An exhaustive ADDIS principle for online FWER control

In this paper, we consider online multiple testing with familywise error rate (FWER) control, where the probability of committing at least one type I error will remain under control while testing a possibly infinite sequence of hypotheses over time. Currently, adaptive-discard (ADDIS) procedures seem to be the most promising online procedures with FWER control in terms of power. Now, our main contribution is a uniform improvement of the ADDIS principle and thus of all ADDIS procedures. This means, the methods we propose reject as least as much hypotheses as ADDIS procedures and in some cases even more, while maintaining FWER control. In addition, we show that there is no other FWER controlling procedure that enlarges the event of rejecting any hypothesis. Finally, we apply the new principle to derive uniform improvements of the ADDIS-Spending and ADDIS-Graph.

Discussion on "Optimal test procedures for multiple hypotheses controlling the familywise expected loss" by Willi Maurer, Frank Bretz, and Xiaolei Xun

This comment builds on the familywise expected loss (FWEL) framework suggested by Maurer, Bretz, and Xun in 2022. By representing the populationwise error rate (PWER) as FWEL, it is illustrated how the FWEL framework can be extended to clinical trials with multiple and overlapping populations and the PWER can be generalized to more general losses. The comment also addresses the question of how to deal with midtrial changes in the posttrial risks and related losses that are caused by data-driven decisions. Focusing on multiarm trials with the possibility of dropping treatments midtrial, we suggest to switch from control of the unconditional expected loss to control of the conditional expected loss that is related to the actual risks and is conditional on the sample event that causes the change in the risks. The problem and here suggested solution is also motivated with a sequence of independent trials for a hitherto incurable disease which ends when an efficient treatment is found. No multiplicity adjustment is applied in this case and we show how this can be justified by the consideration of the changing out-trial risks and with control of conditional type I error rates and losses.

Simultaneous confidence intervals for an extended Koch-Röhmel design in three-arm non-inferiority trials

Three-arm 'gold-standard' non-inferiority trials are recommended for indications where only unstable reference treatments are available and the use of a placebo group can be justified ethically. For such trials, several study designs have been suggested that use the placebo group for testing 'assay sensitivity', that is, the ability of the trial to replicate efficacy. Should the reference fail in the given trial, then non-inferiority could also be shown with an ineffective experimental treatment and hence becomes useless. In this article, we extend the so-called Koch-Röhmel design where a proof of efficacy for the experimental treatment is required in order to qualify for the non-inferiority test. While the efficacy of the experimental treatment is an indication of assay sensitivity, it does not guarantee that the reference is sufficiently efficient to let the non-inferiority claim be meaningful. It has, therefore, been suggested to adaptively test the non-inferiority only if the reference demonstrates superiority to placebo and otherwise to test δ -superiority of the experimental treatment over placebo, where δ is chosen in such a way that it provides proof of non-inferiority with regard to the reference's historical effect. In this article, we extend the previous work by complementing its adaptive test with compatible simultaneous confidence intervals. Confidence intervals are commonly used and suggested by regulatory guidelines for non-inferiority trials. We show how to adopt different approaches to simultaneous confidence intervals from the literature to the setting of three-arm non-inferiority trials and compare these methods in a simulation study. Finally, we apply these methods to a real clinical trial example.

How to avoid concerns with the interpretation of two primary endpoints if significant superiority in one is sufficient for formal proof of efficacy

Formal proof of efficacy of a drug requires that in a prospective experiment, superiority over placebo, or either superiority or at least non-inferiority to an established standard, is demonstrated. Traditionally one primary endpoint is specified, but various diseases exist where treatment success needs to be based on the assessment of two primary endpoints. With co-primary endpoints, both need to be "significant" as a prerequisite to claim study success. Here, no adjustment of the study-wise type-1-error is needed, but sample size is often increased to maintain the pre-defined power. Studies that use an at-least-one concept have been proposed where study success is claimed if superiority for at least one of the endpoints is demonstrated. This is sometimes also called the dual primary endpoint concept, and an appropriate adjustment of the study-wise type-1-error is required. This concept is not covered in the European Guideline on multiplicity because study success can be claimed if one endpoint shows significant superiority, despite a possible deterioration in the other. In line with Röhmel's strategy, we discuss an alternative approach including non-inferiority hypotheses testing that avoids obvious contradictions to proper decision-making. This approach leads back to the co-primary endpoint assessment, and has the advantage that minimum requirements for endpoints can be modeled flexibly for several practical needs. Our simulations show that, if planning assumptions are correct, the proposed additional requirements improve interpretation with only a limited impact on power, that is, on sample size.

Effectiveness and costs of a low-threshold hearing screening programme (HörGeist) for individuals with intellectual disabilities: protocol for a screening study

INTRODUCTION

Individuals with intellectual disabilities (ID) often suffer from hearing loss, in most cases undiagnosed or inappropriately treated. The implementation of a programme of systematic hearing screening, diagnostics, therapy initiation or allocation and long-term monitoring within the living environments of individuals with ID (nurseries, schools, workshops, homes), therefore, seems beneficial.

METHODS AND ANALYSIS

The study aims to assess the effectiveness and costs of a low-threshold screening programme for individuals with ID. Within this programme 1050 individuals with ID of all ages will undergo hearing screening and an immediate reference diagnosis in their living environment (outreach cohort). The recruitment of participants in the outreach group will take place within 158 institutions, for example, schools, kindergartens and places of living or work. If an individual fails the screening assessment, subsequent full audiometric diagnostics will follow and, if hearing loss is confirmed, initiation of therapy or referral to and monitoring of such therapy. A control cohort of 141 participants will receive an invitation from their health insurance provider via their family for the same procedure but within a clinic (clinical cohort). A second screening measurement will be performed with both cohorts 1 year later and the previous therapy outcome will be checked. It is hypothesised that this programme leads to a relevant reduction in the number of untreated or inadequately treated cases of hearing loss and strengthens the communication skills of the newly or better-treated individuals. Secondary outcomes include the age-dependent prevalence of hearing loss in individuals with ID, the costs associated with this programme, cost of illness before-and-after enrolment and modelling of the programme's cost-effectiveness compared with regular care.

