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 conceptualize a composite primary endpoint for parallel-group RCTs of exercise-based cardiac rehabilitation (CR) 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 (CE), both being directly related to reduced premature mortality in patients with cardiac diseases. Based on smallest detectable and minimal clinically important changes (change in exercise capacity of 15 W and change in physical activity of 10 min/day), the CE combines 2 dichotomous endpoints (achieved/not achieved). To examine statistical efficiency, we compared sample size requirements based on the CE to single endpoints using data from 2 completed CR trials. SETTING: Cardiac rehabilitation phase III. PARTICIPANTS: Patients in cardiac rehabilitation. 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, for example, a 10% between-group difference and improvement in the clinical outcome, the CE would increase sample size 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 CE would allow to reduce the sample size by up to 55% or 70%. CONCLUSIONS: Trialists may consider the utility of the CE for future studies in exercise-based CR to reduce sample size requirements. However, perhaps surprisingly at first, the CE 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.

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.

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.

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.

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.

Influence of Intrinsic and Lifestyle Factors on the Development of IgE Sensitization.

BACKGROUND: IgE sensitization is a prerequisite for the development of allergic symptoms. The investigation of factors influencing the development of IgE is therefore crucial for understanding the onset of allergic diseases. METHODS: This epidemiological study investigated personal, intrinsic, and lifestyle factors in a nonselected cohort of 501 Austrian adolescents (aged 12-21 years). IgE levels to 112 allergen molecules were analyzed in the serum of participants using the ImmunoCAP ISAC®. Allergic sensitization, IgE levels to single allergens, and ISAC score sums were correlated with results obtained from a questionnaire. RESULTS: In this adolescent cohort, male participants showed a higher sensitization frequency (56.8%) compared to females (50.9%) and significantly increased IgE levels to profilins. Underweight subjects demonstrated a stronger IgE sensitization. Family size inversely correlated with IgE levels to PR-10 allergens, and predominately paternal allergies were a predictive factor for IgE sensitization in the children. Vaccination, breastfeeding, and delivery mode showed no influence, while a highly protective effect was observed for growing up on a farm. Of all of the investigated lifestyle factors, only smoking significantly influenced the risk for IgE development. Participants with moderate frequencies of colds showed increased sensitization levels. CONCLUSION: A hereditary predisposition and lifestyle factors such as a farming environment, smoking, family size, body weight, or frequency of colds significantly influenced the development of allergen-specific IgE in this cohort of adolescents.

Potential years lost and life expectancy in adults with newly diagnosed epilepsy.

OBJECTIVE: Studies using relative measures, such as standardized mortality ratios, have shown that patients with epilepsy have an increased mortality. Reports on more direct and absolute measure such as life expectancy are sparse. We report potential years lost and how life expectancy has changed over 40 years in a cohort of patients with newly diagnosed epilepsy. METHODS: We analyzed life expectancy in a cohort of adult patients diagnosed with definite epilepsy between 1970 and 2010. Those with brain tumor as cause of epilepsy were excluded. By retrospective probabilistic record linkage, living or death status was derived from the national death registry. We estimated life expectancy by a Weibull regression model using gender, age at diagnosis, epilepsy etiology, and year of diagnosis as covariates at time of epilepsy diagnosis, and 5, 10, 15, and 20 years after diagnosis. Results were compared to the general population, and 95% confidence intervals are given. RESULTS: There were 249 deaths (105 women, age at death 19.0-104.0 years) in 1,112 patients (11,978.4 person-years, 474 women, 638 men). A substantial decrease in life expectancy was observed for only a few subgroups, strongly depending on epilepsy etiology and time of diagnosis: time of life lost was highest in patients with symptomatic epilepsy diagnosed between 1970 and 1980; the impact declined with increasing time from diagnosis. Over half of the analyzed subgroups did not differ significantly from the general population. This effect was reversed in the later decades, and life expectancy was prolonged in some subgroups, reaching a maximum in those with newly diagnosed idiopathic and cryptogenic epilepsy between 2001 and 2010. SIGNIFICANCE: Life expectancy is reduced in symptomatic epilepsies. However, in other subgroups, a prolonged life expectancy was found, which has not been reported previously. Reasons may be manifold and call for further study.

First application of a protein-based approach for time since death estimation.

Awareness of postmortem degradation processes in a human body is fundamental to develop methods for forensic time since death estimation (TDE). Currently, applied approaches are all more or less limited to certain postmortem phases, or have restrictions on behalf of circumstances of death. Novel techniques, however, rarely exceed basic research phases due to various reasons. We report the first application of a novel method, based on decay of muscle proteins, in a recent case of murder-suicide, where other TDE methods failed to obtain data. We detected considerably different protein degradation profiles in both individuals involved and compared the data to our presently available database. We obtained statistical evidence for un-simultaneous death and therefore received valuable information to trace the progression of events based on protein degradation. Although we could not sensibly convert the data to respective times of death, this case highlights the potential for future application and elucidates the necessary further steps to develop a viable TDE method.

Task-induced Changes in Idiopathic Infantile Nystagmus Vary with Gaze.

