Review of: "COVID-19 Social Media Information and Misinformation: a Geospatial Analysis of Inter-regional Tweet Disparities in Ghana"

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Review of the Maximum Likelihood Functions for Right Censored Data. A New Elementary Derivation.

Censoring is a well known feature recurrent in the analysis of lifetime data, occurring in the model when exact lifetimes can be collected for only a representative portion of the surveyed individuals. If lifetimes are known only to exceed some given values, it is referred to as right censoring. In this paper we propose a systematization and a new derivation of the likelihood function for right censored sampling schemes; calculations are reported and assumptions are carefully stated. The sampling schemes considered (Type I, II and Random Censoring) give rise to the same ML function. Only the knowledge of elementary probability theory, namely the definitions of the order statistics and the conditional probability distribution function, are required in the proofs. Lastly we give an intuitive interpretation of Type I Censoring as a special case of Random Censoring, so that a global theory holds

Crude Cumulative Incidence in the form of a Horvitz-Thompson like and Kaplan-Meier like Estimator

The link between the nonparametric estimator of the crude cumulative incidence of a competing risk and the Kaplan-Meier estimator is exploited. The equivalence of the nonparametric crude cumulative incidence to an inverse-probability-of-censoring weighted average of the sub-distribution function is proved. The link between the estimation of crude cumulative incidence curves and Gray\u27s family of nonparametric tests is considered. The crude cumulative incidence is proved to be a Kaplan-Meier like estimator based on the sub-distribution hazard, i.e. the quantity on which Gray\u27s family of tests is based. A standard probabilistic formalism is adopted to have a note accessible to applied statisticians

Fibroblast growth factor 23: translating analytical improvement into clinical effectiveness for tertiary prevention in chronic kidney disease

Fibroblast growth factor 23 (FGF23) plays a key role in the pathophysiology of chronic kidney disease (CKD) and of the associated cardiovascular diseases, ranking on the crossroads of several evolving areas with a relevant impact on the health-care system (ageing, treatment of CKD and prevention from cardiovascular and renal events). In this review, we will critically appraise the overall issues concerning the clinical usefulness of FGF23 determination in CKD, focusing on the analytical performances of the methods, aiming to assess whether and how the clinical introduction of FGF23 may promote cost-effective health care policies in these patients

Distant metastasis dynamics following subsequent surgeries after primary breast cancer removal

Background: The aim of the research was to separate the distant metastasis (DM) enhancing effect due to breast tumour removal from that due to surgical manoeuvre by itself. Methods: DM dynamics following surgery for ipsilateral breast tumour recurrence (IBTR), contralateral breast cancer (CBC) and delayed reconstruction (REC), which was performed after the original breast cancer surgical removal, was analysed. A total of 338 patients with IBTR, 239 with CBC and 312 with REC were studied. Results: The DM dynamics following IBTR, CBC and REC, when assessed with time origin at their surgical treatment, is similar to the analogous pattern following primary tumour removal, with a first major peak at about 18 months and a second lower one at about 5 years from surgery. The time span between primary tumour removal and the second surgery is influential on DM risk levels for IBTR and CBC patients, not for REC patients. Conclusions: The role of breast tumour removal is different from the role of surgery by itself. Our findings suggest that the major effect of reconstructive surgery is microscopic metastasis acceleration, while breast tumour surgical removal (either primary or IBTR or CBC) involves both tumour homeostasis interruption and microscopic metastasis growth acceleration. The removal of a breast tumour would eliminate its homeostatic restrains on metastatic foci, thus allowing metastasis development, which, in turn, would be supported by the forwarding action of the mechanisms triggered by the surgical wounding.publishedVersio

Cell Polarity, Epithelial-Mesenchymal Transition, and Cell-Fate Decision Gene Expression in Ductal Carcinoma In Situ

Loss of epithelial cell identity and acquisition of mesenchymal features are early events in the neoplastic transformation of mammary cells. We investigated the pattern of expression of a selected panel of genes associated with cell polarity and apical junction complex or involved in TGF-β-mediated epithelial-mesenchymal transition and cell-fate decision in a series of DCIS and corresponding patient-matched normal tissue. Additionally, we compared DCIS gene profile with that of atypical ductal hyperplasia (ADH) from the same patient. Statistical analysis identified a “core” of genes differentially expressed in both precursors with respect to the corresponding normal tissue mainly associated with a terminally differentiated luminal estrogen-dependent phenotype, in agreement with the model according to which ER-positive invasive breast cancer derives from ER-positive progenitor cells, and with an autocrine production of estrogens through androgens conversion. Although preliminary, present findings provide transcriptomic confirmation that, at least for the panel of genes considered in present study, ADH and DCIS are part of a tumorigenic multistep process and strongly arise the necessity for the regulation, maybe using aromatase inhibitors, of the intratumoral and/or circulating concentration of biologically active androgens in DCIS patients to timely hamper abnormal estrogens production and block estrogen-induced cell proliferation

Validation of Gene Expression Profiles in Genomic Data through Complementary Use of Cluster Analysis and PCA-Related Biplots

High-throughput genomic assays are used in molecular biology to explore patterns of joint expression of thousands of genes. These methodologies had relevant developments in the last decade, and concurrently there was a need for appropriate methods for analyzing the massive data generated. Identifying sets of genes and samples characterized by similar values of expression and validating these results are two critical issues related to these investigations because of their clinical implication. From a statistical perspective, unsupervised class discovery methods like Cluster Analysis are generally adopted. However, the use of Cluster Analysis mainly relies on the use of hierarchical techniques without considering possible use of other methods. This is partially due to software availability and to easiness of representation of results through a heatmap, which allows to simultaneously visualize clusterization of genes and samples on the same graphical device. One drawback of this strategy is that clusters' stability is often neglected, thus leading to over-interpretation of results. Moreover, validation of results using external datasets is still subject of discussion, since it is well known that batch effects may condition gene expression results even after normalization. In this paper we compared several clustering algorithms (hierarchical, k-means, model-based, Affinity Propagation) and stability indices to discover common patterns of expression and to assess clustering reliability, and propose a rank-based passive projection of Principal Components for validation purposes. Results from a study involving 23 tumor cell lines and 76 genes related to a specific biological pathway and derived from a publicly available dataset, are presented

A comparison of three different methods for classification of breast cancer data

The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classification of a novel dataset and perform a methodological comparison of these. We used the C4.5 tree classifier, a Multilayer Perceptron and a naïve Bayes classifier over a large set of tumour markers. We found good performance of the Multilayer Perceptron even when we reduced the number of features to be classified. We found naive Bayes achieved a competitive performance even though the assumption of normality of the data is strongly violated