ETHICS AND DISSEMINATION

The study has been approved by the Institutional Ethics Review Board of the Medical Association of Westphalia-Lippe and the University of Münster (No. 2020-843 f-S). Participants or guardians will provide written informed consent. Findings will be disseminated through presentations, peer-reviewed journals and conferences.

TRIAL REGISTRATION NUMBER

DRKS00024804.

Smartphone-assisted training with education for patients with hip and/or knee osteoarthritis (SmArt-E): study protocol for a multicentre pragmatic randomized controlled trial

Introduction

Hip and knee osteoarthritis are associated with functional limitations, pain and restrictions in quality of life and the ability to work. Furthermore, with growing prevalence, osteoarthritis is increasingly causing (in)direct costs. Guidelines recommend exercise therapy and education as primary treatment strategies. Available options for treatment based on physical activity promotion and lifestyle change are often insufficiently provided and used. In addition, the quality of current exercise programmes often does not meet the changing care needs of older people with comorbidities and exercise adherence is a challenge beyond personal physiotherapy. The main objective of this study is to investigate the short- and long-term (cost-)effectiveness of the SmArt-E programme in people with hip and/or knee osteoarthritis in terms of pain and physical functioning compared to usual care.

Methods

This study is designed as a multicentre randomized controlled trial with a target sample size of 330 patients. The intervention is based on the e-Exercise intervention from the Netherlands, consists of a training and education programme and is conducted as a blended care intervention over 12 months. We use an app to support independent training and the development of self-management skills. The primary and secondary hypotheses are that participants in the SmArt-E intervention will have less pain (numerical rating scale) and better physical functioning (Hip Disability and Osteoarthritis Outcome Score, Knee Injury and Osteoarthritis Outcome Score) compared to participants in the usual care group after 12 and 3 months. Other secondary outcomes are based on domains of the Osteoarthritis Research Society International (OARSI). The study will be accompanied by a process evaluation.

Discussion

After a positive evaluation, SmArt-E can be offered in usual care, flexibly addressing different care situations. The desired sustainability and the support of the participants’ behavioural change are initiated via the app through audio-visual contact with their physiotherapists. Furthermore, the app supports the repetition and consolidation of learned training and educational content. For people with osteoarthritis, the new form of care with proven effectiveness can lead to a reduction in underuse and misuse of care as well as contribute to a reduction in (in)direct costs.

Trial registration

German Clinical Trials Register, DRKS00028477. Registered on August 10, 2022.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-023-06255-7.

The population-wise error rate for clinical trials with overlapping populations

We introduce a new multiple type I error criterion for clinical trials with multiple, overlapping populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type I error rate, i.e. the probability that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted p-values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggested multiple-testing approach for umbrella trials.

COVID-19 in German Nursing Homes: The Impact of Facilities' Structures on the Morbidity and Mortality of Residents-An Analysis of Two Cross-Sectional Surveys

The COVID-19 pandemic constitutes an exceptional risk to people living and working in nursing homes (NHs). There were numerous cases and deaths among NH residents, especially at the beginning of the pandemic when no vaccines had yet been developed. Besides regional differences, individual NHs showed vast differences in the number of cases and deaths: while in some, nobody was affected, in others, many people were infected or died. We examine the relationship between facility structures and their effect on infections and deaths of NH residents and infections of staff, while considering the influence of COVID-19 prevalence among the general population on the incidence of infection in NHs. Two nationwide German surveys were conducted during the first and second pandemic waves, comprising responses from n = 1067 NHs. Different hurdle models, with an assumed Bernoulli distribution for zero density and a negative binomial distribution for the count density, were fitted. It can be shown that the probability of an outbreak, and the number of cases/deaths among residents and staff, increased with an increasing number of staff and the general spread of the virus. Therefore, reverse isolation of NH residents was an inadequate form of protection, especially at the beginning of the pandemic.

Single-stage, three-arm, adaptive test strategies for non-inferiority trials with an unstable reference

For indications where only unstable reference treatments are available and use of placebo is ethically justified, three-arm "gold standard" designs with an experimental, reference and placebo arm are recommended for non-inferiority trials. In such designs, the demonstration of efficacy of the reference or experimental treatment is a requirement. They have the disadvantage that only little can be concluded from the trial if the reference fails to be efficacious. To overcome this, we investigate novel single-stage, adaptive test strategies where non-inferiority is tested only if the reference shows sufficient efficacy and otherwise δ $$ \delta $$ -superiority of the experimental treatment over placebo is tested. With a properly chosen superiority margin, δ $$ \delta $$ -superiority indirectly shows non-inferiority. We optimize the sample size for several decision rules and find that the natural, data driven test strategy, which tests non-inferiority if the reference's efficacy test is significant, leads to the smallest overall and placebo sample sizes. We proof that under specific constraints on the sample sizes, this procedure controls the family-wise error rate. All optimal sample sizes are found to meet this constraint. We finally show how to account for a relevant placebo drop-out rate in an efficient way and apply the new test strategy to a real life data set.

Blinded sample size re-estimation in a comparative diagnostic accuracy study

Background

The sample size calculation in a confirmatory diagnostic accuracy study is performed for co-primary endpoints because sensitivity and specificity are considered simultaneously. The initial sample size calculation in an unpaired and paired diagnostic study is based on assumptions about, among others, the prevalence of the disease and, in the paired design, the proportion of discordant test results between the experimental and the comparator test. The choice of the power for the individual endpoints impacts the sample size and overall power. Uncertain assumptions about the nuisance parameters can additionally affect the sample size.

Methods

We develop an optimal sample size calculation considering co-primary endpoints to avoid an overpowered study in the unpaired and paired design. To adjust assumptions about the nuisance parameters during the study period, we introduce a blinded adaptive design for sample size re-estimation for the unpaired and the paired study design. A simulation study compares the adaptive design to the fixed design. For the paired design, the new approach is compared to an existing approach using an example study.

Results

Due to blinding, the adaptive design does not inflate type I error rates. The adaptive design reaches the target power and re-estimates nuisance parameters without any relevant bias. Compared to the existing approach, the proposed methods lead to a smaller sample size.

