention on two potentially correlated co-primary time-to-event endpoints. For illustration, we consider the most common case, a comparison of two randomized groups, and use typical copula families to model the bivariate endpoints. A correlation structure of the bivariate logrank statistic is specified to account for the correlation among the endpoints, although the between-group comparison is performed using the univariate logrank statistic. We propose methods to calculate the required sample size to compare the two groups and evaluate the performance of the methods and the behavior of required sample sizes via simulation.

We consider the problem of estimation in adaptive two-stage designs with selection of a single treatment arm at an interim analysis. It is well known that the standard maximum-likelihood estimator of the selected treatment is biased. We prove that selection bias of the maximum-likelihood estimator is maximal when all treatment effects are equal and the most-promising treatment is selected. Furthermore, we consider shrinkage estimation as a solution for the selection bias problem. We thereby extend previous work of Hwang on Lindley's estimator for single-stage multi-armed trials with four or more treatments and post-trial treatment selection. Following Hwang's ideas, we show that a simple two-stage version of Lindley's estimator has uniformly smaller Bayes risk than the maximum-likelihood estimator when assuming an empirical Bayesian framework with independent normal priors for the group means. For designs that start with two or three treatment groups, we suggest using a two-stage version of the common estimator of the best linear unbiased predicator of the corresponding random effects model. We show by an extensive simulation study that the shrinkage estimators perform well compared with maximum-likelihood and previously suggested bias-adjusted estimators in terms of selection bias and mean squared error., (Copyright © 2012 John Wiley & Sons, Ltd.)

We propose a group sequential Holm procedure when there are multiple primary endpoints. This method addresses multiplicities arising from multiple primary endpoints and from multiple analyses in a group sequential design. It has been shown to be a closed testing procedure and preserves the familywise error rate in the strong sense. When multiple endpoints are the only concern without an interim analysis, the method simplifies to the weighted Holm procedure. The proposed method is more powerful than the parallel group sequential method and avoids the need to anticipate the testing order as in the fixed sequence testing scheme., (Copyright © 2012 John Wiley & Sons, Ltd.)

This paper explores historical and current roles of pharmaceutical industry sponsorship of clinical trials testing radiation therapy combinations with molecularly targeted agents and attempts to identify potential solutions to expediting further combination studies. An analysis of clinical trials involving a combination of radiation therapy and novel cancer therapies was performed. Ongoing and completed trials were identified by searching the clinicaltrials.gov Web site, in the first instance, with published trials of drugs of interest identified through American Society of Clinical Oncology, European CanCer Organisation/European Society for Medical Oncology, American Society for Radiation Oncology/European Society for Therapeutic Radiology and Oncology, and PubMed databases and then cross-correlated with clinicaltrials.gov protocols. We examined combination trials involving radiation therapy with novel agents and determined their distribution by tumor type, predominant molecular mechanisms examined in combination to date, timing of initiation of trials relative to a novel agent's primary development, and source of sponsorship of such trials. A total of 564 studies of targeted agents in combination with radiation therapy were identified with or without concomitant chemotherapy. Most studies were in phase I/II development, with only 36 trials in phase III. The tumor site most frequently studied was head and neck (26%), followed by non-small cell lung cancer. Pharmaceutical companies were the sponsors of 33% of studies overall and provided support for only 16% of phase III studies. In terms of pharmaceutical sponsorship, Genentech was the most active sponsor of radiation therapy combinations (22%), followed by AstraZeneca (14%). Most radiation therapy combination trials do not appear to be initiated until after drug approval. In phase III studies, the most common (58%) primary endpoint was overall survival. Collectively, this analysis suggests that such trials are not given priority by pharmaceutical companies. The potential reasons for this and some challenges and possible solutions are discussed., (Copyright © 2012 Elsevier Inc. All rights reserved.)

In this chapter, the design of pharmacokinetic studies and phase III trials in children is discussed. Classical approaches and relatively novel approaches, which may be more useful in the context of drug research in children, are discussed. The burden of repeated blood sampling in pediatric pharmacokinetic studies may be overcome by the population pharmacokinetics approach using nonlinear mixed effect modeling as the statistical solution to sparse data. Indications and contraindications for phase III trials are discussed: only when there is true "equipoise" in the medical scientific community, it is ethical to conduct a randomized clinical trial. The many reasons why a pediatric trial may fail are illustrated with examples. Inadequate sample sizes lead to inconclusive results. Twelve classical strategies to minimize sample sizes are discussed followed by an introduction to group sequential design, boundaries design, and adaptive design. The evidence that these designs reduce sample sized between 35 and 70% is reviewed. The advantages and disadvantages of the different approaches are highlighted to give the reader a broad idea of the design types that can be considered. Finally, working with DMCs during the conduct of trials is introduced. The evidence regarding DMC activities, interim analysis results, and early termination of pediatric trials is presented. So far reporting is incomplete and heterogeneous, and users of trial reports may be misled by the results. A proposal for a checklist for the reporting of DMC issues, interim analyses, and early stopping is presented.

