Composite endpoint to evaluate complement inhibition therapy in patients with paroxysmal nocturnal hemoglobinuria.

This study developed and explored a novel composite endpoint to assess the overall impact that treatment can have on patients living with paroxysmal nocturnal hemoglobinuria (PNH). Candidate composite endpoint variables were selected by a group of experts and included: lactate dehydrogenase levels as a measure of intravascular hemolysis; complete terminal complement inhibition; absence of major adverse vascular events, including thrombosis; absence of any adverse events leading to death or discontinuation of study treatment; transfusion avoidance; and improvements in fatigue-related quality of life as determined by the Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue score. From these variables, a novel composite endpoint was constructed and explored using data collected in the ravulizumab PNH Study 301 (NCT02946463). Thresholds were defined and reported for each candidate variable. Five of the six candidate variables were included in the final composite endpoint; the FACIT-Fatigue score was excluded. Composite endpoint criterion was defined as patients meeting all five selected individual component thresholds. All patients in the ravulizumab arm achieved complete terminal complement inhibition and a reduction in lactate dehydrogenase levels; 51.2% and 41.3% of patients in the ravulizumab arm and eculizumab arm, respectively, achieved all composite endpoint component thresholds (treatment difference: 9.4%; 95% confidence interval: -3.0, 21.5). The composite endpoint provided a single and simultaneous measurement of overall benefit for patients receiving treatment for PNH. Use of the composite endpoint in future PNH research is recommended to determine clinical benefit, and its use in health technology assessments should be evaluated.

Smoothing Corrections for Improving Sample Size Recalculation Rules in Adaptive Group Sequential Study Designs.

BACKGROUND: An adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation. OBJECTIVES: Sample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation. METHODS: Sample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a "jump" from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components. RESULTS: We demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes. CONCLUSION: Based on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.

A new conditional performance score for the evaluation of adaptive group sequential designs with sample size recalculation.

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

Efficacy of intraoperative specimen radiography as margin assessment tool in breast conserving surgery.

PURPOSE: To explore the ability of intraoperative specimen radiography (SR) to correctly identify positive margins in patients receiving breast conserving surgery (BCS). To assess whether the reoperation rate can be reduced by using this method. METHODS: This retrospective study included 470 consecutive cases receiving BCS due to a primarily diagnosed breast cancer. SR was carried out in two planes, assessing the specimen regarding the presence of the lesion and its relation to all margins. If indicated, re-excision of selective orientations was advised. Under consideration of gross inspection and the SR-findings, it was up to the surgeon whether to perform re-resections. The recommendations for re-excision were, separately for each orientation, compared to the histopathological results, serving as gold standard. RESULTS: Intraoperative SR was performed in 470 cases, thus 2820 margins were assessed. Of those, 2510 (89.0%) were negative and 310 (11.0%) positive. SR identified 2179 (77.3%) margins correctly as negative, whereas 331 (11.7%) clear margins were misjudged as positive. Of 310 infiltrated margins, SR identified 114 (4.0%) correctly, whereas 196 (7.0%) infiltrated margins were missed. This resulted in a sensitivity/specificity of 36.8%/86.8% and PPV/NPV of 25.6%/91.8%. Through targeted re-resections positive margins could be reduced by 31.0% [310 to 214 (7.6%)]. On case level, the rate of secondary procedures could be reduced by 37.0% [from 162 to 102 (21.7%)]. CONCLUSIONS: SR is a helpful tool to identify infiltrated margins and to reduce the rate of secondary surgeries by recommending targeted re-excisions of according orientations in order to obtain a final negative margin status.

Introducing a new estimator and test for the weighted all-cause hazard ratio.

BACKGROUND: The rationale for the use of composite time-to-event endpoints is to increase the number of expected events and thereby the power by combining several event types of clinical interest. The all-cause hazard ratio is the standard effect measure for composite endpoints where the all-cause hazard function is given as the sum of the event-specific hazards. However, the effect of the individual components might differ, in magnitude or even in direction, which leads to interpretation difficulties. Moreover, the individual event types often are of different clinical relevance which further complicates interpretation. Our working group recently proposed a new weighted effect measure for composite endpoints called the 'weighted all-cause hazard ratio'. By imposing relevance weights for the components, the interpretation of the composite effect becomes more 'natural'. Although the weighted all-cause hazard ratio seems an elegant solution to overcome interpretation problems, the originally published approach has several shortcomings: First, the proposed point estimator requires pre-specification of a parametric survival model. Second, no closed formula for a corresponding test statistic was provided. Instead, a permutation test was proposed. Third, no clear guidance for the choice of the relevance weights was provided. In this work, we will overcome these problems. METHODS: Within this work a new non-parametric estimator and a related closed formula test statistic are presented. Performance of the new estimator and test is compared to the original ones by a Monte-Carlo simulation study. RESULTS: The original parametric estimator is sensible to miss-specifications of the survival model. The new non-parametric estimator turns out to be very robust even if the required assumptions are not met. The new test shows considerably better power properties than the permutation test, is computationally much less expensive but might not preserve type one error in all situations. A scheme for choosing the relevance weights in the planning stage is provided. CONCLUSION: We recommend to use the non-parametric estimator along with the new test to assess the weighted all-cause hazard ratio. Concrete guidance for the choice of the relevance weights is now available. Thus, applying the weighted all-cause hazard ratio in clinical applications is both - feasible and recommended.