Conclusions

We recommend the application of the optimal sample size calculation and a blinded adaptive design in a confirmatory diagnostic accuracy study. They compensate inefficiencies of the sample size calculation and support to reach the study aim.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01564-2.

A multiple testing framework for diagnostic accuracy studies with co-primary endpoints

Major advances have been made regarding the utilization of machine learning techniques for disease diagnosis and prognosis based on complex and high-dimensional data. Despite all justified enthusiasm, overoptimistic assessments of predictive performance are still common in this area. However, predictive models and medical devices based on such models should undergo a throughout evaluation before being implemented into clinical practice. In this work, we propose a multiple testing framework for (comparative) phase III diagnostic accuracy studies with sensitivity and specificity as co-primary endpoints. Our approach challenges the frequent recommendation to strictly separate model selection and evaluation, that is, to only assess a single diagnostic model in the evaluation study. We show that our parametric simultaneous test procedure asymptotically allows strong control of the family-wise error rate. A multiplicity correction is also available for point and interval estimates. Moreover, we demonstrate in an extensive simulation study that our multiple testing strategy on average leads to a better final diagnostic model and increased statistical power. To plan such studies, we propose a Bayesian approach to determine the optimal number of models to evaluate simultaneously. For this purpose, our algorithm optimizes the expected final model performance given previous (hold-out) data from the model development phase. We conclude that an assessment of multiple promising diagnostic models in the same evaluation study has several advantages when suitable adjustments for multiple comparisons are employed.

Is there a role for statistics in artificial intelligence?

The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching.

Sociodemographic aspects and health care-related outcomes: a latent class analysis of informal dementia care dyads

BACKGROUND

Studies revealed the importance to assess dementia care dyads, composed of persons with dementia and their primary informal caregivers, in a differentiated way and to tailor support services to particular living and care circumstances. Therefore, this study aims first to identify classes of dementia care dyads that differ according to sociodemographic, care-related and dementia-specific characteristics and second, to compare these classes with regard to healthcare-related outcomes.

METHODS

We used data from the cross-sectional German DemNet-D study (n = 551) and conducted a latent class analysis to investigate different classes of dementia care dyads. In addition, we compared these classes with regard to the use of health care services, caregiver burden (BIZA-D), general health of the informal caregiver (EQ-VAS) as well as quality of life (QoL-AD) and social participation (SACA) of the person with dementia. Furthermore, we compared the stability of the home-based care arrangements.

RESULTS

Six different classes of dementia care dyads were identified, based on best Bayesian Information Criterion (BIC), significant likelihood ratio test (p <  0.001), high entropy (0.87) and substantive interpretability. Classes were labelled as "adult child parent relationship & younger informal caregiver", "adult child parent relationship & middle aged informal caregiver", "non family relationship & younger informal caregiver", "couple & male informal caregiver of older age", "couple & female informal caregiver of older age", "couple & younger informal caregiver". The classes showed significant differences regarding health care service use. Caregiver burden, quality of life of the person with dementia and stability of the care arrangement differed also significantly between the classes.

CONCLUSION

Based on a latent class analysis this study indicates differences between classes of informal dementia care dyads. The findings may give direction for better tailoring of support services to particular circumstances to improve healthcare-related outcomes of persons with dementia and informal caregivers.

[Older patients' involvement in research (INVOLVE-Clin): a study protocol]

INTRODUCTION

Patient involvement in health research is an integral part of health care in many countries. It promotes the relevance and quality of research and increases the meaningfulness of research results. Meanwhile, the value of patient involvement has also been recognised in Germany. The lack of a common understanding of patient involvement and appropriate methods make implementation difficult. In Germany, patients are still rarely involved in the planning and conduct of health research. Vulnerable patient groups such as the elderly and the very old are considered particularly challenging for researchers in active patient involvement due to their special needs, which is why they are often neglected. Especially nursing home residents suffer from a variety of health impairments which are accompanied by a high number of prescription drugs and adverse events and can therefore make patient involvement more difficult. The present project aims to test the method of patient advisory boards for the involvement of nursing home residents. Using the design of a clinical trial to optimise medication for nursing home residents as a case study, we will assess the feasibility of the method for this target group. We will also install a patient advocate as moderator of the advisory board. The study plan is described in the present study protocol.

METHODS

Two patient advisory boards with nursing home residents will be established. With a patient advocate acting as moderator, the essential elements of a clinical trial to optimise medication will be discussed and passed on to the study planning team via the patient advocate. The overall topic of the clinical trial is the optimisation of medication in cardiovascular disease. The nursing home residents are informed about the contents and ideas of the study to be planned and the interests of the researchers, respectively, and will discuss the proposals of the study planning team. Nursing home residents', the patient advocate's and the researchers' expectations and experiences will be examined in individual interviews.

DISCUSSION

The study will provide a potentially suitable method to involve nursing home residents in the research process. The jointly developed study design will be incorporated into a new project proposal. The results will be used to inform the development of a German handbook on active public and patient involvement.

Early use of acute medication for preventing migraine attacks: Results from a diary-based cohort study

Background:

Treating migraine attacks early may improve outcome. The aim of this analysis was to investigate whether certain premonitory symptoms could be indicators for taking acute medication.

Methods:

We analyzed 3-month diary data recorded by 271 patients with episodic migraine and looked at all migraine-free intervals. For investigating the interaction between acute medication and neck discomfort associated with sensitivity to lights, noises, or odors, we used a marginal structural model and a Cox regression analysis adjusted for moderate or severe headache.

Results:

The patients (mean age 43 ± 15.4 years, 88% women) recorded a total of 20,219 diary days without migraine. In the marginal structural model analysis, the risk for occurrence of a migraine attack on the subsequent day was reduced when acute medication was used in the presence of neck discomfort associated with sensitivity to lights (hazard ratio 0.4; 95% confidence interval 0.2–0.7), noises (0.4; 0.3–0.7), or odors (0.2; 0.1–0.4). The marginal structural model showed lower risk of migraine attacks than the Cox regression analysis adjusted for moderate or severe headache in the majority of the cases.

Conclusion:

Migraine attacks may be prevented when acute medication is used in the presence of neck discomfort associated with sensitivity to lights, noises, or odors. The results of this study may stimulate further prospective trials.