In this paper we propose a predictive Bayesian approach to sample size determination (SSD) and re-estimation in clinical trials, in the presence of multiple sources of prior information. The method we suggest is based on the use of mixtures of prior distributions for the unknown quantity of interest, typically a treatment effect or an effects-difference. Methodologies are developed using normal models with mixtures of conjugate priors. In particular we extend the SSD analysis of Gajewski and Mayo (Statist. Med. 2006; 25:2554-2566) and the sample size re-estimation technique of Wang (Biometrical J. 2006; 48(5):1-13).

The ability to select a sensitive patient population may be crucial for the development of a targeted therapy. Identifying such a population with an acceptable level of confidence may lead to an inflation in development time and cost. We present an approach that allows to decrease these costs and to increase the reliability of the population selection. It is based on an actual adaptive phase II/III design and uses Bayesian decision tools to select the population of interest at an interim analysis. The primary endpoint is assumed to be the time to some event like e.g. progression. It is shown that the use of appropriately stratified logrank tests in the adaptive test procedure guarantees overall type I error control also when using information on patients that are censored at the adaptive interim analysis. The use of Bayesian decision tools for the population selection decision making is discussed. Simulations are presented to illustrate the operating characteristics of the study design relative to a more traditional development approach. Estimation of treatment effects is considered as well., (Copyright (c) 2009 John Wiley & Sons, Ltd.)

A group-sequential design for clinical trials that involve treatment selection was proposed by Stallard and Todd (Statist. Med. 2003; 22:689-703). In this design, the best among a number of experimental treatments is selected on the basis of data observed at the first of a series of interim analyses. This experimental treatment then continues together with the control treatment to be assessed in one or more further analyses. The method was extended by Kelly et al. (J. Biopharm. Statist. 2005; 15:641-658) to allow more than one experimental treatment to continue beyond the first interim analysis. This design controls the familywise type I error rate under the global null hypothesis, that is in the weak sense, but may not strongly control the error rate, particularly if the treatments selected are not the best-performing ones. In some cases, for example when additional safety data are available, the restriction that the best-performing treatments continue may be unreasonable. This paper describes an extension of the approach of Stallard and Todd that enables construction of a group-sequential design for comparison of several experimental treatments with a control treatment. The new method controls the type I error rate in the strong sense if the number of treatments included at each stage is specified in advance, and is indicated by simulation studies to be conservative when the number of treatments is chosen based on the observed data in a practically relevant way.

The relatively low participation of African Americans in phase III clinical trials has raised concerns about the appropriateness of generalizing study results to African American populations. If African American enrollment in clinical trials continues to be low, the society may continue to see disparities in the treatment of diseases as well as unanswered questions as to why the population fares less than others when diagnosed with certain diseases such as cancer and diabetes. Additionally, more clinical trials are needed to explicitly monitor the difference in outcomes across different populations. This article discusses the various reasons why African American patient recruitment and participation is sub-optimal; the critical role of clinical trials in therapies; recommendations by important authorities; and a new practice model (Collaborative Care Model) as an innovative strategy to augment participation rates of African Americans [and other minorities] in clinical trials.

Background and Purpose: A variety of primary end points have been used in acute stroke trials. We focus on the modified Rankin Scale, a reliable and valid ordinal outcome measure that assesses disability on a 7-point scale., Methods: We provide an abbreviated discussion of analytical methods for ordinal scales, and related effect size measures; we illustrate these methods and their interpretation with outcome data from the SAINT I study of NXY-059 in acute ischemic stroke., Results: The nonparametric Mann-Whitney statistic provides a straightforward method for analysis of the modified Rankin Scale, and incorporates associated measures of effect size. These measures are directly related to the concepts of Number Needed to Treat and Number Needed to Harm., Conclusions: Our re-examination of the outcome data from the SAINT I study provides little evidence for the purported efficacy of NXY-059. More broadly, analysis and interpretation of ordinal outcome scales based on ascribed numerical values to the steps of the scale should be done cautiously. Statistical treatment of multiple primary outcome measures in acute stroke clinical trials should be established before analysis. Lastly, conflating statistical and clinical significance should be avoided.

