Category: Statistics

Nat Rev Drug Discov. 2023 Feb 15. doi: 10.1038/s41573-023-00638-0. Online ahead of print.

ABSTRACT

The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.

PMID:36792750 | DOI:10.1038/s41573-023-00638-0

Sci Rep. 2023 Feb 15;13(1):2730. doi: 10.1038/s41598-023-29463-6.

ABSTRACT

Octopus vulgaris (Cuvier, 1797) is a cephalopod species with great economic value. In western Asturias (northwest of Spain), O. vulgaris artisanal fisheries are relatively well monitored and conditionally eco-labeled by the Marine Stewardship Council (MSC). Despite this, the Asturian octopus stocks have not been genetically assessed so far. In order to improve the current fishery plan and contrast the octopus eco-label validity in Asturias, 539 individuals from five regions of the O. vulgaris geographic distribution, including temporal samplings in Asturias, were collected and genotyped at thirteen microsatellite loci. All the samples under analysis were in agreement with Hardy-Weinberg expectations. Spatial levels of genetic differentiation were estimated using F-statistics, multidimensional scaling, and Bayesian analyses. Results suggested that the O. vulgaris consists of at least four genetically different stocks coming from two ancestral lineages. In addition, temporal analyses showed stability in terms of genetic variation and high N_E (> 50) for several generations in different localities within Asturias, pointing out to indeed sustainable fishery exploitation levels. Even though, the current Asturias fishery plan shows no significant genetic damages to the stocks, the regional-specific management plans need systematic genetic monitoring schemes as part of an efficient and preventive regional fishery regulation strategy.

PMID:36792695 | DOI:10.1038/s41598-023-29463-6

Commun Biol. 2023 Feb 15;6(1):175. doi: 10.1038/s42003-023-04546-2.

ABSTRACT

Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI “localizer” using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.

PMID:36792693 | DOI:10.1038/s42003-023-04546-2

Commun Biol. 2023 Feb 15;6(1):169. doi: 10.1038/s42003-023-04511-z.

ABSTRACT

Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.

PMID:36792689 | DOI:10.1038/s42003-023-04511-z

Sci Rep. 2023 Feb 15;13(1):2717. doi: 10.1038/s41598-023-28765-z.

ABSTRACT

Routine semen analysis provides limited information about a man’s male reproductive potential and can not always fully explain male infertility. Many male infertilities are caused by sperm DNA defects that routine semen quality analyses fail to detect. In this study, we analyzed the association of sperm DNA fragmentation index (DFI) with the semen routine, sperm morphology, in vitro fertilization embryo transfer (IVF-ET)/intracytoplasmic sperm injection (ICSI). Further, we explored the predictive value of sperm DFI in evaluating male fertility and the outcome of IVF-ET/ICSI. Data on sperm DFI, sperm routine, and sperm morphology were collected from 1462 males with infertility. According to DFI levels, there were 468 cases in group I (DFI ≤ 15%), 518 cases in group II (15% < DFI < 30%), and 476 cases in group III (DFI ≥ 30%). The correlations of sperm DFI with semen routine and malformation rate were analyzed. Seminal plasma malondialdehyde (MDA), and total antioxidant capacity (TAC) were assessed. Sperm DFI, semen routine, and sperm morphology were detected in male patients of 101 pairs of IVF-ET/ICSI infertile couples and subdivided into IVF-I group (DFI ≤ 15%), IVF-II group (15% < DFI < 30%), IVF-III group (DFI ≥ 30%), ICSI-I group (DFI ≤ 15%), ICSI-II group (15% < DFI < 30%) and ICSI-III group (DFI ≥ 30%) according to DFI value. The effect of sperm DFI on the outcome of IVF-ET/ICSI was analyzed. There were significant differences in sperm survival rate, sperm concentration, and PR% between groupIII and group II (P < 0.01). There were significant differences in sperm survival rate, sperm concentration and PR% between group III and group I (P < 0.01). There was no significant difference in semen volume, age, abstinence days, or percentage of normal sperm between the three groups (P > 0.05). DFI was positively correlated with MDA content ( P < 0.01) and negatively correlated with TAC (P < 0.01). Sperm DFI was negatively correlated with sperm survival rate, sperm concentration, and PR% (P < 0.01). There was no correlation with age, abstinence days, semen volume, or percentage of normal-form sperm (r = 0.16, 0.05, 0.04, -0.18, p > 0.05). Compared with IVF-I group, the sperm concentration and PR were decreased in IVF-III group. The sperm malformation rate was higher in IVF-III group than that in IVF-II group. Comparatively, the PR was decreased in ICSI-III group. The sperm malformation rate was higher in ICSI-III group than that of the ICSI-I group (P < 0.05). There were no statistically significant differences in fertilization rate, cleavage rate, embryo rate, and clinical pregnancy rate between IVF group or ICSI group, and between all subgroups (P > 0.05). Sperm DFI is negatively associated with sperm survival rate, sperm concentration, and PR%. Antioxidants can decrease the rate of DNA fragmentation. Sperm DFI has proven to be very valuable in the male fertility evaluation, but its significance as a predictor of pregnancy outcomes following assisted reproductive technology. (ART) requires further investigation.

PMID:36792684 | DOI:10.1038/s41598-023-28765-z

Sci Rep. 2023 Feb 15;13(1):2662. doi: 10.1038/s41598-023-29791-7.