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

Simultaneous confidence intervals for ratios with application to the gold standard design with more than one experimental treatment

Considering a study design with two experimental treatments, a reference treatment and a placebo, we extend a previous approach considering the ratios of effects to a procedure for analyzing multiple ratios. The technical framework for constructing tests and compatible simultaneous confidence intervals is set in a general manner. Besides a single step procedure and its extension to a stepdown procedure, also, an informative stepwise procedure in the spirit of our previous work is developed. The latter is especially interesting, because noninferiority studies require informative confidence intervals to infer more information than just noninferiority at the prespecified margin. Results from a simulation study for the three methods are shown. We also argue that an extension to more than two experimental treatments is straightforward.

Parameter Optimization in High-Throughput Testing for Structural Materials

High-throughput screenings are established evaluation methods in the development of functional materials and pharmaceutical active ingredients. The transfer of this approach to the development of structural materials requires extensive adaptations. In addition to the investigation of new test procedures for the determination of material properties and the treatment of metallic materials, the design of experiments is a research focus. Based on given descriptor target values, the statistical design of experiments determines investigations and treatments for the investigation of these materials. In this context, process parameters also have to be determined, as these have a major influence on the later material properties, especially during the treatment of samples. In this article, a method is presented which determines the process parameters iteratively. The validation of the calculated process parameters takes place based on differential scanning calorimetry used as the furnace for the heat treatment of small batches and particle-oriented peening as the characterization method.

Testing multiple dose combinations in clinical trials

Drug combination trials are often motivated by the fact that individual drugs target the same disease but via different routes. A combination of such drugs may then have an overall better effect than the individual treatments which has to be verified by clinical trials. Several statistical methods have been explored that discuss the problem of comparing a fixed-dose combination therapy to each of its components. But an extension of these approaches to multiple dose combinations can be difficult and is not yet fully investigated. In this paper, we propose two approaches by which one can provide confirmatory assurance with familywise error rate control, that the combination of two drugs at differing doses is more effective than either component doses alone. These approaches involve multiple comparisons in multilevel factorial designs where the type 1 error can be controlled first, by bootstrapping tests, and second, by considering the least favorable null configurations for a family of union intersection tests. The main advantage of the new approaches is that their implementation is simple. The implementation of these new approaches is illustrated with a real data example from a blood pressure reduction trial. Extensive simulations are also conducted to evaluate the new approaches and benchmark them with existing ones. We also present an illustration of the relationship between the different approaches. We observed that the bootstrap provided some power advantages over the other approaches with the disadvantage that there may be some error rate inflation for small sample sizes.

Evaluation of multiple prediction models: A novel view on model selection and performance assessment

Model selection and performance assessment for prediction models are important tasks in machine learning, e.g. for the development of medical diagnosis or prognosis rules based on complex data. A common approach is to select the best model via cross-validation and to evaluate this final model on an independent dataset. In this work, we propose to instead evaluate several models simultaneously. These may result from varied hyperparameters or completely different learning algorithms. Our main goal is to increase the probability to correctly identify a model that performs sufficiently well. In this case, adjusting for multiplicity is necessary in the evaluation stage to avoid an inflation of the family wise error rate. We apply the so-called maxT-approach which is based on the joint distribution of test statistics and suitable to (approximately) control the family-wise error rate for a wide variety of performance measures. We conclude that evaluating only a single final model is suboptimal. Instead, several promising models should be evaluated simultaneously, e.g. all models within one standard error of the best validation model. This strategy has proven to increase the probability to correctly identify a good model as well as the final model performance in extensive simulation studies.

Comparison of different approaches for dose response analysis

Characterizing an appropriate dose-response relationship and identifying the right dose in a clinical trial are two main goals of early drug-development. MCP-Mod is one of the pioneer approaches developed within the last 10 years that combines the modeling techniques with multiple comparison procedures to address the above goals in clinical drug development. The MCP-Mod approach begins with a set of potential dose-response models, tests for a significant dose-response effect (proof of concept, PoC) using multiple linear contrasts tests and selects the "best" model among those with a significant contrast test. A disadvantage of the method is that the parameter values of the candidate models need to be fixed a priori for the contrasts tests. This may lead to a loss in power and unreliable model selection. For this reason, several variations of the MCP-Mod approach and a hierarchical model selection approach have been suggested where the parameter values need not be fixed in the proof of concept testing step and can be estimated after the model selection step. This paper provides a numerical comparison of the different MCP-Mod variants and the hierarchical model selection approach with regard to their ability of detecting the dose-response trend, their potential to select the correct model and their accuracy in estimating the dose response shape and minimum effective dose. Additionally, as one of the approaches is based on two-sided model comparisons only, we make it more consistent with the common goals of a PoC study, by extending it to one-sided comparisons between the constant and alternative candidate models in the proof of concept step.

Nonparametric adaptive enrichment designs using categorical surrogate data

Adaptive survival trials are particularly important for enrichment designs in oncology and other life-threatening diseases. Current statistical methodology for adaptive survival trials provide type I error rate control only under restrictions. For instance, if we use stage-wise P values based on increments of the log-rank test, then the information used for the interim decisions need to be restricted to the primary survival endpoint. However, it is often desirable to base interim decisions also on correlated short-term endpoints like tumor response. Alternative statistical approaches based on a patient-wise splitting of the data require unnatural restrictions on the follow-up times and do not permit to efficiently account for an early rejection of the primary null hypothesis. We therefore suggest new approaches that enable us to use discrete surrogate endpoints (like tumor response status) and also to incorporate interim rejection boundaries. The new approaches are based on weighted Kaplan-Meier estimates and thereby have additional advantages. They permit us to account for nonproportional hazards and are robust against informative censoring based on the surrogate endpoint. We will show that nonproportionality is an intrinsic and relevant issue in enrichment designs. Moreover, informative censoring based on the surrogate endpoint is likely because of withdrawals and treatment switches after insufficient treatment response. It is shown and illustrated how nonparametric tests based on weighted Kaplan-Meier estimates can be used in closed combination tests for adaptive enrichment designs, such that type I error rate control is achieved and justified asymptotically.