This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; 'Confirmatory clinical trials with an adaptive design' by Armin Koch; and 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill.

Background: A mixed-effects model repeated measures approach (MMRM) was specified as the primary analysis in the Phase III clinical trials of duloxetine for the treatment of major depressive disorder (MDD). Analysis of covariance using the last observation carried forward approach to impute missing values (LOCF&#95;ANCOVA) was specified as a secondary analysis. Previous research has shown that MMRM and LOCF&#95;ANCOVA yield identical endpoint results when no data are missing, while MMRM is more robust to biases from missing data and thereby provides superior control of Type I and Type II error compared with LOCF&#95;ANCOVA. We compared results from MMRM and LOCF&#95;ANCOVA analyses across eight clinical trials of duloxetine in order to investigate how the choice of primary analysis may influence interpretations of efficacy., Methods: Results were obtained from the eight acute-phase clinical trials that formed the basis of duloxetine's New Drug Application for the treatment of MDD. All 202 mean change analyses from the 20 rating scale total scores and subscales specified a priori in the various protocols were included in the comparisons., Results: In 166/202 comparisons (82.2%), MMRM and LOCF&#95;ANCOVA agreed with regard to the statistical significance of the differences between duloxetine and placebo. In 25/202 cases (12.4%), MMRM yielded a significant difference when LOCF&#95;ANCOVA did not, while in 11/202 cases (5.4%), LOCF&#95;ANCOVA produced a significant difference when MMRM did not. In 110/202 comparisons (54.4%) the p-value from MMRM was lower than that from LOCF&#95;ANCOVA, while in 69/202 comparisons (34.2%), the p-value from LOCF&#95;ANCOVA was lower than that from MMRM. In the remaining 23 comparisons (11.4%), the p-values from LOCF&#95;ANCOVA and MMRM were equal when rounded to the 3rd decimal place (usually as a result of both p-values being <.001). For the HAMD17 total score, the primary outcome in all studies, MMRM yielded 9/12 (75%) significant contrasts, compared with 6/12 (50%) for LOCF&#95;ANCOVA. The expected success rate was 80%., Conclusions: Important differences exist between MMRM and LOCF&#95;ANCOVA. Empirical research has clearly demonstrated the theoretical advantages of MMRM over LOCF&#95;ANCOVA. However, interpretations regarding the efficacy of duloxetine in MDD were unaffected by the choice of analytical technique.

Statistical developments over the past several years are described in this review. Efforts in phase I studies have focused on efficient estimation of maximum tolerated dose. Issues investigated for phase II trials include incorporation of multiple endpoints and randomization. For phase III trials, methods to reduce time or use the sample size more efficiently have been investigated. However, design innovations come with costs, including possible increased risk of incorrect conclusions. Other recent challenging statistical developments in clinical trials relate to use of complementary outcomes such as quality of life and to associated biologic questions, including the emergence of the field of genomics.

This paper investigates the use of a bivariate endpoint in clinical trials of antiretroviral drugs to treat infection with the HIV. While they provide the most reliable information about treatment effects, clinical events such AIDS or death are relatively rare among newly treated patients with HIV infection. As a result, surrogate endpoints, such as time to virological failure and time to regimen termination, are generally used in place of clinical endpoints in studies that enroll patients without previous treatment experience. Because no surrogate endpoint has been completely validated (i.e., shown to capture the full effect of treatment on clinical endpoint), it may be preferable to base treatment decisions on more than one surrogate. In this paper, we consider the bivariate endpoint of time to virological failure and time to regimen termination. Analysis of a bivariate endpoint requires appropriate statistical methods that maintain the experimentwise type I error rate when making simultaneous inference with respect to each endpoint. This article outlines such methods and compares their small sample properties using the simulation study that was executed for the purpose of designing a current AIDS clinical trial.

