Effectiveness of a peer-supported crisis intervention to reduce the proportion of compulsory admissions in acute psychiatric crisis interventions in an outreach and outpatient setting: study protocol for an exploratory cluster randomised trial combined with qualitative methods.

INTRODUCTION: Compulsory admissions are associated with feelings of fear, humiliation and powerlessness. The number of compulsory admissions in Germany and other high-income countries has increased in recent years. Peer support has been shown to increase the self-efficacy of individuals with mental health conditions in acute crises and to reduce the use of coercive measures in clinical settings. The objective of this study is to reduce the number of compulsory admissions by involving peer support workers (PSWs) in acute mental health crises in outreach and outpatient settings. METHODS AND ANALYSIS: This one-year intervention is an exploratory, cluster randomised study. Trained PSWs will join the public crisis intervention services (CIS) in two of five regions (the intervention regions) in the city of Bremen (Germany). PSWs will participate in crisis interventions and aspects of the mental health services. They will be involved in developing and conducting an antistigma training for police officers. The remaining three regions will serve as control regions. All individuals aged 18 and older who experience an acute mental health crisis during the operating hours of the regional CIS in the city of Bremen (around 2000 in previous years) will be included in the study. Semistructured interviews will be conducted with PSWs, 30 patients from control and intervention regions, as well as two focus group discussions with CIS staff. A descriptive comparison between all participants in the intervention and control regions will assess the proportion of compulsory admissions in crisis interventions during the baseline and intervention years, including an analysis of temporal changes. ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of the University of Bremen (file 2022-09) on 20 June 2022. The results will be presented via scientific conferences, scientific journals and communicated to policy-makers and practitioners. TRIAL REGISTRATION NUMBER: DRKS00029377.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Assessing consistency in clinical trials with two subgroups and binary endpoints: A new test within the logistic regression model.

In late stage drug development, the experimental drug is tested in a diverse study population within the relevant indication. In order to receive marketing authorization, robust evidence for the therapeutic efficacy is crucial requiring investigation of treatment effects in well-defined subgroups. Conventionally, consistency analyses in subgroups have been performed by means of interaction tests. However, the interaction test can only reject the null hypothesis of equivalence and not confirm consistency. Simulation studies suggest that the interaction test has low power but can also be oversensitive depending on sample size-leading in combination with the actually ill-posed null hypothesis to findings regardless of clinical relevance. In order to overcome these disadvantages in the setup of binary endpoints, we propose to use a consistency test based on the interval inclusion principle, which is able to reject heterogeneity and confirm consistency of subgroup-specific treatment effects while controlling the type I error. This homogeneity test is based upon the deviation between overall treatment effect and subgroup-specific effects on the odds ratio scale and is compared with an equivalence test based on the ratio of both subgroup-specific effects. Performance of these consistency tests is assessed in a simulation study. In addition, the consistency tests are outlined for the relative risk regression. The proposed homogeneity test reaches sufficient power in realistic scenarios with small interactions. As expected, power decreases for unbalanced subgroups, lower sample sizes, and narrower margins. Severe interactions are covered by the null hypothesis and are more likely to be rejected the stronger they are.

[Older patients' involvement in research (INVOLVE-Clin): a study protocol]. / Aktive Beteiligung von älteren Patient*innen an klinischer Forschung (INVOLVE-Clin): Ein Studienprotokoll.

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.

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.

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.

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.

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 paper 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 design

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.