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Model-based recursive partitioning (MOB) can be used to identify subgroups with differing treatment effects. The detection rate of treatment-by-covariate interactions and the accuracy of identified subgroups using MOB depend strongly on the sample size. Using data from multiple randomized controlled clinical trials can overcome the problem of too small samples. However, naively pooling data from multiple trials may result in the identification of spurious subgroups as differences in study design, subject selection and other sources of between-trial heterogeneity are ignored. In order to account for between-trial heterogeneity in individual participant data (IPD) meta-analysis random-effect models are frequently used. Commonly, heterogeneity in the treatment effect is modelled using random effects whereas heterogeneity in the baseline risks is modelled by either fixed effects or random effects. In this article, we propose metaMOB, a procedure using the generalized mixed-effects model tree (GLMM tree) algorithm for subgroup identification in IPD meta-analysis. Although the application of metaMOB is potentially wider, e.g. randomized experiments with participants in social sciences or preclinical experiments in life sciences, we focus on randomized controlled clinical trials. In a simulation study, metaMOB outperformed GLMM trees assuming a random intercept only and model-based recursive partitioning (MOB), whose algorithm is the basis for GLMM trees, with respect to the false discovery rates, accuracy of identified subgroups and accuracy of estimated treatment effect. The most robust and therefore most promising method is metaMOB with fixed effects for modelling the between-trial heterogeneity in the baseline risks.

Die Rolle von Ethikkommissionen bei der Bewertung klinischer Arzneimittelprüfungen hat sich im Laufe der Jahre gewandelt. Aus den kollegialen Beratungsgremien wurden Patientenschutzorganisationen mit Behördencharakter. Während das ärztliche Berufsrecht in Deutschland bei biomedizinischer Forschung am Menschen lediglich eine Beratungspflicht für Ärzte vorsieht, bei der eine ablehnende Stellungnahme in rechtlicher Hinsicht nicht zu einer Unzulässigkeit des Forschungsvorhabens führt, verlangt das Arzneimittelgesetz (AMG) eine zustimmende Bewertung durch die zuständige Ethikkommission. Das AMG definiert sowohl die durch die Ethikkommission zu prüfenden Elemente eines Antrages für eine klinische Arzneimittelprüfung als auch abschließend die Gründe, anhand derer die zustimmende Bewertung versagt werden kann. Ethikkommissionen, die Anträge gemäß AMG bewerten, müssen nach Landesrecht gebildet sein und sind interdisziplinär mit Fachärzten, Juristen und Methodikern besetzt. Wesentliche Bewertungsinhalte sind ein ärztlich vertretbares Nutzen-Risiko-Verhältnis, die Angemessenheit der verwendeten Methoden einschließlich biometrischer Aspekte, die Anforderungen an die Studienteilnehmer, wie z. B. Einwilligungsfähigkeit, die Eignung der Prüfer und Prüfeinrichtungen sowie die Angemessenheit der schriftlichen Informationen, mit denen Studienteilnehmer informiert werden und sie ihre Einwilligung erteilen sollen. Trotz der bereits jetzt hohen Regelungsdichte werden zukünftig mit der Anwendbarkeit der Verordnung (EU) Nr. 536/2014 die Anforderungen an Zusammensetzung und Arbeitsweise von Ethikkommissionen noch detaillierter gesetzlich festgelegt mit dem Ziel, die Bewertung klinischer Arzneimittelprüfungen in der EU weiter zu harmonisieren.

Therapeutic innovation & regulatory science: official journal of DIA 56(2), 244-254 (2022). doi:10.1007/s43441-021-00352-2, Published by Springer Nature, [New York]

Neuroinflammation, a key early pathomechanistic alteration of Alzheimer’s disease, may represent either a detrimental or a compensatory mechanism or both (according to the disease stage). YKL-40, a glycoprotein highly expressed in differentiated glial cells, is a candidate biomarker for in vivo tracking neuroinflammation in humans. We performed a longitudinal study in a monocentric cohort of cognitively healthy individuals at risk for Alzheimer’s disease exploring whether age, sex, and the apolipoprotein E e4 allele affect plasma YKL-40 concentrations. We investigated whether YKL-40 is associated with brain amyloid-β (Aβ) deposition, neuronal activity, and neurodegeneration as assessed via neuroimaging biomarkers. Finally, we investigated whether YKL-40 may predict cognitive performance. We found an age-associated increase of YKL-40 and observed that men display higher concentrations than women, indicating a potential sexual dimorphism. Moreover, YKL-40 was positively associated with memory performance and negatively associated with brain Aβ deposition (but not with metabolic signal). Consistent with translational studies, our results suggest a potentially protective effect of glia on incipient brain Aβ accumulation and neuronal homeostasis.

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.

Hierarchical random effect models are used for different purposes in clinical research and other areas. In general, the main focus is on population parameters related to the expected treatment effects or group differences among all units of an upper level (e.g. subjects in many settings). Optimal design for estimation of population parameters are well established for many models. However, optimal designs for the prediction for the individual units may be different. Several settings are identified in which individual prediction may be of interest. In this paper, we determine optimal designs for the individual predictions, e.g. in multi-cluster trials or in trials that investigate a new treatment in a number of different subpopulations, and compare them to a conventional balanced design with respect to treatment allocation. Our investigations show that in the case of uncorrelated cluster intercepts and cluster treatments the optimal allocations are far from being balanced if the treatment effects vary strongly as compared to the residual error and more subjects should be recruited to the active (new) treatment. Nevertheless, efficiency loss may be limited resulting in a moderate sample size increase when individual predictions are foreseen with a balanced allocation.