A proposal for a new PhD level curriculum on quantitative methods for drug development

This paper provides an overview of "Improving Design, Evaluation and Analysis of early drug development Studies" (IDEAS), a European Commission-funded network bringing together leading academic institutions and small- to large-sized pharmaceutical companies to train a cohort of graduate-level medical statisticians. The network is composed of a diverse mix of public and private sector partners spread across Europe, which will host 14 early-stage researchers for 36 months. IDEAS training activities are composed of a well-rounded mixture of specialist methodological components and generic transferable skills. Particular attention is paid to fostering collaborations between researchers and supervisors, which span academia and the private sector. Within this paper, we review existing medical statistics programmes (MSc and PhD) and highlight the training they provide on skills relevant to drug development. Motivated by this review and our experiences with the IDEAS project, we propose a concept for a joint, harmonised European PhD programme to train statisticians in quantitative methods for drug development.

Effectiveness and cost-effectiveness of an integrated care program for schizophrenia: an analysis of routine data

In Germany, a regional social health insurance fund provides an integrated care program for patients with schizophrenia (IVS). Based on routine data of the social health insurance, this evaluation examined the effectiveness and cost-effectiveness of the IVS compared to the standard care (control group, CG). The primary outcome was the reduction of psychiatric inpatient treatment (days in hospital), and secondary outcomes were schizophrenia-related inpatient treatment, readmission rates, and costs. To reduce selection bias, a propensity score matching was performed. The matched sample included 752 patients. Mean number of psychiatric and schizophrenia-related hospital days of patients receiving IVS (2.3 ± 6.5, 1.7 ± 5.0) per quarter was reduced, but did not differ statistically significantly from CG (2.7 ± 7.6, 1.9 ± 6.2; p = 0.772, p = 0.352). Statistically significant between-group differences were found in costs per quarter per person caused by outpatient treatment by office-based psychiatrists (IVS: €74.18 ± 42.30, CG: €53.20 ± 47.96; p < 0.001), by psychiatric institutional outpatient departments (IVS: €4.83 ± 29.57, CG: €27.35 ± 76.48; p < 0.001), by medication (IVS: €471.75 ± 493.09, CG: €429.45 ± 532.73; p = 0.015), and by psychiatric outpatient nursing (IVS: €3.52 ± 23.83, CG: €12.67 ± 57.86, p = 0.045). Mean total psychiatric costs per quarter per person in IVS (€1117.49 ± 1662.73) were not significantly lower than in CG (€1180.09 ± 1948.24; p = 0.150). No statistically significant differences in total schizophrenia-related costs per quarter per person were detected between IVS (€979.46 ± 1358.79) and CG (€989.45 ± 1611.47; p = 0.084). The cost-effectiveness analysis showed cost savings of €148.59 per reduced psychiatric and €305.40 per reduced schizophrenia-related hospital day. However, limitations, especially non-inclusion of costs related to management of the IVS and additional home treatment within the IVS, restrict the interpretation of the results. Therefore, the long-term impact of this IVS deserves further evaluation.

A Bayesian model to estimate the cutoff and the clinical utility of a biomarker assay

To enable targeted therapies and enhance medical decision-making, biomarkers are increasingly used as screening and diagnostic tests. When using quantitative biomarkers for classification purposes, this often implies that an appropriate cutoff for the biomarker has to be determined and its clinical utility must be assessed. In the context of drug development, it is of interest how the probability of response changes with increasing values of the biomarker. Unlike sensitivity and specificity, predictive values are functions of the accuracy of the test, depend on the prevalence of the disease and therefore are a useful tool in this setting. In this paper, we propose a Bayesian method to not only estimate the cutoff value using the negative and positive predictive values, but also estimate the uncertainty around this estimate. Using Bayesian inference allows us to incorporate prior information, and obtain posterior estimates and credible intervals for the cut-off and associated predictive values. The performance of the Bayesian approach is compared with alternative methods via simulation studies of bias, interval coverage and width and illustrations on real data with binary and time-to-event outcomes are provided.

Interval and point estimation in adaptive Phase II trials with binary endpoint

Phase II clinical trials are concerned with making decision of whether a treatment is sufficiently efficacious to be worth further investigations in late large scale Phase III trials. In oncology Phase II trials, frequentist single-arm two-stage group-sequential designs with a binary endpoint are commonly used. To allow for more flexibility, adaptive versions of these designs have been proposed. In this paper, we propose point and interval estimation for adaptive designs in which the second stage sample size is a pre-specified function of first stage's number of responses. Our approach is based on sample space orderings, from which we derive p-values, and point and interval estimates. Simulation studies show that our proposed methods perform better, in terms of bias and root mean square error, than the fixed-sample maximum likelihood estimator.

The Average Hazard Ratio - A Good Effect Measure for Time-to-event Endpoints when the Proportional Hazard Assumption is Violated?

BACKGROUND

In many clinical trial applications, the endpoint of interest corresponds to a time-to-event endpoint. In this case, group differences are usually expressed by the hazard ratio. Group differences are commonly assessed by the logrank test, which is optimal under the proportional hazard assumption. However, there are many situations in which this assumption is violated. Especially in applications were a full population and several subgroups or a composite time-to-first-event endpoint and several components are considered, the proportional hazard assumption usually does not simultaneously hold true for all test problems under investigation. As an alternative effect measure, Kalbfleisch and Prentice proposed the so-called 'average hazard ratio'. The average hazard ratio is based on a flexible weighting function to modify the influence of time and has a meaningful interpretation even in the case of non-proportional hazards. Despite this favorable property, it is hardly ever used in practice, whereas the standard hazard ratio is commonly reported in clinical trials regardless of whether the proportional hazard assumption holds true or not.

OBJECTIVES

There exist two main approaches to construct corresponding estimators and tests for the average hazard ratio where the first relies on weighted Cox regression and the second on a simple plug-in estimator. The aim of this work is to give a systematic comparison of these two approaches and the standard logrank test for different time-toevent settings with proportional and nonproportional hazards and to illustrate the pros and cons in application.

METHODS

We conduct a systematic comparative study based on Monte-Carlo simulations and by a real clinical trial example.