France ranks third among European countries as regards the level of investment in clinical R&D and, overall, accounts for a contributive effort proportional to the size of its population and pharmaceutical market respectively. However, there is a trend for phase II and III studies to become proportionally fewer than in the past, while the number of phase IV studies is increasing. In a growing proportion of the mega-trials, which are instrumental for establishing evidence-based practice, French experts, investigators and, even more seriously, French patients, are insufficiently represented. Though studies in France are initiated relatively fast due to a clear regulatory framework and perform equally well as far as quantitative and qualitative factors are concerned, compared with most European countries involved in clinical research the costs incurred per completed patient are higher than those recorded in the other countries. Academic research shares most of these constraints and suffers from a lack of financial and human resources, while it faces additional delays in implementing studies because of longer administrative processes. Given the stakes in play, specific solutions should be implemented to maintain and further develop French competitiveness in clinical R&D. At the patient level, positive perception and awareness of the usefulness and safety of participating in clinical trials need to be emphasized. Education at the school level and using the lay media should be developed. Intervention of institutional and government officials is much needed. Direct patient recruitment should be developed through advertisement and the Internet, as well as within doctors' offices and through collaboration with patients' organizations. Patient information and consent forms should be made much simpler than those imposed within the framework of global studies because of FDA requirements. The French health system discourages the recruitment of patients by investigators who are not the family doctor or the usual care provider. Thus, motivation and education of general practitioners and hospital doctors may be increased by involving them during the trial design phase and in the publication process. Specific administrative solutions, within private or public institutions, need to be developed for investigators who do not personally wish to receive investigation fees. Because of lack of availability, investigators need to be assisted with study nurse services and site management organizations, particularly within hospitals and clinics, using the model of the Clinical Investigation Centres. Networks of clinical investigation centres and of individual investigators need to be created. Implementing these solutions should lead to better implications for and reputation of French clinical research.

Large clinical trials in life-threatening conditions are usually conducted under the surveillance of a Data and Safety Monitoring Board (DSMB), whose remit is to protect the ethical and safety interests of the patients. The purpose of this paper is to describe a formal approach to safety monitoring, using a sequential safety procedure to aid the decisions made by the DSMB. This procedure is designed to recommend termination of the study as soon as evidence that the experimental treatment is worse than the control in terms of the primary safety response is so strong that it is unethical to proceed. The use of this formal sequential procedure enables probabilities of the study stopping erroneously and stopping correctly, under various degrees of experimental treatment disadvantage, to be examined. Also scenarios depicting data sets which lead to continuing or stopping can be presented. Such explorations are useful in encouraging all DSMB members to consider carefully, prior to the start of a study, the conditions under which they would seriously wish to consider termination. The implementation of these methods is described for three recently completed trials in which it has been used. Finally, our current recommendations for the design of these procedures, arising from these and other similar experiences, are given., (Copyright 2000 John Wiley & Sons, Ltd.)

The maximum information trial paradigm for clinical trials with failure time data was recognized and has been investigated. With the maximum information trial paradigm, a study is concluded when a prespecified maximum number of events of interest, thus maximum information, has been accrued if there was no early stopping due to treatment difference or lack thereof. We present two interactive FORTRAN programs for use in designing maximum information trials based on group sequential logrank tests. The program SEQPWR computes the attainable power of group sequential logrank tests given the combinations of the accrual and follow-up durations. The program SEQOPR allows the users to investigate the operating characteristics of the maximum information trial given the information fractions of interim analyses. A clinical trial from the Eastern Cooperative Oncology Group is provided to illustrate the usage and features of the programs.

Data Monitoring Committees in the Southwest Oncology Group comprise the study leadership and the leadership of the Group, augmented by some outside representation. The early stopping guidelines used by these committees are explicit in each protocol, and can be characterized as specifying a few interim analyses at conservative statistical levels. We describe our ten years of experience with this system, and give examples of different trials which have stopped either for positive or negative results.

There is significant need for rapid yet reliable evaluation of promising new interventions for the treatment of patients with cancer or HIV infection. Considerable attention has been given to identifying replacement or 'surrogate' endpoints for the true clinical efficacy endpoints, in order to reduce the cost, size and duration of clinical trials. We discuss issues which affect the validity of surrogate markers. The reliability of the CD4 lymphocyte count marker is carefully considered in clinical trials of anti-retroviral agents in HIV infected individuals. The nature of surrogate markers and their reliability is discussed in cancer prevention, screening and treatment trials. Some suggested uses of marker information are also considered.

The decision to stop accrual early to a clinical trial is often difficult and multifaceted. Interim monitoring boundaries have been found useful in U.S. oncology trials for such decisions for reasons described here. This paper also discusses rationale that lead to more conservative approaches to early stopping decisions than are currently employed. A recent initiative of the National Cancer Institute to achieve the objectives of independent data monitoring committees in the phase III clinical trials which it sponsors is also described.