The aim of diagnostic accuracy studies is to evaluate how accurately a diagnostic test can distinguish diseased from nondiseased individuals. Depending on the research question, different study designs and accuracy measures are appropriate. As the prior knowledge in the planning phase is often very limited, modifications of design aspects such as the sample size during the ongoing trial could increase the efficiency of diagnostic trials. In intervention studies, group sequential and adaptive designs are well established. Such designs are characterized by preplanned interim analyses, giving the opportunity to stop early for efficacy or futility or to modify elements of the study design. In contrast, in diagnostic accuracy studies, such flexible designs are less common, even if they are as important as for intervention studies. However, diagnostic accuracy studies have specific features, which may require adaptations of the statistical methods or may lead to specific advantages or limitations of sequential and adaptive designs. In this article, we summarize the current status of methodological research and applications of flexible designs in diagnostic accuracy research. Furthermore, we indicate and advocate future development of adaptive design methodology and their use in diagnostic accuracy trials from an interdisciplinary viewpoint. The term "interdisciplinary viewpoint" describes the collaboration of experts of the academic and nonacademic research. peerReviewed

Umbrella trials have been suggested to increase trial conduct efficiency when investigating different biomarker-driven experimental therapies. An overarching platform is used for patient screening and subsequent subtrial allocation according to patients' biomarker status. Two subtrial allocation schemes for patients with a positive test result for multiple biomarkers are (i) the pragmatic allocation to the eligible subtrial with the currently fewest included patients and (ii) the random allocation to one of the eligible subtrials. Obviously, the subtrials compete for such patients which are consequently underrepresented in the subtrials. To address questions of the impact of an umbrella design in general as well as with respect to subtrial allocation and analysis method, we investigate an umbrella trial with two parallel group subtrials and discuss generalisations. First, we analytically quantify the impact of the umbrella design with random allocation on the number of patients needed to be screened, the biomarker status distribution and treatment effect estimates compared to the corresponding gold standard of an independent parallel group design. Using simulations and real data, we subsequently compare both allocation schemes and investigate weighted linear regression modelling as possible analysis method for the umbrella design. Our results show that umbrella designs are more efficient than the gold standard. However, depending on the biomarker status distribution in the disease population, an umbrella design can introduce differences in estimated treatment effects in the presence of an interaction between treatment and biomarker status. In principle, weighted linear regression together with the random allocation scheme can address this difference though it is difficult to assess if such an approach is applicable in practice. In any case, caution is required when using treatment effect estimates derived from umbrella designs for e.g. future trial planning or meta-analyses.

Enrichment designs that select placebo nonresponders have gained much attention during the last years in areas with high placebo response rates, eg, in depression. Proposals were made that re-randomize patients who did not respond to placebo during a first study phase as the sequential parallel design (SPD). This design uses in a second phase an enriched patient population where the treatment effect is expected to be more pronounced. This may be problematic if an effect in the overall population is claimed. Proposals were made to combine the treatment effects in the overall population from study phase 1 and the enriched population from study phase 2, alleviating but not solving the issue of a potential selection bias. This paper shows how this bias corresponding to the effect difference between the overall population and the enriched population depends on the variability of a potential subject-by-treatment interaction. Sample sizes are given, which lead to a significant result in the combining test with a given probability if actually the average effect in the overall population is zero. If, on the other hand, no subject-by-treatment interaction is given, the enrichment is shown to be inefficient. We conclude that enrichment designs using placebo nonresponders are not able to claim a positive average effect in the overall population if a subject-by-treatment interaction cannot be excluded. It cannot be used to demonstrate positive efficacy in the overall population in a pivotal phase III trial but may be used in early phases to demonstrate varying treatment effects between patients.

Recently, new draft guidelines on multiplicity issues in clinical trials have been issued by European Medicine Agency (EMA) and Food and Drug Administration (FDA), respectively. Multiplicity is an issue in clinical trials, if the probability of a false-positive decision is increased by insufficiently accounting for testing multiple hypotheses. We outline the regulatory principles related to multiplicity issues in confirmatory clinical trials intended to support a marketing authorization application in the EU, describe the reasons for an increasing complexity regarding multiple hypotheses testing and discuss the specific multiplicity issues emerging within the regulatory context and being relevant for drug approval.

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late-stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well-established and regulatory-acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4-5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP-Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)-based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well-designed dose-finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Blinded sample size re-estimation and information monitoring based on blinded data has been suggested to mitigate risks due to planning uncertainties regarding nuisance parameters. Motivated by a randomized controlled trial in pediatric multiple sclerosis (MS), a continuous monitoring procedure for overdispersed count data was proposed recently. However, this procedure assumed constant event rates, an assumption often not met in practice. Here we extend the procedure to accommodate time trends in the event rates considering two blinded approaches: (a) the mixture approach modeling the number of events by a mixture of two negative binomial distributions and (b) the lumping approach approximating the marginal distribution of the event counts by a negative binomial distribution. Through simulations the operating characteristics of the proposed procedures are investigated under decreasing event rates. We find that the type I error rate is not inflated relevantly by either of the monitoring procedures, with the exception of strong time dependencies where the procedure assuming constant rates exhibits some inflation. Furthermore, the procedure accommodating time trends has generally favorable power properties compared with the procedure based on constant rates which stops often too late. The proposed method is illustrated by the clinical trial in pediatric MS.