RESULTS

Our results suggest that the properties of the average hazard ratio depend on the underlying weighting function. The two approaches to construct estimators and related tests show very similar performance for adequately chosen weights. In general, the average hazard ratio defines a more valid effect measure than the standard hazard ratio under non-proportional hazards and the corresponding tests provide a power advantage over the common logrank test.

CONCLUSIONS

As non-proportional hazards are often met in clinical practice and the average hazard ratio tests often outperform the common logrank test, this approach should be used more routinely in applications.

Toxicity and phototoxicity in human ARPE-19 retinal pigment epithelium cells of dyes commonly used in retinal surgery

PURPOSE

To compare, for the first time, systematically the toxicity and phototoxicity of dyes and dye combinations used in vitreoretinal surgery. The dyes were trypan blue, brilliant blue G, trypan blue + brilliant blue G, indocyanine green, bromophenol blue, bromophenol blue + brilliant blue G, and acid violet 17, in clinically used concentrations.

METHODS

Human ARPE retinal pigment epithelium cells were exposed to the dyes for 30 min. For phototoxicity, the cells were exposed for 15 min to high-intensity light from a light emitting diode source with an intensity similar to surgical conditions. Toxicity was assayed either directly after exposure to either dye alone or dye and light, or with a delay of 24 h.

RESULTS

None of the dyes or their combinations was toxic when cells were exposed to them at ambient light. Acid violet led to a reduction viability by 90% already immediately after light exposure. Bromophenol blue and its combination with brilliant blue G showed strong phototoxicity (reduction of viability by 83%) when assayed with delay. Indocyanine green with different agents to adjust osmolarity (balanced salt solution, glucose, and mannitol) was not found to be toxic.

CONCLUSION

The strong immediate phototoxicity of acid violet reflects its clinical toxicity. Bromophenol blue might also be disadvantageous for patient outcome because of its delayed phototoxicity. The other dyes (trypan blue, brilliant blue g, and indocyanine green) were not found to be toxic neither with exposure to ambient light nor after exposure to light of intensities used in surgery.

Flexible Two-Stage Designs: An Overview

In this overview we introduce the basic ideas behind a new flexible approach in sequential designs. The different concepts based on two-stage combination tests and conditional error functions are brought together. We sketch the construction of p-values, confidence intervals, and median unbiased estimates. Finally, recursive combination tests are introduced which extend the flexibility to the choice of the number of interim analyses.

Model selection based on combined penalties for biomarker identification

The growing role of targeted medicine has led to an increased focus on the development of actionable biomarkers. Current penalized selection methods that are used to identify biomarker panels for classification in high-dimensional data, however, often result in highly complex panels that need careful pruning for practical use. In the framework of regularization methods, a penalty that is a weighted sum of the L₁ and L₀ norm has been proposed to account for the complexity of the resulting model. In practice, the limitation of this penalty is that the objective function is non-convex, non-smooth, the optimization is computationally intensive and the application to high-dimensional settings is challenging. In this paper, we propose a stepwise forward variable selection method which combines the L₀ with L₁ or L₂ norms. The penalized likelihood criterion that is used in the stepwise selection procedure results in more parsimonious models, keeping only the most relevant features. Simulation results and a real application show that our approach exhibits a comparable performance with common selection methods with respect to the prediction performance while minimizing the number of variables in the selected model resulting in a more parsimonious model as desired.

Sequential tests for non-proportional hazards data

In clinical trials survival endpoints are usually compared using the log-rank test. Sequential methods for the log-rank test and the Cox proportional hazards model are largely reported in the statistical literature. When the proportional hazards assumption is violated the hazard ratio is ill-defined and the power of the log-rank test depends on the distribution of the censoring times. The average hazard ratio was proposed as an alternative effect measure, which has a meaningful interpretation in the case of non-proportional hazards, and is equal to the hazard ratio, if the hazards are indeed proportional. In the present work we prove that the average hazard ratio based sequential test statistics are asymptotically multivariate normal with the independent increments property. This allows for the calculation of group-sequential boundaries using standard methods and existing software. The finite sample characteristics of the new method are examined in a simulation study in a proportional and a non-proportional hazards setting.

Comparison of different contouring definitions of the rectum as organ at risk (OAR) and dose-volume parameters predicting rectal inflammation in radiotherapy of prostate cancer: which definition to use?

OBJECTIVE

The objective of this retrospective planning study was to find a contouring definition for the rectum as an organ at risk (OAR) in curative three-dimensional external beam radiotherapy (EBRT) for prostate cancer (PCa) with a predictive correlation between the dose-volume histogram (DVH) and rectal toxicity.

METHODS

In a pre-study, the planning CT scans of 23 patients with PCa receiving definitive EBRT were analyzed. The rectum was contoured according to 13 different definitions, and the dose distribution was correlated with the respective rectal volumes by generating DVH curves. Three definitions were identified to represent the most distinct differences in the shapes of the DVH curves: one anatomical definition recommended by the Radiation Therapy Oncology Group (RTOG) and two functional definitions based on the target volume. In the main study, the correlation between different relative DVH parameters derived from these three contouring definitions and the occurrence of rectal toxicity during and after EBRT was studied in two consecutive collectives. The first cohort consisted of 97 patients receiving primary curative EBRT and the second cohort consisted of 66 patients treated for biochemical recurrence after prostatectomy. Rectal toxicity was investigated by clinical investigation and scored according to the Common Terminology Criteria for Adverse Events. Candidate parameters were the volume of the rectum, mean dose, maximal dose, volume receiving at least 60 Gy (V₆₀), area under the DVH curve up to 25 Gy and area under the DVH curve up to 75 Gy in dependence of each chosen rectum definition. Multivariable logistic regression considered other clinical factors such as pelvine lymphatics vs local target volume, diabetes, prior rectal surgery, anticoagulation or haemorrhoids too.

