A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia

Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient's follow-up, employed to develop a reference pool of patients. Score evolution's similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient's biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution's similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL.

Effect of public health interventions during the first epidemic wave of COVID-19 in Cyprus: a modelling study

BACKGROUND

Cyprus addressed the first wave of SARS CoV-2 (COVID-19) by implementing non-pharmaceutical interventions (NPIs). The aims of this study were: a) to estimate epidemiological parameters of this wave including infection attack ratio, infection fatality ratio, and case ascertainment ratio, b) to assess the impact of public health interventions and examine what would have happened if those interventions had not been implemented.

METHODS

A dynamic, stochastic, individual-based Susceptible-Exposed-Infected-Recovered (SEIR) model was developed to simulate COVID-19 transmission and progression in the population of the Republic of Cyprus. The model was fitted to the observed trends in COVID-19 deaths and intensive care unit (ICU) bed use.

RESULTS

By May 8th, 2020, the infection attack ratio was 0.31% (95% Credible Interval [CrI]: 0.15, 0.54%), the infection fatality ratio was 0.71% (95% CrI: 0.44, 1.61%), and the case ascertainment ratio was 33.2% (95% CrI: 19.7, 68.7%). If Cyprus had not implemented any public health measure, the healthcare system would have been overwhelmed by April 14th. The interventions averted 715 (95% CrI: 339, 1235) deaths. If Cyprus had only increased ICU beds, without any social distancing measure, the healthcare system would have been overwhelmed by April 19th.

CONCLUSIONS

The decision of the Cypriot authorities to launch early NPIs limited the burden of the first wave of COVID-19. The findings of these analyses could help address the next waves of COVID-19 in Cyprus and other similar settings.

Modeling the first wave of Covid-19 pandemic in the Republic of Cyprus

We present different data analytic methodologies that have been applied in order to understand the evolution of the first wave of the Coronavirus disease 2019 in the Republic of Cyprus and the effect of different intervention measures that have been taken by the government. Change point detection has been used in order to estimate the number and locations of changes in the behaviour of the collected data. Count time series methods have been employed to provide short term projections and a number of various compartmental models have been fitted to the data providing with long term projections on the pandemic's evolution and allowing for the estimation of the effective reproduction number.

Effect of resveratrol on non-alcoholic fatty liver disease

The aim of the present study was to investigate the effect of a micronized formulation of trans-resveratrol in humans with non-alcoholic fatty liver disease (NAFLD). Trans-Resveratrol has been used in the form of micronized formulation, which is better absorbed, has strong antioxidants effects, is more effective than plain resveratrol formulations and is circulated on the market as a food supplement. Resveratrol (3,5,4′-trihydroxy-trans-stilbene) is a stilbenoid and a phytoalexin produced by several plants. NAFLD is an increasing clinical problem involving the liver for which effective treatments are required. The present study was based on two patient groups. The study, which commenced on April 2013 and finished on April 2015, included 44 patients, aged 29–70 years, with an average weight of 84.6 kg (n=22 per group; 28 men and 16 women) who were randomly assigned to groups and given 50 mg Evelor capsule (n=22) and 200 mg Evelor H tablet (n=22) correspondingly on a daily basis. The patients were followed up for 6 months. Quantity fat measurements, with ultrasound on the liver and kidney, were carried out. There was an initial measurement (time 1) and one after six months (time 2). The study results showed the effects of Trans-resveratrol micronized formulation in reducing the liver fat, as well as decreasing hepatic enzymes, serum glutamate pyruvic transaminase (SGPT) and gamma-glutamyl transpeptidase (g-GT) and insulin resistance. At the end of the study, the statistical analysis showed a statistically significant reduction on the liver fat. These data demonstrate that use of Trans-resveratrol micronized formulation improves features of NAFLD, and prevents liver damage. Thus, Trans-resveratrol micronized formulation can be a new treatment method for NAFLD.

The effect of resveratrol on hypertension: A clinical trial

The aim of this clinical trial was to investigate the effects of Evelor, a micronized formulation of resveratrol (RESV; 3,5,4'-trihydroxy-trans-stilbene), in patients with primary hypertension. RESV is a stilbenoid and phytoalexin produced by several plants in response to injury or attack by pathogens, such as bacteria and fungi. Patients included in the clinical trial were split into the following two groups, based on the severity of their disease: Group A (n=46), stage I hypertension [systolic blood pressure (SBP), 140-159 mmHg; diastolic blood pressure (DBP), 90-99 mmHg] and Group B (n=51), stage II hypertension (SBP, 160-179 mmHg; DBP, 100-109 mmHg). Each group was divided into two subgroups: A1 and B1, patients treated with standard antihypertensive therapy (A1, 10 mg Dapril; B1, 20 mg Dapril), and A2 and B2, patients treated with antihypertensive therapy (Dapril) plus Evelor. The present study aimed to determine the effects of Evelor, in addition to the standard hypertension treatment, and its effect on the hepatic enzymes serum glutamate-pyruvate transaminase (SGPT) and gamma-glutamyl transferase (gamma-GT). Following the trial, which lasted two years (October 2010 to October 2012), the mean blood pressure of both groups lay within the normal range, indicating that blood pressure was efficiently controlled. The results of the present study demonstrate that the addition of RESV to standard antihypertensive therapy is sufficient to reduce blood pressure to normal levels, without the need for additional antihypertensive drugs. In addition, statistical analysis of the results identified a significant reduction in plasma concentration levels of SGPT (P<0.001) and gamma-GT (P<0.001) with the addition of RESV, indicating that RESV prevents liver damage.