Introduction Blood-based biomarkers of pathophysiological brain amyloid β (Aβ) accumulation, particularly for preclinical target and large-scale interventions, are warranted to effectively enrich Alzheimer's disease clinical trials and management. Methods We investigated whether plasma concentrations of the Aβ 1–40 /Aβ 1–42 ratio, assessed using the single-molecule array (Simoa) immunoassay, may predict brain Aβ positron emission tomography status in a large-scale longitudinal monocentric cohort (N = 276) of older individuals with subjective memory complaints. We performed a hypothesis-driven investigation followed by a no-a-priori hypothesis study using machine learning. Results The receiver operating characteristic curve and machine learning showed a balanced accuracy of 76.5% and 81%, respectively, for the plasma Aβ 1–40 /Aβ 1–42 ratio. The accuracy is not affected by the apolipoprotein E ( APOE ) e4 allele, sex, or age. Discussion Our results encourage an independent validation cohort study to confirm the indication that the plasma Aβ 1–40 /Aβ 1–42 ratio, assessed via Simoa, may improve future standard of care and clinical trial design.

Over the years, the role of ethics committees (ECs) in the review process of clinical trial applications (CTAs) has changed from being a collegial advisory body to a patient protection organisation with an authority character. While the law governing the medical profession in Germany only provides for an obligation for physicians to ask for an EC review in biomedical research on human beings, a negative opinion on the CTA does not lead to the inadmissibility of the research project from a legal point of view. In contrast, the German Medicinal Product Act (Arzneimittelgesetz, AMG) requires a favourable opinion as an approving assessment by the competent EC.The AMG defines both the elements of a clinical trial application to be reviewed by the EC as well as the principle grounds of non-acceptance to reject a favourable opinion. ECs that assess CTAs must be constituted in accordance with the state law and must be composed of interdisciplinary medical specialists, lawyers and methodologists. The main assessment criteria are a medically acceptable risk-benefit ratio, the appropriateness of the methods used, including biometric aspects, the requirements to be met by the study participants, such as their ability to give consent, the suitability of the investigators and trial facilities as well as the appropriateness of the written information with which the study participants are to be informed and give their consent.In spite of the already high degree of regulation, the applicability of the European Clinical Trial Regulation will result in even more detailed legal requirements for the composition and working procedures of an EC with the aim of further harmonising the assessment of CTAs in the EU.

In the European Union (EU), responsibilities for medicines and companion diagnostics (CDxs) are divided. The European Medicines Agency (EMA) and national agencies approve medicines, whereas notified bodies (NBs) certify CDx. The EU regulation on in vitro diagnostic medical devices of 2017 mandates NBs to consult regulatory authorities, usually the EMA, during the certification of a CDx. This consultation is required for an innovative CDx based on a new biomarker and introduced together with an innovative medicine, as well as for any new “generic” CDx for the same well-known biomarker and using the same methodological approach as an established reference CDx. EMA’s view how the CDx affect the benefit–risk balance of the corresponding medicines may well change NBs’ certification decisions. This chapter delineates EMA’s current and possible future principles in the assessment of the impact of CDx on the benefit–risk balance of medicines.

Randomized controlled trials (RCTs) aim at providing reliable estimates of treatment benefit. Missing data and nonadherence to treatment are distinct problems that can substantially impede this task. In practice, the fact that the handling of missing data due to nonadherence affects the question that is addressed is often ignored. Estimands allow precisely predefining the question of interest. Estimands are definitions of that which is being estimated with regard to population, endpoint, and handling of postrandomization events (eg, nonadherence). Depending on the situation, different estimands are of relevance. Therefore, it is important that the intention-to-treat (ITT) principle, which is considered the gold standard for analyzing RCTs, does not restrict an RCT's primary objective to only one of several relevant estimands. Although much ambiguity is involved around what is considered to constitute the ITT principle, many associate ITT with completely following up all patients and including all data of all randomized patients as allocated into the analysis. This would restrict primary objectives to estimating the effect of treatment policy and is certainly not warranted in all situations. To maintain the advantage of having the clear recommendation to follow the ITT principle while allowing for various relevant estimands as primary objective, we argue that the appropriate way forward is to define ITT as including all randomized patients into the analysis set and each patient is to be allocated to his or her randomized treatment. This definition comprises the actual intent of ITT and can be fully implemented also in settings where complete follow-up is impossible.

During this decade, breakthrough conceptual shifts have commenced to emerge in the field of Alzheimer’s disease (AD) recognizing risk factors and the non-linear dynamic continuum of complex pathophysiologies amongst a wide dimensional spectrum of multi-factorial brain proteinopathies/neurodegenerative diseases. As is the case in most fields of medicine, substantial advancements in detecting, treating and preventing AD will likely evolve from the generation and implementation of a systematic precision medicine strategy. This approach will likely be based on the success found from more advanced research fields, such as oncology. Precision medicine will require integration and transfertilization across fragmented specialities of medicine and direct reintegration of Neuroscience, Neurology and Psychiatry into a continuum of medical sciences away from the silo approach. Precision medicine is biomarker-guided medicine on systems-levels that takes into account methodological advancements and discoveries of the comprehensive pathophysiological profiles of complex multi-factorial neurodegenerative diseases, such as late-onset sporadic AD. This will allow identifying and characterizing the disease processes at the asymptomatic preclinical stage, where pathophysiological and topographical abnormalities precede overt clinical symptoms by many years to decades. In this respect, the uncharted territory of the AD preclinical stage has become a major research challenge as the field postulates that early biomarker guided customized interventions may offer the best chance of therapeutic success. Clarification and practical operationalization is needed for comprehensive dissection and classification of interacting and converging disease mechanisms, description of genomic and epigenetic drivers, natural history trajectories through space and time, surrogate biomarkers and indicators of risk and progression, as well as considerations about the regulatory, ethical, political and societal consequences of early detection at asymptomatic stages. In this scenario, the integrated roles of genome sequencing, investigations of comprehensive fluid-based biomarkers and multimodal neuroimaging will be of key importance for the identification of distinct molecular mechanisms and signaling pathways in subsets of asymptomatic people at greatest risk for progression to clinical milestones due to those specific pathways. The precision medicine strategy facilitates a paradigm shift in Neuroscience and AD research and development away from the classical “one-size-fits-all” approach in drug discovery towards biomarker guided “molecularly” tailored therapy for truly effective treatment and prevention options. After the long and winding decade of failed therapy trials progress towards the holistic systems-based strategy of precision medicine may finally turn into the new age of scientific and medical success curbing the global AD epidemic.

With advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for subgroup identification were proposed over the last years. Although overviews of subgroup identification methods are available, systematic comparisons of their performance in simulation studies are rare. Interaction trees (IT), model-based recursive partitioning, subgroup identification based on differential effect, simultaneous threshold interaction modeling algorithm (STIMA), and adaptive refinement by directed peeling were proposed for subgroup identification. We compared these methods in a simulation study using a structured approach. In order to identify a target population for subsequent trials, a selection of the identified subgroups is needed. Therefore, we propose a subgroup criterion leading to a target subgroup consisting of the identified subgroups with an estimated treatment difference no less than a pre-specified threshold. In our simulation study, we evaluated these methods by considering measures for binary classification, like sensitivity and specificity. In settings with large effects or huge sample sizes, most methods perform well. For more realistic settings in drug development involving data from a single trial only, however, none of the methods seems suitable for selecting a target population. Using the subgroup criterion as alternative to the proposed pruning procedures, STIMA and IT can improve their performance in some settings. The methods and the subgroup criterion are illustrated by an application in amyotrophic lateral sclerosis. peerReviewed

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017. The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods., Comment: 9 pages, 3 figures

The analysis of adverse events (AEs) is a key component in the assessment of a drug's safety profile. Inappropriate analysis methods may result in misleading conclusions about a therapy's safety and consequently its benefit-risk ratio. The statistical analysis of AEs is complicated by the fact that the follow-up times can vary between the patients included in a clinical trial. This paper takes as its focus the analysis of AE data in the presence of varying follow-up times within the benefit assessment of therapeutic interventions. Instead of approaching this issue directly and solely from an analysis point of view, we first discuss what should be estimated in the context of safety data, leading to the concept of estimands. Although the current discussion on estimands is mainly related to efficacy evaluation, the concept is applicable to safety endpoints as well. Within the framework of estimands, we present statistical methods for analysing AEs with the focus being on the time to the occurrence of the first AE of a specific type. We give recommendations which estimators should be used for the estimands described. Furthermore, we state practical implications of the analysis of AEs in clinical trials and give an overview of examples across different indications. We also provide a review of current practices of health technology assessment (HTA) agencies with respect to the evaluation of safety data. Finally, we describe problems with meta-analyses of AE data and sketch possible solutions. peerReviewed

The complex multifactorial nature of polygenic Alzheimer's disease (AD) presents significant challenges for drug development. AD pathophysiology is progressing in a non-linear dynamic fashion across multiple systems levels - from molecules to organ systems - and through adaptation, to compensation, and decompensation to systems failure. Adaptation and compensation maintain homeostasis: a dynamic equilibrium resulting from the dynamic non-linear interaction between genome, epigenome, and environment. An individual vulnerability to stressors exists on the basis of individual triggers, drivers, and thresholds accounting for the initiation and failure of adaptive and compensatory responses. Consequently, the distinct pattern of AD pathophysiology in space and time must be investigated on the basis of the individual biological makeup. This requires the implementation of systems biology and neurophysiology to facilitate Precision Medicine (PM) and Precision Pharmacology (PP). The regulation of several processes at multiple levels of complexity from gene expression to cellular cycle to tissue repair and system-wide network activation has different time delays (temporal scale) according to the affected systems (spatial scale). The initial failure might originate and occur at every level potentially affecting the whole dynamic interrelated systems within an organism. Unraveling the spatial and temporal dynamics of non-linear pathophysiological mechanisms across the continuum of hierarchical self-organized systems levels and from systems homeostasis to systems failure is key to understand AD. Measuring and, possibly, controlling space- and time-scaled adaptive and compensatory responses occurring during AD will represent a crucial step to achieve the capacity to substantially modify the disease course and progression at the best suitable timepoints, thus counteracting disrupting critical pathophysiological inputs. This approach will provide the conceptual basis for effective disease-modifying pathway-based targeted therapies. PP is based on an exploratory and integrative strategy to complex diseases such as brain proteinopathies including AD, aimed at identifying simultaneous aberrant molecular pathways and predicting their temporal impact on the systems levels. The depiction of pathway-based molecular signatures of complex diseases contributes to the accurate and mechanistic stratification of distinct subcohorts of individuals at the earliest compensatory stage when treatment intervention may reverse, stop, or delay the disease. In addition, individualized drug selection may optimize treatment safety by decreasing risk and amplitude of side effects and adverse reactions. From a methodological point of view, comprehensive "omics"-based biomarkers will guide the exploration of spatio-temporal systems-wide morpho-functional shifts along the continuum of AD pathophysiology, from adaptation to irreversible failure. The Alzheimer Precision Medicine Initiative (APMI) and the APMI cohort program (APMI-CP) have commenced to facilitate a paradigm shift towards effective drug discovery and development in AD.

Lack of adherence to study protocol and missing data are often unavoidable in clinical trials, and both increase the need to differentiate between the ideal treatment effect if the medication is taken as directed and the treatment effect in presence of the actual adherence pattern. In this regard, estimands have become the focus of attention. An estimand is simply that which is being estimated. In the context of treatment benefit, an estimand may address either efficacy or effectiveness aspects. Defining the estimand of interest is an essential step to take before deciding on trial design and primary analysis. The choice of estimand has consequences for various other factors to be considered during any clinical trial's planning phase. This study presents a process chart including all aspects to consider during planning. After deciding on the primary estimand, the trial design should be specified, followed by the primary analysis. Both should appropriately address the chosen estimand. Finally, sensitivity analyses should be taken into account. Provided are suggestions for all the planning steps involved, especially on choosing between efficacy and effectiveness, and relevant examples from clinical practice to illustrate them. It is recommended that one bear in mind the process chart during planning of any clinical trial and give reasonable justification for each decision in the study protocol.

Genotoxic carcinogens pose great hazard to human health. Uncertainty of current risk assessment strategies and long latency periods between first carcinogen exposure and diagnosis of tumors have raised interest in predictive biomarkers. Initial DNA adduct formation is a necessary step for genotoxin induced carcinogenesis. However, as DNA adducts not always translate into tumorigenesis, their predictive value is limited. Here we hypothesize that the combined analysis of pro-mutagenic DNA adducts along with time-matched gene expression changes could serve as a superior prediction tool for genotoxic carcinogenesis. Eker rats, heterozygous for the tuberous sclerosis (Tsc2) tumor suppressor gene and thus highly susceptible towards genotoxic renal carcinogens, were continuously treated with the DNA alkylating carcinogen methylazoxymethanol acetate (MAMAc). Two weeks of MAMAc treatment resulted in a time-dependent increase of O(6)-methylguanine and N7-methylguanine adducts in the kidney cortex, which was however not reflected by significant expression changes of cyto-protective genes involved in DNA repair, cell cycle arrest or apoptosis. Instead, we found a transcriptional regulation of genes involved in the tumor-related MAPK, FoxO and TGF-beta pathways. Continuous MAMAc treatment for up to 6 months resulted in a mild but significant increase of cancerous lesions. In summary, the combined analysis of DNA adducts and early gene expression changes could serve as a suitable predictive tool for genotoxicant-induced carcinogenesis. published

Summary Dose finding studies often compare several doses of a new compound with a marketed standard treatment as an active control. In the past, however, research has focused mostly on experimental designs for placebo controlled dose finding studies. To the best of our knowledge, optimal designs for dose finding studies with an active control have not been considered so far. As the statistical analysis for an active controlled dose finding study can be formulated in terms of a mixture of two regression models, the related design problem is different from what has been investigated before in the literature. We present a rigorous approach to the problem of determining optimal designs for estimating the smallest dose achieving the same treatment effect as the active control. We determine explicitly the locally optimal designs for a broad class of models employed in such studies. We also discuss robust design strategies and determine related Bayesian and standardized minimax optimal designs. We illustrate the results by investigating alternative designs for a clinical trial which has recently appeared in a consulting project of one of the authors.

Background Elderly patients are particularly vulnerable to adverse drug reactions, especially if they are affected by additional risk factors such as multimorbidity, polypharmacy, impaired renal function and intake of drugs with high risk potential. Apart from these clinical parameters, drug safety and efficacy can be influenced by pharmacogenetic factors. Evidence-based recommendations concerning drug-gene-combinations have been issued by international consortia and in drug labels. However, clinical benefit of providing information on individual patient factors in a comprehensive risk assessment aiming to reduce the occurrence and severity of adverse drug reactions is not evident. Purpose of this randomized controlled trial is to compare the effect of a concise individual risk information leaflet with standard information on risk factors for side effects. Methods/Design The trial was designed as a prospective, two-arm, randomized, controlled, multicenter, pragmatic study. 960 elderly, multimorbid outpatients in general medicine are included if they take at least one high risk and one other long-term drug (polymedication). As high risk “index drugs” oral anticoagulants and antiplatelets were chosen because of their specific, objectively assessable side effects. Following randomization, test group patients receive an individualized risk assessment leaflet evaluating their personal data concerning bleeding- and thromboembolic-risk-scores, potential drug-drug-interactions, age, renal function and pharmacogenetic factors. Control group patients obtain a standardized leaflet only containing general information on these criteria. Follow-up period is 9 months for each patient. Primary endpoint is the occurrence of a thromboembolic/bleeding event or death. Secondary endpoints are other adverse drug reactions, hospital admissions, specialist referrals and medication changes due to adverse drug reactions, the patients’ adherence to medication regimen as well as health related quality of life, mortality and resulting costs. Discussion Despite extensive evidence of risk factors for adverse drug reactions, there are few prospective trial data about an individualized risk assessment including pharmacogenetic information to increase patient safety. By conducting a health economic analysis, we will evaluate if the application of an individualized drug therapy in daily routine is cost-effective. Trial registration German Clinical Trials Register: DRKS00006256, date of registration 09/01/15.

For a paediatric development to be rationally informed by all available knowledge, it is necessary to systematically collect and learn from available data, expert knowledge and prior developments. Aspects such as drug formulation, bioanalytical methodology, pharmacokinetics/pharmacodynamics (PK/PD), study design and statistics should be considered when defining a development plan. In this context, model informed drug discovery and development (MID3) methodology [1] is likely to prove useful.

