Applying exercise capacity and physical activity as single vs. composite endpoints for trials of cardiac rehabilitation interventions: Rationale, use-case, and a blueprint method for sample size calculation.

OBJECTIVE: To conceptualise a composite primary endpoint for parallel-group RCTs of exercise-based cardiac rehabilitation interventions, and to explore its application and statistical efficiency. DESIGN: We conducted a statistical exploration of sample size requirements. We combined exercise capacity and physical activity for the composite endpoint, both being directly related to reduced premature mortality in cardiac patients. Based on smallest detectable and minimal clinically important changes (change in exercise capacity of 15W and change in physical activity of 10 min/day), the composite endpoint combines two dichotomous endpoints (achieved/not achieved). To examine statistical efficiency, we compared sample size requirements based on the composite endpoint to single endpoints using data from two completed cardiac rehabilitation trials. SETTING: Cardiac rehabilitation phase III PARTICIPANTS: Cardiac rehabilitation patients INTERVENTIONS: Not applicable MAIN OUTCOME MEASURE(S): Exercise capacity (Pmax assessed by incremental cycle ergometry) and physical activity (daily minutes of moderate to vigorous physical activity assessed by accelerometry) RESULTS: Expecting, e.g., a 10% between-group difference and improvement in the clinical outcome, the composite endpoint would require a sample size increase by up to 21% or 61%, depending on the dataset. When expecting a 10% difference and designing an intervention with the aim of non-deterioration, the composite endpoint would allow to reduce the sample size by up to 55% or 70%. CONCLUSIONS: Trialists may consider the utility of the composite endpoint for future studies in exercise-based cardiac rehabilitation, which could reduce sample size requirements. However, perhaps surprisingly at first, the composite endpoint could also lead to an increased sample size needed, depending on the observed baseline proportions in the trial population and the aim of the intervention.

A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases.

Ordinal data in a repeated measures design of a crossover study for rare diseases usually do not allow for the use of standard parametric methods, and hence, nonparametric methods should be considered instead. However, only limited simulation studies in settings with small sample sizes exist. Therefore, starting from an Epidermolysis Bullosa simplex trial with the above-mentioned design, a rank-based approach using the R package nparLD and different generalized pairwise comparisons (GPC) methods were compared impartially in a simulation study. The results revealed that there was not one single best method for this particular design, because a trade-off exists between achieving high power, accounting for period effects, and for missing data. Specifically, nparLD as well as the unmatched GPC approaches do not address crossover aspects, and the univariate GPC variants partly ignore the longitudinal information. The matched GPC approaches, on the other hand, take the crossover effect into account in the sense of incorporating the within-subject association. Overall, the prioritized unmatched GPC method achieved the highest power in the simulation scenarios, although this may be due to the specified prioritization. The rank-based approach yielded good power even at a sample size of N = 6 $N=6$ , whereas the matched GPC method could not control the type I error.

Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials.

BACKGROUND: Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians. RESULTS: It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered. CONCLUSION: Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.

The need for robust critique of arts and health research: Dance-movement therapy, girls, and depression.

We examine a highly cited randomized controlled trial on dance-movement therapy with adolescent girls with mild depression and examine its treatment in 14 evidence reviews and meta-analyses of dance research. We demonstrate substantial limitations in the trial which seriously undermine the conclusions reached regarding the effectiveness of dance movement therapy in reducing depression. We also show that the dance research reviews vary substantially in their treatment of the study. Some reviews provide a positive assessment of the study and take its findings at face value without critical commentary. Others are critical of the study, identifying significant limitations, but showing marked differences in Cochrane Risk of Bias assessments. Drawing on recent criticisms of systematic reviewing and meta-analysis, we consider how reviews can be so variable and discuss what is needed to improve the quality of primary studies, systematic reviews, and meta-analyses in the field of creative arts and health.

Third dose of SARS-CoV-2 vaccination in hemato-oncological patients and health care workers: immune responses and adverse events - a retrospective cohort study.