PURPOSE: Investigations of infantile nystagmus syndrome (INS) at center or at the null position have reported that INS worsens when visual demand is combined with internal states, e.g. stress. Visual function and INS parameters such as foveation time, frequency, amplitude, and intensity can also be influenced by gaze position. We hypothesized that increases from baseline in visual demand and mental load would affect INS parameters at the null position differently than at other gaze positions. METHODS: Eleven participants with idiopathic INS were asked to determine the direction of Tumbling-E targets, whose visual demand was varied through changes in size and contrast, using a staircase procedure. Targets appeared between ±25° in 5° steps. The task was repeated with both mental arithmetic and time restriction to impose higher mental load, confirmed through subjective ratings and concurrent physiological measurements. RESULTS: Within-subject comparisons were limited to the null and 15° away from it. No significant main effects of task on any INS parameters were found. At both locations, high mental load worsened task performance metrics, i.e. lowest contrast (P = .001) and smallest optotype size reached (P = .012). There was a significant interaction between mental load and gaze position for foveation time (P = .02) and for the smallest optotype reached (P = .028). The increase in threshold optotype size from the low to high mental load was greater at the null than away from it. During high visual demand, foveation time significantly decreased from baseline at the null as compared to away from it (mean difference ± SE: 14.19 ± 0.7 msec; P = .010). CONCLUSIONS: Under high visual demand, the effects of increased mental load on foveation time and visual task performance differed at the null as compared to 15° away from it. Assessment of these effects could be valuable when evaluating INS clinically and when considering its impact on patients' daily activities.

Inference for low- and high-dimensional multigroup repeated measures designs with unequal covariance matrices.

We propose tests for main and simple treatment effects, time effects, as well as treatment by time interactions in possibly high-dimensional multigroup repeated measures designs. The proposed inference procedures extend the work by Brunner et al. (2012) from two to several treatment groups and remain valid for unbalanced data and under unequal covariance matrices. In addition to showing consistency when sample size and dimension tend to infinity at the same rate, we provide finite sample approximations and evaluate their performance in a simulation study, demonstrating better maintenance of the nominal α-level than the popular Box-Greenhouse-Geisser and Huynh-Feldt methods, and a gain in power for informatively increasing dimension. Application is illustrated using electroencephalography (EEG) data from a neurological study involving patients with Alzheimer's disease and other cognitive impairments.

Subjective memory impairment and cholinergic transmission: a TMS study.

Subjective memory impairment (SMI) is being increasingly recognized as a preclinical phase of Alzheimer disease (AD). Short latency afferent inhibition (SAI) is helpful in demonstrating dysfunction of central cholinergic circuits, and was reported to be abnormal in patients with AD and amnestic multiple domain mild cognitive impairment. In this study, we found normal SAI in 20 subjects with SMI. SAI could be a useful biomarker for identifying, among individuals with memory complaints, those in whom cholinergic degeneration has occurred.

Reaction-time task reliability is more accurately computed with permutation-based split-half correlations than with Cronbach's alpha.

While it has become standard practice to report the reliability of self-report scales, it remains uncommon to do the same for experimental paradigms. To facilitate this practice, we review old and new ways to compute reliability in reaction-time tasks, and we compare their accuracy using a simulation study. Highly inaccurate and negatively biased reliability estimates are obtained through the common practice of averaging sets of trials and submitting them to Cronbach's alpha. Much more accurate reliability estimates are obtained using split-half reliability methods, especially by computing many random split-half correlations and aggregating them in a metric known as permutation-based split-half reliability. Through reanalysis of existing data and comparison of reliability values reported in the literature, we confirm that Cronbach's alpha also tends to be lower than split-half reliability in real data. We further establish a set of practices to maximize the accuracy of the permutation-based split-half reliability coefficient through simulations. We find that its accuracy is improved by ensuring each split-half dataset contains an approximately equal number of trials for each stimulus, by correcting the averaged correlation for test length using a modified variant of the Spearman-Brown formula, and by computing a sufficient number of split-half correlations: around 5,400 are needed to obtain a stable estimate for median-based double-difference scores computed from 30 participants and 256 trials. To conclude, we review the available software for computing this coefficient.

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.

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.

Estimation of Box's ε for low- and high-dimensional repeated measures designs with unequal covariance matrices.

We present new inference methods for the analysis of low- and high-dimensional repeated measures data from two-sample designs that may be unbalanced, the number of repeated measures per subject may be larger than the number of subjects, covariance matrices are not assumed to be spherical, and they can differ between the two samples. In comparison, we demonstrate how crucial it is for the popular Huynh-Feldt (HF) method to make the restrictive and often unrealistic or unjustifiable assumption of equal covariance matrices. The new method is shown to maintain desired α-levels better than the well-known HF correction, as demonstrated in several simulation studies. The proposed test gains power when the number of repeated measures is increased in a manner that is consistent with the alternative. Thus, even increasing the number of measurements on the same subject may lead to an increase in power. Application of the new method is illustrated in detail, using two different real data sets. In one of them, the number of repeated measures per subject is smaller than the sample size, while in the other one, it is larger.

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.

Gene set analysis for longitudinal gene expression data.

BACKGROUND: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios. In these longitudinal studies, gene expression is repeatedly measured over time such that a GSA needs to take into account the within-gene correlations in addition to possible between-gene correlations. RESULTS: We provide a robust nonparametric approach to compare the expressions of longitudinally measured sets of genes under multiple treatments or experimental conditions. The limiting distributions of our statistics are derived when the number of genes goes to infinity while the number of replications can be small. When the number of genes in a gene set is small, we recommend permutation tests based on our nonparametric test statistics to achieve reliable type I error and better power while incorporating unknown correlations between and within-genes. Simulation results demonstrate that the proposed method has a greater power than other methods for various data distributions and heteroscedastic correlation structures. This method was used for an IL-2 stimulation study and significantly altered gene sets were identified. CONCLUSIONS: The simulation study and the real data application showed that the proposed gene set analysis provides a promising tool for longitudinal microarray analysis. R scripts for simulating longitudinal data and calculating the nonparametric statistics are posted on the North Dakota INBRE website http://ndinbre.org/programs/bioinformatics.php. Raw microarray data is available in Gene Expression Omnibus (National Center for Biotechnology Information) with accession number GSE6085.

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.