RESULTS

In Cohort 1 (primary EBRT), the mean rectal volumes for definitions "RTOG", planning target volume "(PTV)-based" and "PTV-linked" were 100 cm³ [standard deviation (SD) 43 cm³], 60 cm³ (SD 26 cm³) and 74 cm³ (SD 31 cm³), respectively (p < 0.01; analysis of variance). The mean rectal doses according to these definitions were 35 Gy (SD 8 Gy), 48 Gy (SD 4 Gy) and 44 Gy (SD 5 Gy) (p < 0.01). In Cohort 2 (salvage EBRT), the mean rectal volumes were 114 cm³ (SD 47 cm³), 64 cm³ (SD 26 cm³) and 81 cm³ (SD 30 cm³) (p < 0.01) and the mean doses received by the rectum were 36 Gy (SD 8 Gy), 49 Gy (SD 5 Gy) and 44 Gy (SD 5 Gy) (p < 0.01). Acute or subacute rectal inflammation occurred in 69 (71.9%) patients in Cohort 1 and in 43 (70.5%) in Cohort 2. We did not find a correlation between all investigated DVH parameters and rectal toxicity, irrespective of the investigated definition. By adding additional variables in multivariate analysis, the predictive ability was substantially improved. Still, there was essentially no difference in the probability of predicting rectal inflammation occurrence between the tested contouring definitions.

CONCLUSION

The RTOG anatomy-based recommendations are questionable in comparison with functional definitions, as they result in higher variances in several relative DVH parameters. Moreover, the anatomy-based definition is no better and no worse in the predictive value concerning clinical end points. Advances in knowledge: Functional definitions for the rectum as OAR are easier to apply, faster to contour, have smaller variances and do not offer less information than the anatomy-based RTOG definition.

Prospective Analysis of Factors Related to Migraine Attacks: The PAMINA Study

Migraine is related to numerous factors such as hormones, stress or nutrition, but information about their actual importance is limited. Therefore, we analysed prospectively a wide spectrum of factors related to headache in migraineurs. We examined 327 migraineurs recruited via newspapers who kept a comprehensive diary for 3 months. Statistical analysis comprising 28 325 patient days and 116 dichotomous variables was based on the interval between two successive headache attacks. We calculated univariate Cox regression analyses and included covariables with a P-value of <0.05 in two stepwise multivariate Cox regression analyses, the first accounting for a correlation of the event times within a subject, the second stratified by the number of headache-free intervals. We performed similar analyses for the occurrence of migraine attacks and for the persistence of headache and migraine. Menstruation had the most prominent effect, increasing the hazard of occurrence or persistence of headache and migraine by up to 96%. All other factors changed the hazard by <35%. The two days before menstruation and muscle tension in the neck, psychic tension, tiredness, noise and odours on days before headache onset increased the hazard of headache or migraine, whereas days off, a divorced marriage, relaxation after stress, and consumption of beer decreased the hazard. In addition, three meteorological factors increased and two others decreased the hazard. In conclusion, menstruation is most important in increasing the risk of occurrence and persistence of headache and migraine. Other factors increase the risk less markedly or decrease the risk.

Precision of maximum likelihood estimation in adaptive designs

There has been increasing interest in trials that allow for design adaptations like sample size reassessment or treatment selection at an interim analysis. Ignoring the adaptive and multiplicity issues in such designs leads to an inflation of the type 1 error rate, and treatment effect estimates based on the maximum likelihood principle become biased. Whereas the methodological issues concerning hypothesis testing are well understood, it is not clear how to deal with parameter estimation in designs were adaptation rules are not fixed in advanced so that, in practice, the maximum likelihood estimate (MLE) is used. It is therefore important to understand the behavior of the MLE in such designs. The investigation of Bias and mean squared error (MSE) is complicated by the fact that the adaptation rules need not be fully specified in advance and, hence, are usually unknown. To investigate Bias and MSE under such circumstances, we search for the sample size reassessment and selection rules that lead to the maximum Bias or maximum MSE. Generally, this leads to an overestimation of Bias and MSE, which can be reduced by imposing realistic constraints on the rules like, for example, a maximum sample size. We consider designs that start with k treatment groups and a common control and where selection of a single treatment and control is performed at the interim analysis with the possibility to reassess each of the sample sizes. We consider the case of unlimited sample size reassessments as well as several realistically restricted sample size reassessment rules. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Evaluation of an integrated care program for schizophrenia: concept and study design

Concept and design of an independent scientific evaluation of different pathways of care for schizophrenia patients in Germany with respect to effectiveness and efficiency are presented. In this prospective, observational study, schizophrenia patients receiving an integrated care treatment, the intervention group (IG), are compared with patients under routine care conditions treated by the same physician (first control group, CG 1). A second control group (CG 2) of patients treated by office-based psychiatrists not participating in the integrated care program will be recruited and their data compared with the two other groups. The total amount of psychiatric hospital days after 12 months is defined as primary outcome parameter. Secondary outcome parameters comprise the frequency of psychiatric inpatient readmissions, severity of schizophrenia symptoms, remission rates and quality of life. Patients undergo assessments at baseline, month 6 and 12 using standardized and experimental questionnaires. Routine data of a regional German social health insurance fund complement information on included patients. Additionally, a cost-effectiveness and cost-utility analysis will be performed. Until now, 137 psychiatrists included 980 patients in the integrated care project in Lower Saxony, Germany, and 47 psychiatrists (IG and both CGs) are willing to participate in the independent evaluation. For the first time, a prospective observational controlled evaluation study of a countrywide integrated care project planning to recruit 500 schizophrenia patients has started using comprehensive assessments as well as routine data of a social health insurance fund.

Adaptive seamless designs with interim treatment selection: a case study in oncology

The planning of an oncology clinical trial with a seamless phase II/III adaptive design is discussed. Two regimens of an experimental treatment are compared to a control at an interim analysis, and the most-promising regimen is selected to continue, together with control, until the end of the study. Because the primary endpoint is expected to be immature at the interim regimen selection analysis, designs that incorporate primary as well as surrogate endpoints in the regimen selection process are considered. The final testing of efficacy at the end of the study comparing the selected regimen to the control with respect to the primary endpoint uses all relevant data collected both before and after the regimen selection analysis. Several approaches for testing the primary hypothesis are assessed with regard to power and type I error rate. Because the operating characteristics of these designs depend on the specific regimen selection rules considered, benchmark scenarios are proposed in which a perfect surrogate and no surrogate is used at the regimen selection analysis. The operating characteristics of these benchmark scenarios provide a range where those of the actual study design are expected to lie. A discussion on family-wise error rate control for testing primary and key secondary endpoints as well as an assessment of bias in the final treatment effect estimate for the selected regimen are also presented.

A new class of powerful and informative simultaneous confidence intervals

Step-down tests uniformly improve single-step tests with regard to power and the average number of rejected hypotheses. However, when extended to simultaneous confidence intervals (SCIs), the resulting SCIs often provide no additional information to the sheer hypothesis test. We speak, in this case, of a non-informative rejection. Non-informative rejections are particularly problematic in clinical trials with multiple treatments, where an informative rejection is required to obtain useful estimates of the treatment effects. The extension of single-step tests to confidence intervals does not have this deficiency. As a consequence, step-down tests, when extended to SCIs, do not uniformly improve single-step tests with regard to informative rejections. To overcome this deficiency, we suggest the construction of a new class of simultaneous confidence intervals that uniformly improve the Bonferroni and Holm SCIs with regard to informative rejections. This can be achieved using a dual family of weighted Bonferroni tests, with the weights depending continuously on the parameter values. We provide a simple algorithm for these computations and show that the resulting lower confidence bounds have an attractive shrinkage property. The method is extended to union-intersection tests, such as the Dunnett procedure, and is investigated in a comparative simulation study. We further illustrate the utility of the method with an example from a real clinical trial in which two experimental treatments are compared with an active comparator with respect to non-inferiority and superiority.

Informative simultaneous confidence intervals in hierarchical testing

BACKGROUND AND OBJECTIVES

In clinical trials involving multiple tests it is often difficult to obtain informative simultaneous confidence intervals (SCIs). In particular in hierarchical testing, no quantification of effects is possible for the first tested (and most important) hypothesis after its rejection. Our goal is a construction of SCIs that are always informative.

METHODS

We present an approach where the level is split after rejection of each hypothesis to obtain an informative confidence bound. The splitting weights are continuous functions of the parameters. Our method is realizable by a simple algorithm and is illustrated by an intuitive graphical representation.

RESULTS

We show theoretically and by an example that the new SCIs always provide information when a hypothesis is rejected. The power to reject the first hypothesis is not smaller than for the classical fixed-sequence procedure. The price for the extra information is a small power loss in the hypotheses proceeding the most important one.

CONCLUSIONS

Given the substantial gain in information, a small loss of power for the non-primary hypotheses seems often acceptable. Especially in the context of non-inferiority trials, this method is a useful alternative. The flexibility in the choice of the weight functions makes the procedure attractive for applications.

Empirical Bayes estimation of the selected treatment mean for two-stage drop-the-loser trials: a meta-analytic approach

Point estimation for the selected treatment in a two-stage drop-the-loser trial is not straightforward because a substantial bias can be induced in the standard maximum likelihood estimate (MLE) through the first stage selection process. Research has generally focused on alternative estimation strategies that apply a bias correction to the MLE; however, such estimators can have a large mean squared error. Carreras and Brannath (Stat. Med. 32:1677-90) have recently proposed using a special form of shrinkage estimation in this context. Given certain assumptions, their estimator is shown to dominate the MLE in terms of mean squared error loss, which provides a very powerful argument for its use in practice. In this paper, we suggest the use of a more general form of shrinkage estimation in drop-the-loser trials that has parallels with model fitting in the area of meta-analysis. Several estimators are identified and are shown to perform favourably to Carreras and Brannath's original estimator and the MLE. However, they necessitate either explicit estimation of an additional parameter measuring the heterogeneity between treatment effects or a quite unnatural prior distribution for the treatment effects that can only be specified after the first stage data has been observed. Shrinkage methods are a powerful tool for accurately quantifying treatment effects in multi-arm clinical trials, and further research is needed to understand how to maximise their utility.

Opportunities and challenges of combined effect measures based on prioritized outcomes

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.

New diagnostic markers in salivary gland tumors

Parotid gland tumors are a rare and heterogeneous entity. Molecular markers are sparse. The aim of the study was to identify new diagnostic markers in benign and malignant salivary tumors. A tissue microarray was constructed with 158 tumor samples. Expression of 21 tumor antigens involved in tumor cell survival and known for prognostic potential was assessed immunohistochemically in all parotid gland samples. CEA, Cox-1, Cox-2, Sigma, beta-Catenin, WISP-1 and PDGF-beta were differently regulated in benign and malignant parotid tumors. Subsequently, these seven proteins entered the step-wise logistic regression analysis. As a second step, we defined a score for differentiating benign versus malignant parotid lesions: 4*CEA+15*Cox-1+4*Cox-2+4*Sigma+3*PDGF-beta+10*beta-Catenin+14*Wisp1. Sensitivity and specificity of 94 and 83% were reached. Besides routine hematoxylin and eosin staining, definition of new diagnostic markers and subsequently a new diagnostic score are an attempt to create an additional tool for the diagnosis of parotid gland tumors.

Group sequential designs for three-arm 'gold standard' non-inferiority trials with fixed margin

In the recent years there have been numerous publications on the design and the analysis of three-arm 'gold standard' noninferiority trials. Whenever feasible, regulatory authorities recommend the use of such three-arm designs including a test treatment, an active control, and a placebo. Nevertheless, it is desirable in many respects, for example, ethical reasons, to keep the placebo group size as small as possible. We first give a short overview on the fixed sample size design of a three-arm noninferiority trial with normally distributed outcomes and a fixed noninferiority margin. An optimal single stage design is derived that should serve as a benchmark for the group sequential designs proposed in the main part of this work. It turns out, that the number of patients allocated to placebo is substantially low for the optimal design. Subsequently, approaches for group sequential designs aiming to further reduce the expected sample sizes are presented. By means of choosing different rejection boundaries for the respective null hypotheses, we obtain designs with quite different operating characteristics. We illustrate the approaches via numerical calculations and a comparison with the optimal single stage design. Furthermore, we derive approximately optimal boundaries for different goals, for example, to reduce the overall average sample size. The results show that the implementation of a group sequential design further improves the optimal single stage design. Besides cost and time savings, the possible early termination of the placebo arm is a key advantage that could help to overcome ethical concerns.