During the last 10 years the European Medicines Agency (EMA) organized a number of workshops on modeling and simulation, working towards greater integration of modeling and simulation (M&S) in the development and regulatory assessment of medicines. In the 2011 EMA - European Federation of Pharmaceutical Industries and Associations (EFPIA) Workshop on Modelling and Simulation, European regulators agreed to the necessity to build expertise to be able to review M&S data provided by companies in their dossier. This led to the establishment of the EMA Modelling and Simulation Working Group (MSWG). Also, there was agreement reached on the need for harmonization on good M&S practices and for continuing dialog across all parties. The MSWG acknowledges the initiative of the EFPIA Model-Informed Drug Discovery and Development (MID3) group in promoting greater consistency in practice, application, and documentation of M&S and considers the paper is an important contribution towards achieving this objective.

In the midst of gaining more experience in pursuing scientifically sound approaches of adaptive designs in clinical trials, a panel discussion with international representatives from industry, academia, and regulatory agencies was held at the Basel Biometric Society Spring Conference, March 12, 2010. The goal was to develop some consensus among industry, government, and academic statisticians concerning requirements and methods for adaptive designs in clinical trials. In this paper, we summarize the panelists' perspectives given at that session.

Modern drug development requires an efficient clinical development program to have a reasonable chance of successfully leading to the submission of the therapy, given that the therapy is effective, or to early stopping if this is not the case. Clinical trials and programs should be designed to effectively support this final goal. Currently, the statistical planning in drug development is based on parts of a clinical program in isolation, conditioned on one fixed setting, focusing on sample size calculation or simple design questions. There is, however, an increasing demand for a clinical program optimization and acceleration as well as an unconditional evaluation of relative program efficiency, robustness, and validity. The complexity of the development process, however, often does not allow for simple solutions, frequently requiring computer simulations to support these assessments. We propose a general framework for comparing competing options for clinical programs, trial designs, and analysis methods as a basis for decision making and to facilitate the internal and external dialogue with key stakeholders. The final decision making ultimately needs to factor in quantitative aspects as well as additional qualitative dimensions such as logistic feasibility, regulatory acceptance, and so on. A terminology is introduce that clearly describes the different aspects of such a framework, the range of underlying assumptions, the competing options, and the metrics that are used to assess and compare these options. Three specific case studies are presented that illustrate these concepts at three different levels: program planning, trial design, and analysis methods.

When assessing the effectiveness of a hormonal method of fertility control in men, the classical approach used for the assessment of hormonal contraceptives in women, by estimating the pregnancy rate or using a life-table analysis for the time to pregnancy, is difficult to apply in a clinical development program. The main reasons are the dissociation of the treated unit, i.e. the man, and the observed unit, i.e. his female partner, the high variability in the frequency of male intercourse, the logistical cost and ethical concerns related to the monitoring of the trial. A reasonable surrogate endpoint of the definite endpoint time to pregnancy is sperm count. In addition to the avoidance of the mentioned problems, trials that compare different treatments are possible with reasonable sample sizes, and study duration can be shorter. However, current products do not suppress sperm production to 100 per cent in all men and the sperm count is only observed with measurement error. Complete azoospermia might not be necessary in order to achieve an acceptable failure rate compared with other forms of male fertility control. Therefore, the use of sperm count as a surrogate endpoint must rely on the results of a previous trial in which both the definitive- and surrogate-endpoint results were assessed. The paper discusses different estimation functions of the mean pregnancy rate (corresponding to the cumulative hazard) that are based on the results of sperm count trial and a previous trial in which both sperm count and time to pregnancy were assessed, as well as the underlying assumptions. Sample size estimations are given for pregnancy rate estimation with a given precision.

Background Randomized controlled trials (RCTs) are the gold standard design of clinical research to assess interventions. However, RCTs cannot always be applied for practical or ethical reasons. To investigate the current practices in rare diseases, we review evaluations of therapeutic interventions in paediatric multiple sclerosis (MS) and Creutzfeldt-Jakob disease (CJD). In particular, we shed light on the endpoints used, the study designs implemented and the statistical methodologies applied. Methods We conducted literature searches to identify relevant primary studies. Data on study design, objectives, endpoints, patient characteristics, randomization and masking, type of intervention, control, withdrawals and statistical methodology were extracted from the selected studies. The risk of bias and the quality of the studies were assessed. Results Twelve (seven) primary studies on paediatric MS (CJD) were included in the qualitative synthesis. No double-blind, randomized placebo-controlled trial for evaluating interventions in paediatric MS has been published yet. Evidence from one open-label RCT is available. The observational studies are before-after studies or controlled studies. Three of the seven selected studies on CJD are RCTs, of which two received the maximum mark on the Oxford Quality Scale. Four trials are controlled observational studies. Conclusions Evidence from double-blind RCTs on the efficacy of treatments appears to be variable between rare diseases. With regard to paediatric conditions it remains to be seen what impact regulators will have through e.g., paediatric investigation plans. Overall, there is space for improvement by using innovative trial designs and data analysis techniques. Electronic supplementary material The online version of this article (doi:10.1186/s13023-016-0402-6) contains supplementary material, which is available to authorized users.

The purpose of this study is to compare the prevalence of depression among different types of hospital departments. Furthermore, it compares different methods for assessment of its recognition by non-psychiatric physicians.993 inpatients of internal, surgical, gynecological and physical rehabilitation wards of community hospitals were interviewed by research psychiatrists using the Clinical Interview Schedule. Ward physicians were asked to fill in a short questionnaire in order to assess whether they could correctly identify patients with mental illnesses. In addition, routine discharge diagnoses were assessed.Of the total sample, 13.3 % suffered from depression. Depression was most frequent on physical rehabilitation units (24.2 %), followed by surgical (9.8 %) and internal (9.5 %) wards. On gynecological wards, prevalence of depression was lowest (8.7 %). Of those suffering from depression, 45.7 % were identified as mentally ill by non-psychiatric ward physicians when using questionnaire data. Only 21.0 % of the depressed received a psychiatric discharge diagnosis, which equals less than half of those identified by questionnaire.Of the total sample, 13.3 % of patients suffered from depression. Depression was most frequent in physical rehabilitation units (24.2 %), followed by surgical (9.8 %) and internal (9.5 %) wards. In gynecological wards, the prevalence of depression was the lowest (8.7 %). Of those suffering from depression, 45.7 % were identified as mentally ill by non-psychiatric ward physicians when using questionnaire data. Only 21.0 % of the depressed received a psychiatric discharge diagnosis, less than half of those identified by the questionnaire.Depression is very common among inpatients of physical hospital departments. Unfortunately, depression is frequently overlooked in everyday clinical work. Routine discharge diagnoses give only very limited information about how often ward physicians recognize mental disorders. Furthermore, hospital discharge diagnoses should not be used for planning mental health services.

In a clinical dose finding study with active control a new drug with several dose levels is compared with an active comparator drug. The main focus of such studies often lies on the estimation of a target dose that leads to the same efficacy as the control. This article investigates the finite sample properties of the maximum likelihood estimation of the target dose and compares several approaches for constructing corresponding confidence intervals under the assumption of a linear dose-response curve and normal error terms. Furthermore, the impact of deviations from the model assumptions regarding the error distribution is explored. peerReviewed

In a clinical trial with an active treatment and a placebo the situation may occur that two (or even more) primary endpoints may be necessary to describe the active treatment's benefit. The focus of our interest is a more specific situation with two primary endpoints in which superiority in one of them would suffice given that non-inferiority is observed in the other. Several proposals exist in the literature for dealing with this or similar problems, but prove insufficient or inadequate at a closer look (e.g. Bloch et al. (2001, 2006) or Tamhane and Logan (2002, 2004)). For example, we were unable to find a good reason why a bootstrap p -value for superiority should depend on the initially selected non-inferiority margins or on the initially selected type I error α. We propose a hierarchical three step procedure, where non-inferiority in both variables must be proven in the first step, superiority has to be shown by a bivariate test (e.g. Holm (1979), O'Brien (1984), Hochberg (1988), a bootstrap (Wang (1998)), or Lauter (1996)) in the second step, and then superiority in at least one variable has to be verified in the third step by a corresponding univariate test. All statistical tests are performed at the same one-sided significance level α. From the above mentioned bivariate superiority tests we preferred Lauter's SS test and the Holm procedure for the reason that these have been proven to control the type I error strictly, irrespective of the correlation structure among the primary variables and the sample size applied. A simulation study reveals that the performance regarding power of the bivariate test depends to a considerable degree on the correlation and on the magnitude of the expected effects of the two primary endpoints. Therefore, the recommendation of which test to choose depends on knowledge of the possible correlation between the two primary endpoints. In general, Lauter's SS procedure in step 2 shows the best overall properties, whereas Holm's procedure shows an advantage if both a positive correlation between the two variables and a considerable difference between their standardized effect sizes can be expected. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

The current guideline of the European Agency for the Evaluation of Medicinal Products (EMEA) on the clinical investigation of steroid contraceptives in women, which was prepared by the Committee for Proprietary Medicinal Products (CPMP), calls for the calculation of a confidence interval for the Pearl Index, a widely used measure to describe the reliability of a contraceptive method. The key studies should be large enough to give a Pearl Index with a 95% confidence interval such that the difference between the upper limit of the confidence interval and the corresponding point estimate does not exceed a given margin. As a consequence of this guidance, the success probability of a Pearl Index study is given by its capability, i.e. probability to fulfil this criterion. The resulting power function based on the necessary exposure time does not increase strictly. The minimum exposure time Tmin should be calculated such that this function exceeds a given probability for all T ≥ Tmin. In this paper, the underlying model is discussed, and definitions and formulae are given for the assumption of a Poisson model. The necessary total exposure time is calculated as a function of a given true pregnancy rate. In addition, some simulations were conducted for the model where a drop-out mechanism is incorporated into the process. Assuming an exponential distribution for the time to drop-out the phenomenon that the power does not increase strictly with exposure time is less pronounced. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Summary: Purpose: Postictal psychosis is a well-known complication, occurring especially in patients with temporal lobe epilepsy. It usually runs a benign course. The literature on this topic is sparse, and the underlying pathogenic mechanisms are not known. Methods: We report five patients with temporal lobe epilepsy in whom postictal psychosis developed during the course of video-EEG monitoring; they were studied with hexamethyl-propyleneamine-oxime single-photon emission computed tomography (HMPAO-SPECT) during and after the psychotic event. Results: In comparison to the interictal state, all SPECT scans obtained during postictal psychosis were remarkable for bifrontal and bitemporal hyperperfusion patterns. Some studies also demonstrated unilateral left lateral frontal hyperperfusion. These cortical blood-flow patterns appeared to be distinct from those obtained during complex partial seizures. Conclusions: Our data suggest that postictal psychoses in patients with temporal lobe epilepsy are associated with hyperactivation of both temporal and frontal lobe structures. This hyperperfusion may reflect ongoing (subcortical) discharges, active inhibitory mechanisms that terminate the seizure, or simply a dysregulation of cerebral blood flow.

Background: To evaluate the effect of response-acquisition technique on psychometric performance in visual-field testing, the conventional one-button yes/time-out method was compared with a two-button yes/no method for responding whether or not the stimulus was detected. There are a number of situations in which the single-button technique leads to ambiguous results. In this study, we thus expected the yes/no method to reduce tendencies towards habituation and automatic responding. Our hypothesis was that the two-button technique could reduce the rate of erroneous responses. Methods: Luminance-difference sensitivity for bright stimuli (32') on a photopic background was evaluated at 26 locations within the central visual field (30°) using a specially equalised video display unit and a modified 4/2-dB staircase strategy (six reversals, maximum-likelihood threshold estimation). Sixty-one ophthalmologically normal subjects (aged 20–30 years) were examined twice with each method. Results: Mean sensitivities with the two-button yes/no method were found to be, on average, 0.13 dB above those measured with the one-button yes/time-out technique – a difference without clinical relevance. Within-subject variability did not differ between the two methods. However, the less intuitive two-button yes/no method had a slightly higher number of false responses in catch trials. Conclusion: Compared to the conventional one-button yes/time-out method, the two-button yes/no method in normal young subjects thus showed little difference in mean sensitivities and equivalent within-subject variabilities. Concerning our initial hypothesis, the yes/no method is of somewhat higher complexity and is not able to reduce the rate of erroneous responses. The one-button yes/time-out method fared a little better in error rate. In summary, the yes/no method is an alternative and additional possibility of response acquisition in visual-field testing, which is worthy of being tested in a clinical study with elderly subjects.

Fundus-oriented perimetry (FOP) was used to evaluate the effectiveness of different-sized bright and dark stimuli in detecting and quantitatively measuring angioscotoma. The foveolas and optic disks of digitized fundus images were aligned with their psychophysical counterparts to construct individual grids of perimetric stimuli. Each grid included a linear set of test point locations crossing a retinal vessel. Angioscotomas immediately became visible in nine of 13 healthy normal volunteers tested with FOP. Additional mathematical processing of local loss of differential light sensitivity (dls) disclosed an angioscotoma for at least one stimulus condition in all persons tested. The angioscomas were usually deeper for small (12′) targets than for large (32′) ones. On the other hand, the overall noise at dls thresholds was generally higher for small than for large stimuli regardless of whether the stimuli were bright or dark. No noteworthy differences were found in detection rates or signal-to-noise ratios under different stimulus conditions (dark/bright/small/large). FOP permits the individual arrangement of stimuli for specific morphological conditions and is thus capable of detecting even minute visual field defects such as angioscotomas.

To describe small scotomas in visual field examinations several statistical models are proposed and applied to the evaluation of angioscotoma in 13 ophthalmologically normal subjects. A special perimetric grid is used where thresholds can be estimated along a line of narrow-spaced test points which crosses the predicted location of the retinal vessel. A two-stage analysis employs single estimations to fit a threshold curve by means of a special parametric description of the luminance difference sensitivity threshold as a function of stimulus position. An alternative model incorporates the threshold as a function of position into the probabilistic description of the binary response (stimulus seen/not seen).

OBJECTIVE To examine endothelial function (EF) noninvasively in IDDM and NIDDM patients with long diabetes duration. RESEARCH DESIGN AND METHODS We studied EF in 17 IDDM patients without diabetic complications and in 25 NIDDM patients with comparable glycemic control and with diabetic complications and compared both with nondiabetic control subjects matched for age, sex, and lumen diameter. Using high-resolution ultrasound, we measured the endothelialdependent (FAD%) and independent vasodilation (GTN%); the blood flow at rest, postocclusive, and after application of 400 μg glyceroltrinitrate of the brachial artery; and the intima media thickness (IMT) of the common carotid artery. RESULTS In the IDDM patients, neither FAD% (8.2 ± 4.6 vs. 7.6 ± 4.2%), GTN% (16.3 ± 4.9 vs. 18.4 ± 6.4%), nor postocclusive blood flow (40.6 ± 19.1 vs. 39.3 ± 23.6 cm/s) differed from the control subjects. IMT (0.59 ± 0.10 vs. 0.55 ± 0.14 mm) was slightly, but not significantly, elevated. In contrast, the NIDDM patients showed an impaired FAD% (3.8 ± 3.3 vs. 6.9 ± 4.4%, P < 0.01), no difference in GTN%, and a decreased postocclusive blood flow (18.5 ± 13.8 vs. 32.7 ± 20.0 cm/s, P < 0.01). IMT was significantly increased in NIDDM patients (0.77 ± 0.14 vs. 0.62 ± 0.10 mm, P < 0.001). CONCLUSIONS In contrast to NIDDM patients with cardiovascular complications, IDDM patients with long diabetes duration and good long-term metabolic control do not have impaired EF compared with control subjects.

In a dose-finding study with an active control, several doses of a new drug are compared with an established drug (the so-called active control). One goal of such studies is to characterize the dose–response relationship and to find the smallest target dose concentration d*, which leads to the same efficacy as the active control. For this purpose, the intersection point of the mean dose–response function with the expected efficacy of the active control has to be estimated. The focus of this paper is a cubic spline-based method for deriving an estimator of the target dose without assuming a specific dose–response function. Furthermore, the construction of a spline-based bootstrap CI is described. Estimator and CI are compared with other flexible and parametric methods such as linear spline interpolation as well as maximum likelihood regression in simulation studies motivated by a real clinical trial. Also, design considerations for the cubic spline approach with focus on bias minimization are presented. Although the spline-based point estimator can be biased, designs can be chosen to minimize and reasonably limit the maximum absolute bias. Furthermore, the coverage probability of the cubic spline approach is satisfactory, especially for bias minimal designs. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.