[Current challenges in the assessment of ethical proposals-aspects of digitalization and personalization in the healthcare system]. / Aktuelle Herausforderungen bei der Bewertung von Ethikanträgen ­ Aspekte der Digitalisierung und Personalisierung im Gesundheitswesen.

The global aim of medical ethics committees is to judge the scientific quality and the integrity of the content of medical research projects (studies), thereby assessing the benefit-risk profile. Apart from judging content-related aspects and the legal correctness, the study design and the analysis strategy must also be assessed from a biostatistical point of view. This very sophisticated task is further complicated by the fact that medical research constantly faces new challenges.Within this work, current developments in medical research that directly impact the assessability of ethical proposals will be identified and discussed. The aim is to sensitize researchers to the opportunities and challenges of new developments.The work focusses on the topics of digitalization in the healthcare system and individualized medicine. The authors illustrate some problems resulting from these developments that affect the ethical justification of medical research projects. Problems related to medical as well as biostatistical aspects are presented and their direct implications on the legal justification and ethical and moral conceptual integrity are highlighted.New developments in medical research such as digitalization and individualized medicine offer new perspectives for optimized therapies. These promising developments must be further advanced. A critical view on the so far only poorly investigated consequences of embedding new data sources and study designs must urgently accompany this process. Transparency and clarity in formulating ethical proposals is thereby of utmost importance.

A weighted combined effect measure for the analysis of a composite time-to-first-event endpoint with components of different clinical relevance.

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

Choice of futility boundaries for group sequential designs with two endpoints.

BACKGROUND: In clinical trials, the opportunity for an early stop during an interim analysis (either for efficacy or for futility) may relevantly save time and financial resources. This is especially important, if the planning assumptions required for power calculation are based on a low level of evidence. For example, when including two primary endpoints in the confirmatory analysis, the power of the trial depends on the effects of both endpoints and on their correlation. Assessing the feasibility of such a trial is therefore difficult, as the number of parameter assumptions to be correctly specified is large. For this reason, so-called 'group sequential designs' are of particular importance in this setting. Whereas the choice of adequate boundaries to stop a trial early for efficacy has been broadly discussed in the literature, the choice of optimal futility boundaries has not been investigated so far, although this may have serious consequences with respect to performance characteristics. METHODS: In this work, we propose a general method to construct 'optimal' futility boundaries according to predefined criteria. Further, we present three different group sequential designs for two endpoints applying these futility boundaries. Our methods are illustrated by a real clinical trial example and by Monte-Carlo simulations. RESULTS: By construction, the provided method of choosing futility boundaries maximizes the probability to correctly stop in case of small or opposite effects while limiting the power loss and the probability of stopping the study 'wrongly'. Our results clearly demonstrate the benefit of using such 'optimal' futility boundaries, especially compared to futility boundaries commonly applied in practice. CONCLUSIONS: As the properties of futility boundaries are often not considered in practice and unfavorably chosen futility boundaries may imply bad properties of the study design, we recommend assessing the performance of these boundaries according to the criteria proposed in here.

A mother-child intervention program in adolescent mothers and their children to improve maternal sensitivity, child responsiveness and child development (the TeeMo study): study protocol for a randomized controlled trial.