Inference for the relative treatment effect with the density ratio model

Consider the problem of estimating and testing the relative treatment effect between two populations based on a random sample from each distribution. Under the well–established normal theory, inference is based on analysis of variance methods. However, there are many examples of skewed data which show that normal theory is not applicable. Then the problem of inference regarding the treatment effect can be attacked by standard nonparametric methods. In this paper, we propose a semiparametric model, the so–called density ratio model, which specifies that the log–likelihood ratio of two densities is linear in some parameters. For testing hypotheses regarding the relative treatment effect, a robust test is obtained by employing the density ratio model for a suitable Box–Cox transformation of the data. The transformation, along with the density ratio model, are estimated by maximum empirical likelihood. The new test procedure is studied theoretically and it is applied to real and simulated data. It is further compared with some nonparametric competitors, and it is found to have relatively high power across a wide variety of distributions, including those outside the density ratio family.

Two Cholesky-log-GARCH models for multivariate volatilities

Parsimonious estimation of high-dimensional covariance matrices is of fundamental importance in multivariate statistics. Typical examples occur in finance, where the instantaneous dependence among several asset returns should be taken into account. Multivariate GARCH processes have been established as a standard approach for modelling such data. However, the majority of GARCH-type models are either based on strong assumptions that may not be realistic or require restrictions that are often too hard to be satisfied in practice. We consider two alternative decompositions of time-varying covariance matrices [Formula: see text]. The first is based on the modified Cholesky decomposition of the covariance matrices and second relies on the hyperspherical parametrization of the standard Cholesky factor of their correlation matrices [Formula: see text]. Then, we combine each Cholesky factor with the log-GARCH models for the corresponding time–varying volatilities and use a quasi maximum likelihood approach to estimate the parameters. Using log-GARCH models is quite natural for achieving the positive definiteness of [Formula: see text] and this is a novelty of this work. Application of the proposed methodologies to two real financial datasets reveals their usefulness in terms of parsimony, ease of implementation and stresses the choice of the appropriate models using familiar data-driven processes such as various forms of the exploratory data analysis and regression.

On Outliers and Interventions in Count Time Series following GLMs

We discuss the analysis of count time series following generalised linear models in the presence of outliers and intervention effects. Different modifications of such models are formulated which allow to incorporate, detect and to a certain degree distinguish extraordinary events (interventions) of different types in count time series retrospectively. An outlook on extensions to the problem of robust parameter estimation, identification of the model orders by robust estimation of autocorrelations and partial autocorrelations, and online surveillance by sequential testing for outlyingness is provided. 

Interventions in log-linear Poisson autoregression

We consider the problem of estimating and detecting outliers in count time series data following a log-linear observation driven model. Log-linear models for count time series arise naturally because they correspond to the canonical link function of the Poisson distribution. They yield both positive and negative dependence, and covariate information can be conveniently incorporated. Within this framework, we establish test procedures for detection of unusual events (‘interventions’) leading to different kinds of outliers, we implement joint maximum likelihood estimation of model parameters and outlier sizes and we derive formulae for correcting the data for detected interventions. The effectiveness of the proposed methodology is illustrated with two real data examples. The first example offers a fresh data analytic point of view towards the polio data. Our methodology identifies different forms of outliers in these data by an observation-driven model. The second example deals with some campylobacterosis data which we analyzed in a previous communication, by a different model. The results are reconfirmed by the new model that we put forward in this communication. The reliability of the procedure is verified using artificial data examples.

Order-restricted semiparametric inference for the power bias model

The power bias model, a generalization of length-biased sampling, is introduced and investigated in detail. In particular, attention is focused on order-restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations without resorting to any parametric assumptions. Examples and real data analysis demonstrate the usefulness of this approach.

Identifying periodically expressed transcripts in microarray time series data

MOTIVATION

Microarray experiments are now routinely used to collect large-scale time series data, for example to monitor gene expression during the cell cycle. Statistical analysis of this data poses many challenges, one being that it is hard to identify correctly the subset of genes with a clear periodic signature. This has lead to a controversial argument with regard to the suitability of both available methods and current microarray data.

METHODS

We introduce two simple but efficient statistical methods for signal detection and gene selection in gene expression time series data. First, we suggest the average periodogram as an exploratory device for graphical assessment of the presence of periodic transcripts in the data. Second, we describe an exact statistical test to identify periodically expressed genes that allows one to distinguish periodic from purely random processes. This identification method is based on the so-called g-statistic and uses the false discovery rate approach to multiple testing.

RESULTS

Using simulated data it is shown that the suggested method is capable of identifying cell-cycle-activated genes in a gene expression data set even if the number of the cyclic genes is very small and regardless the presence of a dominant non-periodic component in the data. Subsequently, we re-examine 12 large microarray time series data sets (in part controversially discussed) from yeast, human fibroblast, human HeLa and bacterial cells. Based on the statistical analysis it is found that a majority of these data sets contained little or no statistical significant evidence for genes with periodic variation linked to cell cycle regulation. On the other hand, for the remaining data the method extends the catalog of previously known cell-cycle-specific transcripts by identifying additional periodic genes not found by other methods. The problem of distinguishing periodicity due to generic cell cycle activity and to artifacts from synchronization is also discussed.

AVAILABILITY

The approach has been implemented in the R package GeneTS available from http://www.stat.uni-muenchen.de/~strimmer/software.html under the terms of the GNU General Public License.