ABSTRACT

Transcutaneous electrical nerve stimulator (TENS) has been demonstrated to be beneficial in glycemic control in animal models, but its application in humans has not been well studied. We randomly assigned 160 patients with type 2 diabetes on oral antidiabetic drugs 1:1 to the TENS study device (n = 81) and placebo (n = 79). 147 (92%) randomized participants (mean [SD] age 59 [10] years, 92 men [58%], mean [SD] baseline HbA_1c level 8.1% [0.6%]) completed the trial. At week 20, HbA_1c decreased from 8.1% to 7.9% in the TENS group (- 0.2% [95% CI – 0.4% to – 0.1%]) and from 8.1% to 7.8% in the placebo group (- 0.3% [95% CI – 0.5% to – 0.2%]) (P = 0.821). Glycemic variability, measured as mean amplitude of glycemic excursion (MAGE) at week 20 were significantly different in the TENS group vs. the placebo group (66 mg/dL [95% CI 58, 73] vs. 79 mg/dL [95% CI 72, 87]) (P = 0.009). Our study provides the clinical evidence for the first time in humans that TENS does not demonstrate a statistically significant HbA_1c reduction. However, it is a safe complementary therapy to improve MAGE in patients with type 2 diabetes.

PMID:36792682 | DOI:10.1038/s41598-023-29791-7

Sci Rep. 2023 Feb 15;13(1):2695. doi: 10.1038/s41598-023-29340-2.

ABSTRACT

The Systems Biology community has taken numerous actions to develop data and modeling standards towards FAIR data and model handling. Nevertheless, the debate about incentives and rewards for individual researchers to make their results reproducible is ongoing. Here, we pose the specific question of whether reproducible models have a higher impact in terms of citations. Therefore, we statistically analyze 328 published models recently classified by Tiwari et al. based on their reproducibility. For hypothesis testing, we use a flexible Bayesian approach that provides complete distributional information for all quantities of interest and can handle outliers. The results show that in the period from 2013, i.e., 10 years after the introduction of SBML, to 2020, the group of reproducible models is significantly more cited than the non-reproducible group. We show that differences in journal impact factors do not explain this effect and that this effect increases with additional standardization of data and error model integration via PEtab. Overall, our statistical analysis demonstrates the long-term merits of reproducible modeling for the individual researcher in terms of citations. Moreover, it provides evidence for the increased use of reproducible models in the scientific community.

PMID:36792648 | DOI:10.1038/s41598-023-29340-2

Stat Med. 2023 Feb 15. doi: 10.1002/sim.9684. Online ahead of print.

ABSTRACT

Many statistical methods have been applied to VAERS (vaccine adverse event reporting system) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. The AE ontology contains important information about biological similarities between AEs. In this paper, we develop a model to estimate vaccine-AE associations while incorporating the AE ontology. We model a group of AEs using the zero-inflated negative binomial model and then estimate the vaccine-AE association using the empirical Bayes approach. This model handles the AE count data with excess zeros and allows borrowing information from related AEs. The proposed approach was evaluated by simulation studies and was further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS) dataset. The proposed method is implemented in an R package available at https://github.com/umich-biostatistics/zGPS.AO.

PMID:36791465 | DOI:10.1002/sim.9684

J Neural Eng. 2023 Feb 15. doi: 10.1088/1741-2552/acbc4b. Online ahead of print.

ABSTRACT

OBJECTIVE: Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN).

APPROACH: An unsupervised and a supervised objective were jointly optimised by combining an autoencoder and an artefact classifier in one multi-output model that processes multi-channel EEG inputs. The proposed semi-supervised multi-task training strategy was compared to a classical supervised strategy and other existing state-of-the-art models. The models were trained and tested separately on two different datasets, which contained partially annotated multi-channel neonatal EEG. Models were evaluated using the F1-statistic and the relevance of the method was investigated in the context of a functional brain age prediction model.

MAIN RESULTS: The proposed multi-task and multi-channel CNN methods outperformed state-of-the-art methods, reaching F1 scores of 86.2% and 95.7% on two separate datasets. The proposed semi-supervised multi-task training strategy was shown to be superior to a classical supervised training strategy when the amount of labels in the dataset was artificially reduced. Finally, we found that the error of a brain age prediction model correlated with the amount of automatically detected artefacts in the EEG segment.

SIGNIFICANCE: Our results show that the proposed semi-supervised multi-task training strategy can train CNNs successfully even when the amount of labels in the dataset is limited. Therefore, this method is a promising semi-supervised technique for developing deep learning models with scarcely labelled data. Moreover, a correlation between the error of functional brain age estimates and the amount of detected artefacts in the corresponding EEG segments indicates the relevance of artefact detection for robust automated EEG analysis.

PMID:36791462 | DOI:10.1088/1741-2552/acbc4b

Am Fam Physician. 2023 Feb;107(2):159-164.

ABSTRACT

Trigger points producing myofascial pain syndromes are common in primary care. Located within skeletal muscle, trigger points are taut, band-like nodules capable of producing pain and disability. Some evidence from clinical trials supports massage, physical therapy, and osteopathic manual medicine as first-line less invasive treatment strategies. Trigger points are often treated with injections; although randomized trials have found statistically significant results with trigger point injections, conclusions are limited by low numbers of study participants, difficulty in blinding, the potential for a placebo effect, and lack of posttreatment follow-up. No single pharmacologic agent used in trigger point injections has been proven superior to another, nor has any single agent been proven superior to placebo. Trigger point injections, therefore, should be reserved for patients whose myofascial pain has been refractory to other measures, and family physicians should first employ less invasive treatment strategies. Trigger point management is only one part of a comprehensive, multimodal, and team-based approach to patients with myofascial pain.

PMID:36791442