BACKGROUND: Children of adolescent mothers present a high-risk group for child neglect and maltreatment. Previous findings suggest that early interventions can reduce maltreatment by improving the quality of mother-child interaction, particularly maternal sensitivity. The aim of the current study is to evaluate the effects of a mother-child intervention program using home visits and video-feedback regarding mother-child interaction (STEEP-b) plus psychiatric treatment of the mother in cases where mental illness is present compared with TAU (treatment as usual, that is, standardized support by the child welfare system) on enhancing maternal sensitivity and child responsiveness in adolescent, high-risk mothers. The second aim of the current project is to investigate behavioral and neural differences between adolescent and adult mothers at baseline and postintervention. METHODS/DESIGN: This is a randomized controlled trial (RCT) with 120 high-risk adolescent mothers (<21 years old) and their 3- to 6-month-old children. Half of the participants will be randomized to receive STEEP-b in addition to their standard treatment for up to 12 to 18 sessions over 9 months. The other half will continue with treatment as usual. For further comparisons, 40 adult mothers with positive and negative rearing experiences (>25 years) will additionally be recruited to investigate behavioral and neural differences between the adolescent and adult group. Blind assessments will take place at T1 (pre-intervention), at the end of the 9-month intervention (T2, postintervention) and 6 months postintervention (T3, follow-up). Moderators of treatment outcomes and sociodemographic data will be assessed at T1. The primary outcome hypothesis is that STEEP-b added to treatment as usual will improve maternal sensitivity and child responsiveness compared with treatment as usual alone in high-risk adolescent mothers. The primary hypothesis will be evaluated at the end of the 9-month follow-up assessment based on the intention-to-treat principle. The trial is funded by the German Ministry for Research and Education (BMBF). Data collection started in October 2012. DISCUSSION: This is a randomized controlled trial that evaluates the effects of an early intervention program (STEEP-b) on the quality of mother-child interaction and child development in adolescent, high-risk mothers. TRIAL REGISTRATION: DRKS00004409 (27 September 2012).

Evaluation of virtual touch tissue imaging quantification, a new shear wave velocity imaging method, for breast lesion assessment by ultrasound.

OBJECTIVES: To evaluate virtual touch tissue imaging quantification (VTIQ) as a new elastography method concerning its intra- and interexaminer reliability and its ability to differentiate benign from malignant breast lesions in comparison to and in combination with ultrasound (US) B-mode breast imaging reporting and data system (BI-RADS) assessment. MATERIALS AND METHODS: US and VTIQ were performed by two examiners in 103 women with 104 lesions. Intra- and interexaminer reliability of VTIQ was assessed. The area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of BIRADS, VTIQ, and combined data were compared. RESULTS: Fifty-four of 104 lesions were malignant. Intraexaminer reliability was consistent, and interexaminer agreement showed a strong positive correlation (r = 0.93). The mean VTIQ values in malignant lesions were significantly higher than those in benign (7.73 m/s ± 1.02 versus 4.46 m/s ± 1.87; P < 0.0001). The combination of US-BIRADS with the optimal cut-off for clinical decision making of 5.18 m/s yielded a sensitivity of 98%, specificity of 82%, PPV of 86%, and NPV of 98%. The combination of BIRADS and VTIQ led to improved test validity. CONCLUSION: VTIQ is highly reliable and reproducible. There is a significant difference regarding the mean maximum velocity of benign and malignant lesions. Adding VTIQ to BIRADS assessment improves the specificity.

Objective assessment of aesthetic outcome after breast conserving therapy: subjective third party panel rating and objective BCCT.core software evaluation.

We analysed intra- and inter-rater agreement of subjective third party assessment and agreement with a semi-automated objective software evaluation tool (BCCT.core). We presented standardized photographs of 50 patients, taken shortly and one year after surgery to a panel of five breast surgeons, six breast nurses, seven members of a breast cancer support group, five medical and seven non-medical students. In two turns they rated aesthetic outcome on a four point scale. Moreover the same photographs were evaluated by the BCCT.core software. Intra-rater agreement in the panel members was moderate to substantial (k = 0.4-0.5; wk = 0.6-0.7; according to different subgroups and times of assessment). In contrast inter-rater agreement was only slight to fair (mk = 0.1-0.3). Agreement between the panel participants and the software was fair (wk = 0.24-0.45). Subjective third party assessment only fairly agree with objective BCCT.core evaluation just as third party participants do not agree well among each other.

A new outlier identification test for method comparison studies based on robust regression.

The identification of outliers in method comparison studies (MCS) is an important part of data analysis, as outliers can indicate serious errors in the measurement process. Common outlier tests proposed in the literature usually require a homogeneous sample distribution and homoscedastic random error variances. However, datasets in MCS usually do not meet these assumptions. In this work, a new outlier test based on robust linear regression is proposed to overcome these special problems. The LORELIA (local reliability) residual test is based on a local, robust residual variance estimator, given as a weighted sum of the observed residuals. The new test is compared to a standard test proposed in the literature by a Monte Carlo simulation. Its performance is illustrated in examples.