BACKGROUND: Due to potentially immune-escaping virus variants and waning immunity, a third SARS-CoV-2 vaccination dose is increasingly recommended. However, data in patients with cancer are limited. PATIENTS AND METHODS: We measured anti-SARS-CoV-2 spike protein antibody levels after the third vaccination dose in 439 patients with cancer and 41 health care workers (HCW) at an academic centre in Austria and a rural community hospital in Italy. Adverse events were retrieved from questionnaires. RESULTS: Overall, 439 patients and 41 HCW were included. SARS-CoV-2 infections were observed in 62/439 (14.1%) patients before vaccination and in 5/439 (1.1%) patients after ≥1 dose. Longitudinal analysis revealed a decrease of antibody levels between 3 and 6 months after second vaccination in patients with solid tumours (p < 0.001) and haematological malignancies without anti-B cell therapies (p < 0.001). After the third dose, anti-S levels increased compared to the first/second dose. Patients receiving B cell-targeted agents had lower antibody levels than patients with haematological malignancies undergoing other treatments (p < 0.001) or patients with solid tumours (p < 0.001). Moreover, anti-S levels correlated with CD19+ (B cell) and CD56+ (NK cell) counts in peripheral blood. The most frequent adverse events after the third dose were local pain (75/160, 46.9%), fatigue (25/160, 15.6%) and fever/chills (16/160, 10.0%). Patients with cancer had lower anti-S levels than HCW (p = 0.015). CONCLUSIONS: This study in patients with cancer shows improved antibody levels after the third vaccination dose at an acceptable side-effect profile. Lower antibody levels than in controls underline the need for further follow-up studies and dedicated trials.

Testing for equality of distributions using the concept of (niche) overlap.

In this paper, we propose a new non-parametric test for equality of distributions. The test is based on the recently introduced measure of (niche) overlap and its rank-based estimator. As the estimator makes only one basic assumption on the underlying distribution, namely continuity, the test is universal applicable in contrast to many tests that are restricted to only specific scenarios. By construction, the new test is capable of detecting differences in location and scale. It thus complements the large class of rank-based tests that are constructed based on the non-parametric relative effect. In simulations this new test procedure obtained higher power and lower type I error compared to two common tests in several settings. The new procedure shows overall good performance. Together with its simplicity, this test can be used broadly. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00362-021-01239-y.

Macroclimatic conditions as main drivers for symbiotic association patterns in lecideoid lichens along the Transantarctic Mountains, Ross Sea region, Antarctica.

Lecideoid lichens as dominant vegetation-forming organisms in the climatically harsh areas of the southern part of continental Antarctica show clear preferences in relation to environmental conditions (i.e. macroclimate). 306 lichen samples were included in the study, collected along the Ross Sea coast (78°S-85.5°S) at six climatically different sites. The species compositions as well as the associations of their two dominant symbiotic partners (myco- and photobiont) were set in context with environmental conditions along the latitudinal gradient. Diversity values were nonlinear with respect to latitude, with the highest alpha diversity in the milder areas of the McMurdo Dry Valleys (78°S) and the most southern areas (Durham Point, 85.5°S; Garden Spur, 84.5°S), and lowest in the especially arid and cold Darwin Area (~ 79.8°S). Furthermore, the specificity of mycobiont species towards their photobionts decreased under more severe climate conditions. The generalist lichen species Lecanora fuscobrunnea and Lecidea cancriformis were present in almost all habitats, but were dominant in climatically extreme areas. Carbonea vorticosa, Lecidella greenii and Rhizoplaca macleanii were confined to milder areas. In summary, the macroclimate is considered to be the main driver of species distribution, making certain species useful as bioindicators of climate conditions and, consequently, for assessing the consequences of climate change.

SARS-CoV-2 screening in cancer outpatients during the second wave of the COVID-19 pandemic : Conclusions for crisis response at a high-volume oncology center.

BACKGROUND: During the second wave of the coronavirus disease 2019 (COVID-19) pandemic Austria suffered one of the highest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rates worldwide. We report performance parameters of a SARS-CoV­2 screening program established for cancer outpatients at our center. METHODS: Institutional policy recommended routine biweekly SARS-CoV­2 testing. Adherence to the testing recommendation during the second wave of the COVID-19 pandemic between 1 October and 30 November 2020 was analyzed. The SARS-CoV­2 infection rate during first wave period (21 March to 4 May 2020) was compared to the one during second wave. RESULTS: A total of 1577 cancer patients were seen at our outpatient clinic during the second wave. In 1079/1577 (68.4%) patients, at least 1 SARS-CoV2 test was performed. Overall 2833 tests were performed, 23/1577 (1.5%, 95% confidence interval, CI 1.0-2.2%) patients were tested positive for SARS-CoV­2, which indicates a significant increase compared to the first wave (4/1016; 0.4%, 95% CI 0.1-1.0%) with an odds ratio of 3.9 (95% CI 1.5-10.1; p < 0.005). Patients undergoing active anticancer treatment (172/960; 17.9% not tested) were more likely to have undergone a SARS-CoV­2 test than patients in follow-up or best supportive care (326/617; 52.8% not tested p < 0.001). Furthermore, patients with only 1 visit within 4 weeks were more likely to not have undergone a SARS-CoV­2 test (386/598; 64.5%) compared to patients with 2 or more visits (112/979; 11.4%; p < 0.001). The projected number of patients with undetected SARS-CoV­2 infection during the study period was 5. CONCLUSION: We identified clinical patient parameters influencing SARS-CoV­2 testing coverage in cancer outpatients. Our data can provide information on generation of standard operating procedures and resource allocation during subsequent infection waves.

Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe.

Background: High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years. Not only has their occurrence been linked to epileptogenesis, but also to physiologic processes, such as memory consolidation. There are at least two big challenges for HFO research. First, detection, when performed manually, is time consuming and prone to rater biases, but when performed automatically, it is biased by artifacts mimicking HFOs. Second, distinguishing physiologic from pathologic HFOs in patients with epilepsy is problematic. Here we automatically and manually detected HFOs in intracranial EEGs (iEEG) of patients with epilepsy, recorded during a visual memory task in order to assess the feasibility of the different detection approaches to identify task-related ripples, supporting the physiologic nature of HFOs in the temporal lobe. Methods: Ten patients with unclear seizure origin and bilaterally implanted macroelectrodes took part in a visual memory consolidation task. In addition to iEEG, scalp EEG, electrooculography (EOG), and facial electromyography (EMG) were recorded. iEEG channels contralateral to the suspected epileptogenic zone were inspected visually for HFOs. Furthermore, HFOs were marked automatically using an RMS detector and a Stockwell classifier. We compared the two detection approaches and assessed a possible link between task performance and HFO occurrence during encoding and retrieval trials. Results: HFO occurrence rates were significantly lower when events were marked manually. The automatic detection algorithm was greatly biased by filter-artifacts. Surprisingly, EOG artifacts as seen on scalp electrodes appeared to be linked to many HFOs in the iEEG. Occurrence rates could not be associated to memory performance, and we were not able to detect strictly defined "clear" ripples. Conclusion: Filtered graphoelements in the EEG are known to mimic HFOs and thus constitute a problem. So far, in invasive EEG recordings mostly technical artifacts and filtered epileptiform discharges have been considered as sources for these "false" HFOs. The data at hand suggests that even ocular artifacts might bias automatic detection in invasive recordings. Strict guidelines and standards for HFO detection are necessary in order to identify artifact-derived HFOs, especially in conditions when cognitive tasks might produce a high amount of artifacts.

SARS-CoV-2 Testing in Patients With Cancer Treated at a Tertiary Care Hospital During the COVID-19 Pandemic.

PURPOSE: To analyze the prevalence of SARS-CoV-2 infection in patients with cancer in hospital care after implementation of institutional and governmental safety measurements. METHODS: Patients with cancer routinely tested for SARS-CoV-2 RNA by nasal swab and real-time polymerase chain reaction between March 21 and May 4, 2020, were included. The results of this cancer cohort were statistically compared with the SARS-CoV-2 prevalence in the Austrian population as determined by a representative nationwide random sample study (control cohort 1) and a cohort of patients without cancer presenting to our hospital (control cohort 2). RESULTS: A total of 1,688 SARS-CoV-2 tests in 1,016 consecutive patients with cancer were performed. A total of 270 of 1,016 (26.6%) of the patients were undergoing active anticancer treatment in a neoadjuvant/adjuvant and 560 of 1,016 (55.1%) in a palliative setting. A total of 53 of 1,016 (5.2%) patients self-reported symptoms potentially associated with COVID-19. In 4 of 1,016 (0.4%) patients, SARS-CoV-2 was detected. At the time of testing at our department, all four SARS-CoV-2-positive patients were asymptomatic, and two of them had recovered from symptomatic COVID-19. Viral clearance was achieved in three of the four patients 14-56 days after testing positive. The estimated odds ratio of SARS-CoV-2 prevalence between the cancer cohort and control cohort 1 was 1.013 (95% CI, 0.209 to 4.272; P = 1), and between control cohort 2 and the cancer cohort it was 18.333 (95% CI, 6.056 to 74.157). CONCLUSION: Our data indicate that continuation of active anticancer therapy and follow-up visits in a large tertiary care hospital are feasible and safe after implementation of strict population-wide and institutional safety measures during the current COVID-19 pandemic. Routine SARS-CoV-2 testing of patients with cancer seems advisable to detect asymptomatic virus carriers and avoid uncontrolled viral spread.

Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology.

Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N = 9), mild cognitive impairment (N = 19), and subjective cognitive complaints (N = 4) and healthy controls (N = 18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline.

Sample size calculation and blinded recalculation for analysis of covariance models with multiple random covariates.

When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the analysis of covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example.

Multivariate permutation tests for two sample testing in presence of nondetects with application to microarray data.

Very often, data collected in medical research are characterized by censored observations and/or data with mass on the value zero. This happens for example when some measurements fall below the detection limits of the specific instrument used. This type of left censored observations is called "nondetects". Such a situation of an excessive number of zeros in a data set is also referred to as zero-inflated data. In the present work, we aim at comparing different multivariate permutation procedures in two-sample testing for data with nondetects. The effect of censoring is investigated with regard to the different values that may be attributed to nondetected values, both under the null hypothesis and under alternative. We motivate the problem using data from allergy research.

Sample sizes and statistical methods in interventional studies on individuals with spinal cord injury: A systematic review.

AIM: Prevalence and incidence of spinal cord injury (SCI) are low. However, sample sizes have not been systematically examined yet, although this might represent useful information for study planning and power considerations. Therefore, our objective was to determine the median sample size in clinical trials on SCI individuals. Moreover, within small-sample size studies, statistical methods and awareness of potential problems regarding small samples were examined. METHODS: We systematically reviewed all studies on human SCI individuals published between 2014 and 2015, where the effect of an intervention on one or more health-related outcomes was assessed by means of a hypothesis test. If at least one group had a size <20, the study was classified as a small sample size study. PubMed was searched for eligible studies; subsequently, data on sample sizes and statistical methods were extracted and summarized descriptively. RESULTS: Out of 8897 studies 207 were included. Median total sample size was 18 (range 4-582). Small sample sizes were found in 167/207 (81%) studies, resulting limitations and implications for statistical analyses were mentioned in 109/167 (65%) studies. CONCLUSIONS: Although most recent SCI trials have been conducted with small samples, the consequences on statistical analysis methods and the validity of the results are rarely acknowledged.

Small-sample performance and underlying assumptions of a bootstrap-based inference method for a general analysis of covariance model with possibly heteroskedastic and nonnormal errors.

It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced analysis of covariance models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present paper, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general analysis of covariance model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap analysis of covariance. The results of our simulation study indicate that our proposed test remedies the problems of the analysis of covariance F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial).

Optimal sample size planning for the Wilcoxon-Mann-Whitney test.

There are many different proposed procedures for sample size planning for the Wilcoxon-Mann-Whitney test at given type-I and type-II error rates α and ß, respectively. Most methods assume very specific models or types of data to simplify calculations (eg, ordered categorical or metric data, location shift alternatives, etc). We present a unified approach that covers metric data with and without ties, count data, ordered categorical data, and even dichotomous data. For that, we calculate the unknown theoretical quantities such as the variances under the null and relevant alternative hypothesis by considering the following "synthetic data" approach. We evaluate data whose empirical distribution functions match the theoretical distribution functions involved in the computations of the unknown theoretical quantities. Then, well-known relations for the ranks of the data are used for the calculations. In addition to computing the necessary sample size N for a fixed allocation proportion t = n1 /N, where n1 is the sample size in the first group and N = n1 + n2 is the total sample size, we provide an interval for the optimal allocation rate t, which minimizes the total sample size N. It turns out that, for certain distributions, a balanced design is optimal. We give a characterization of such distributions. Furthermore, we show that the optimal choice of t depends on the ratio of the two variances, which determine the variance of the Wilcoxon-Mann-Whitney statistic under the alternative. This is different from an optimal sample size allocation in case of the normal distribution model.

HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury.

Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10-20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.

A Fast and Robust Way to Estimate Overlap of Niches, and Draw Inference.

The problem of quantifying the overlap of Hutchinsonian niches has received much attention lately, in particular in quantitative ecology, from where it also originates. However, the niche concept has the potential to also be useful in many other application areas, as for example in economics. We are presenting a fully nonparametric, robust solution to this problem, along with exact shortcut formulas based on rank-statistics, and with a rather intuitive probabilistic interpretation. Furthermore, by deriving the asymptotic sampling distribution of the estimators, we are proposing the first asymptotically valid inference method, providing confidence intervals for the niche overlap. The theoretical considerations are supplemented by simulation studies and a real data example.

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions.

To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved.