Tag: statistics

Gesundheitswesen. 2021 Sep;83(8-09):e41-e48. doi: 10.1055/a-1531-5507. Epub 2021 Sep 8.

ABSTRACT

OBJECTIVE: Well-established mortality ratio methodology can contribute to a fuller picture of the SARS-CoV-2/COVID-19 burden of disease by revealing trends and informing mitigation strategies. This work examines respective data from Germany by way of example.

METHODS: Using monthly and weekly all-cause mortality data from January 2016 to June 2020 (published by the German Federal Statistical Institute) for all ages,<65 years and≥65 years, and specified for Germany’s federal states, we explored mortality as sequela of COVID-19. We analysed standardized mortality ratios (SMRs) comparing 2020 with 2016-2019 as reference years with a focus on trend detection.

RESULTS: In Germany as a whole, elevated mortality in April (most pronounced for Bavaria) declined in May. The states of Hamburg and Bremen had increased SMRs in all months under study. In Mecklenburg-Western Pomerania, decreased SMRs in January turned monotonically to increased SMRs by June. Irrespective of age group, this trend was pronounced and significant.

CONCLUSIONS: Increased SMRs in Hamburg and Bremen must be interpreted with caution because of potential upward distortions due to a “catchment bias”. A pronounced excess mortality in April across Germany was confirmed and a hitherto undetected trend of increasing SMRs for Mecklenburg-Western Pomerania was revealed. To meet the pandemic challenge and to benefit from research based on data collected in standardized ways, national authorities should regularly conduct SMR analyses. For independent analyses, national authorities should also expedite publishing raw mortality and population data, including detailed information on age, sex, and cause of death, in the public domain.

PMID:34496443 | DOI:10.1055/a-1531-5507

Neural Comput. 2021 May 13;33(6):1616-1655. doi: 10.1162/neco_a_01382.

ABSTRACT

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.

PMID:34496386 | DOI:10.1162/neco_a_01382

J Neurol Surg A Cent Eur Neurosurg. 2021 Sep 8. doi: 10.1055/s-0041-1730965. Online ahead of print.

ABSTRACT

OBJECTIVE: Treatment for newly diagnosed isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) includes maximum safe resection, followed by adjuvant radio(chemo)therapy (RCx) with temozolomide. There is evidence that it is safe for GBM patients to prolong time to irradiation over 4 weeks after surgery. This study aimed at evaluating whether this applies to GBM patients with different levels of residual tumor volume (RV).

METHODS: Medical records of all patients with newly diagnosed GBM at our department between 2014 and 2018 were reviewed. Patients who received adjuvant radio (chemo) therapy, aged older than 18 years, and with adequate perioperative imaging were included. Initial and residual tumor volumes were determined. Time to irradiation was dichotomized into two groups (≤28 and >28 days). Univariate analysis with Kaplan-Meier estimate and log-rank test was performed. Survival prediction and multivariate analysis were performed employing Cox proportional hazard regression.

RESULTS: One hundred and twelve patients were included. Adjuvant treatment regimen, extent of resection, residual tumor volume, and O⁶-methylguanine DNA methyltransferase (MGMT) promoter methylation were statistically significant factors for overall survival (OS). Time to irradiation had no impact on progression-free survival (p = 0.946) or OS (p = 0.757). When stratified for different thresholds of residual tumor volume, survival predication via Cox regression favored time to irradiation below 28 days for patients with residual tumor volume above 2 mL, but statistical significance was not reached.

CONCLUSION: Time to irradiation had no significant influence on OS of the entire cohort. Nevertheless, a statistically nonsignificant survival prolongation could be observed in patients with residual tumor volume > 2 mL when admitted to radiotherapy within 28 days after surgery.

PMID:34496417 | DOI:10.1055/s-0041-1730965

Neural Comput. 2021 May 13;33(6):1656-1697. doi: 10.1162/neco_a_01384.

ABSTRACT

We develop in this letter a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may be present in the response variable. The idea of EGM is to approximate the density function of the noise distribution instead of approximating the truth function directly as usual. Unlike the classical maximum likelihood estimation that encourages equal importance of all observations and could be problematic in the presence of abnormal observations, EGM schemes can be interpreted from a minimum distance estimation viewpoint and allow the ignorance of those observations. Furthermore, we show that several well-known robust nonconvex regression paradigms, such as Tukey regression and truncated least square regression, can be reformulated into this new framework. We then develop a learning theory for EGM by means of which a unified analysis can be conducted for these well-established but not fully understood regression approaches. This new framework leads to a novel interpretation of existing bounded nonconvex loss functions. Within this new framework, the two seemingly irrelevant terminologies, the well-known Tukey’s biweight loss for robust regression and the triweight kernel for nonparametric smoothing, are closely related. More precisely, we show that Tukey’s biweight loss can be derived from the triweight kernel. Other frequently employed bounded nonconvex loss functions in machine learning, such as the truncated square loss, the Geman-McClure loss, and the exponential squared loss, can also be reformulated from certain smoothing kernels in statistics. In addition, the new framework enables us to devise new bounded nonconvex loss functions for robust learning.

PMID:34496383 | DOI:10.1162/neco_a_01384

Neural Comput. 2021 May 13;33(6):1572-1615. doi: 10.1162/neco_a_01381.

ABSTRACT

An emerging paradigm proposes that neural computations can be understood at the level of dynamic systems that govern low-dimensional trajectories of collective neural activity. How the connectivity structure of a network determines the emergent dynamical system, however, remains to be clarified. Here we consider a novel class of models, gaussian-mixture, low-rank recurrent networks in which the rank of the connectivity matrix and the number of statistically defined populations are independent hyperparameters. We show that the resulting collective dynamics form a dynamical system, where the rank sets the dimensionality and the population structure shapes the dynamics. In particular, the collective dynamics can be described in terms of a simplified effective circuit of interacting latent variables. While having a single global population strongly restricts the possible dynamics, we demonstrate that if the number of populations is large enough, a rank R network can approximate any R-dimensional dynamical system.

PMID:34496384 | DOI:10.1162/neco_a_01381

J Cogn Neurosci. 2021 Jul 1;33(8):1549-1562. doi: 10.1162/jocn_a_01735.

ABSTRACT

Our understanding of the sensory environment is contextualized on the basis of prior experience. Measurement of auditory ERPs provides insight into automatic processes that contextualize the relevance of sound as a function of how sequences change over time. However, task-independent exposure to sound has revealed that strong first impressions exert a lasting impact on how the relevance of sound is contextualized. Dynamic causal modeling was applied to auditory ERPs collected during presentation of alternating pattern sequences. A local regularity (a rare p = .125 vs. common p = .875 sound) alternated to create a longer timescale regularity (sound probabilities alternated regularly creating a predictable block length), and the longer timescale regularity changed halfway through the sequence (the regular block length became shorter or longer). Predictions should be revised for local patterns when blocks alternated and for longer patterning when the block length changed. Dynamic causal modeling revealed an overall higher precision for the error signal to the rare sound in the first block type, consistent with the first impression. The connectivity changes in response to errors within the underlying neural network were also different for the two blocks with significantly more revision of predictions in the arrangement that violated the first impression. Furthermore, the effects of block length change suggested errors within the first block type exerted more influence on the updating of longer timescale predictions. These observations support the hypothesis that automatic sequential learning creates a high-precision context (first impression) that impacts learning rates and updates to those learning rates when predictions arising from that context are violated. The results further evidence automatic pattern learning over multiple timescales simultaneously, even during task-independent passive exposure to sound.

PMID:34496376 | DOI:10.1162/jocn_a_01735

J Cogn Neurosci. 2021 Jul 1;33(8):1397-1412. doi: 10.1162/jocn_a_01738.

ABSTRACT

Objects are grouped into categories through a complex combination of statistical and structural regularities. We sought to better understand the neural responses to the structural features of object categories that result from implicit learning. Adult participants were exposed to 32 object categories that contained three structural properties: frequency, variability, and co-occurrences, during an implicit learning task. After this exposure, participants completed a recognition task and were then presented with blocks of learned object categories during fMRI sessions. Analyses were performed by extracting data from ROIs placed throughout the fusiform gyri and lateral occipital cortex and comparing the effects of the different structural properties throughout the ROIs. Behaviorally, we found that symbol category recognition was supported by frequency, but not variability. Neurally, we found that sensitivity to object categories was greater in the right hemisphere and increased as ROIs were moved posteriorly. Frequency and variability altered the brain activation while processing object categories, although the presence of learned co-occurrences did not. Moreover, variability and co-occurrence interacted as a function of ROI, with the posterior fusiform gyrus being most sensitive to this relationship. This result suggests that variability may guide the learner to relevant co-occurrences and this is supported by the posterior ventral temporal cortex. Broadly, our results suggest that the internal features of the categories themselves are key factors in the category learning system.

PMID:34496382 | DOI:10.1162/jocn_a_01738

Am J Nephrol. 2021 Sep 8:1-13. doi: 10.1159/000517619. Online ahead of print.

ABSTRACT

INTRODUCTION: Transforming growth factor-β1 (TGF-β1) is a multifunctional cytokine, with diverse roles in fibrosis and inflammation, which acts through Smad signaling in renal pathology. We intended to investigate the expression of TGF-β/Smad signaling in glomerulonephritis (GN) and to assess its role as risk factor for progression to chronic kidney disease (CKD).

METHODS: We evaluated the immunohistochemical expression of TGF-β1, phosphorylated Smad3 (pSmad3), and Smad7 semiquantitatively and quantitatively using computerized image analysis program in different compartments of 50 renal biopsies with GN, and the results were statistically analyzed with clinicopathological parameters. We also examined the associations among their expressions, the impact of their co-expression, and their role in progression to CKD.

RESULTS: TGF-β1 expression correlated positively with segmental glomerulosclerosis (p= 0.025) and creatinine level at diagnosis (p = 0.002), while pSmad3 expression with interstitial inflammation (p = 0.024). In glomerulus, concomitant expressions of high Smad7 and medium pSmad3 were observed to be correlated with renal inflammation, such as cellular crescent (p = 0.011), intense interstitial inflammation (p = 0.029), and lower serum complement (C) 3 (p = 0.028) and C4 (p = 0.029). We also reported a significant association between pSmad3 expression in glomerular endothelial cells of proliferative GN (p = 0.045) and in podocytes of nonproliferative GN (p = 0.005). Finally, on multivariate Cox-regression analysis, TGF-β1 expression (hazard ratio = 6.078; 95% confidence interval: 1.168-31.627; p = 0.032) was emerged as independent predictor for CKD.

DISCUSSION/CONCLUSION: TGF-β1/Smad signaling is upregulated with specific characteristics in different forms of GN. TGF-β1 expression is indicated as independent risk factor for progression to CKD, while specific co-expression pattern of pSmad3 and Smad7 in glomerulus is correlated with renal inflammation.

PMID:34496361 | DOI:10.1159/000517619

Oncol Res Treat. 2021 Sep 8:1-9. doi: 10.1159/000519000. Online ahead of print.

ABSTRACT

INTRODUCTION: Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect persisting after completion of neurotoxic chemotherapies. This observational study was designed to evaluate the effectiveness of the dietary supplement OnLife® (patented mixture of specific fatty acids and palmitoylethanolamide) in improving symptoms of CIPN in breast and colon cancer patients.

METHODS: Improvement of CIPN was evaluated in adult patients, previously treated with (neo)adjuvant paclitaxel- (breast cancer) or oxaliplatin-based (colon cancer) therapies, receiving OnLife® for 3 months after completion of chemotherapy. The primary endpoint was to compare the severity of peripheral sensory neuropathy (PSN) and peripheral motor neuropathy (PMN) before and at the end of OnLife® treatment. Secondary endpoints included the assessment of patient-reported quality of life and CIPN symptoms as assessed by questionnaires.

RESULTS: 146 patients (n = 75 breast cancer patients and n = 71 colon cancer patients) qualified for analysis; 31.1% and 37.5% of breast cancer patients had an improvement of PSN and PMN, respectively. In colon cancer patients, PSN and PMN improved in 16.9% and 20.0% of patients, respectively. According to patient-reported outcomes, 45.9% and 37.5% of patients with paclitaxel-induced PSN and PMN, and 23.9% and 22.0% of patients with oxaliplatin-induced PSN and PMN experienced a reduction of CIPN symptoms, respectively.

CONCLUSION: OnLife® treatment confirmed to be beneficial in reducing CIPN severity and in limiting the progression of neuropathy, more markedly in paclitaxel-treated patients and also in patients with oxaliplatin-induced CIPN.

PMID:34496363 | DOI:10.1159/000519000

Schizophr Res. 2021 Sep 5;236:123-134. doi: 10.1016/j.schres.2021.08.005. Online ahead of print.

ABSTRACT

BACKGROUND: As the burden of treatment-resistant schizophrenia (TRS) on patients and society is high it is important to identify predictors of response to medications in TRS. The aim was to analyse whether baseline patient and study characteristics predict treatment response in TRS in drug trials.

METHODS: A comprehensive search strategy completed in PubMed, Cochrane and Web of Science helped identify relevant studies. The studies had to meet the following criteria: English language clinical trial of pharmacological treatment of TRS, clear definition of TRS and response, percentage of response reported, at least one baseline characteristic presented, and total sample size of at least 15. Meta-regression techniques served to explore whether baseline characteristics predict response to medication in TRS.

RESULTS: 77 articles were included in the systematic review. The overall sample included 7546 patients, of which 41% achieved response. Higher positive symptom score at baseline predicted higher response percentage. None of the other baseline patient or study characteristics achieved statistical significance at predicting response. When analysed in groups divided by antipsychotic drugs, studies of clozapine and other atypical antipsychotics produced the highest response rate.

CONCLUSIONS: This meta-analytic review identified surprisingly few baseline characteristics that predicted treatment response. However, higher positive symptoms and the use of atypical antipsychotics – particularly clozapine -was associated with the greatest likelihood of response. The difficulty involved in the prediction of medication response in TRS necessitates careful monitoring and personalised medication management. There is a need for more investigations of the predictors of treatment response in TRS.

PMID:34496316 | DOI:10.1016/j.schres.2021.08.005