<abstract xmlns="http://www.w3.org/1999/xhtml"> The best effective dose of a chemotherapy is defined using the maximum tolerated dose (MTD) of toxicity. It is possible that the toxicity of a dose may increase when the dose-response curve is not monotonic. In the case of metronomic chemotherapy (MC) a 1/10th level of MC dose is considered as a targeted dose of therapy and is safer in terms of toxicity levels. The objective of this study is to develop an algorithm based on the dose response model of MC to evaluate the best effective dose based on the molecular target agent. The molecular target agent is defined as the optimal biological dose achieved by the best effective dose, as the lowest dose with the highest rate of safety and efficacy. The first proposed design is parametric and assumes a logistic dose-efficacy curve for dose determination, and the second design uses quadratic regression to identify the optimal biological dose. We conducted extensive simulation studies to investigate the operating characteristics of the proposed designs. Simulation studies provide a possible way to decide on the best effective dose of MC to be considered in further phases through the finding of the optimal biological dose. The proposed design is assumed, with the threshold value of optimum biological dose (OBD), to detect the best dose of MC. This consistent design with specific dose response models can be recommended for practice.</abstract>

An Algorithm to Define the Optimum Biological Dose of Metronomic Chemotherapy

<abstract xmlns="http://www.w3.org/1999/xhtml"> This paper investigates a stopping rule to be utilised in phase I clinical trials. The motivation is to develop a dynamic rule so that a trial stops early if the maximum tolerated dose lies towards the beginning of a dose region. Also, it will employ many patients if the maximum tolerated dose lies towards the end of a dose region. A two-parameter logistic model is assumed for the dose-response data. A trial is stopped early before reaching the maximum number of patients when the width of the Bayesian posterior probability interval of the slope parameter meets a desired value. Instead of setting a pre-specified width to stop at, we determine it based on the parameter estimate obtained after a reasonable number of steps in a trial. Simulation studies of six plausible dose-response scenarios show that the proposed stopping rule is capable of limiting the number of patients to be recruited depending on the underlying scenario. Although the rule is applied to a D-optimum design here, it will be equally applicable to other model-based designs.</abstract>

A dynamic stopping rule for phase I clinical trials

<abstract xmlns="http://www.w3.org/1999/xhtml"> Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitance to adopt this approach may stem from an unfamiliarity with the computational tools necessary for computing Bayes factors. Previous work has shown that closed-form approximations of Bayes factors are relatively easy to obtain for between-groups methods, such as an analysis of variance or t-test. In this paper, I extend this approximation to develop a formula for the Bayes factor that directly uses information that is typically reported for ANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples of its use, I report the results of simulations which show that even with minimal input, this approximate Bayes factor produces similar results to existing software solutions.</abstract>

Computing Bayes factors to measure evidence from experiments: An extension of the BIC approximation

<abstract xmlns="http://www.w3.org/1999/xhtml"> We introduce a method that eliminates the specification error and spurious relationships in regression. In addition, we introduce a test of strong causality. Furthermore, hypothesis testing (inference) becomes almost unneeded. Moreover, this method virtually resolves error problems such as heteroscedasticity, autocorrelation, non-stationarity and endogeneity.</abstract>

The perfect regression and causality test: A solution to regression problems

<abstract xmlns="http://www.w3.org/1999/xhtml"> Half F2 diallel crosses of eight spring cultivars of rapeseed were used in partial circulant diallel analyses to estimate biometric genetic parameters for phenological traits, yield components and seed yield. The greatest variation in the GCA-to-SCA mean square ratio was related to days to flowering, and its lowest variation to days to maturity. A high coefficient of variation of the narrow-sense heritability estimate was obtained for days to maturity, followed by plant height and seed yield. For diallel analysis with a low number of parents, the differences of the heritability estimates of the traits were small; therefore, for precise estimation of heritability, a high number of diallel crosses will be preferred.</abstract>

Effect of number of parents on some important biometric genetic parameters in rapeseed (Brassica napus L.)

<abstract xmlns="http://www.w3.org/1999/xhtml"> The purpose of breeding experiments is to predict the best yielding lines to be registered. Unfortunately, the results obtained in different locations and years are often different. The main objective of this study was the evaluation and choice of experimental locations. The methods used included ANOVA, Andrews’ curves, PCA, cluster analysis, coefficients of usefulness and heritability coefficients. The experimental data are derived from prepreliminary and preliminary breeding experiments with spring barley (Hordeum vulgare L.), malting and fodder, conducted in the period from 2008 to 2013 at six experimental stations in Poland. The results showed that some of the locations were similar in respect of the analyzed coefficients, while some locations were unique. The most valuable locations were indicated as those which have the greatest contribution to the interaction and the greatest usefulness (the lowest joint usefulness coefficient). This is because, at the last stage of new variety cultivation, when new varieties are to be registered, they are evaluated in more variable experimental environments.</abstract>

The analysis of usefulness of locations on the basis of spring barley trials

<abstract xmlns="http://www.w3.org/1999/xhtml"> The objective of this study was to assess genotype-by-environment interaction for seed glucosinolate content in winter rapeseed cultivars grown in western Poland using the additive main effects and multiplicative interaction model. The study concerned 25 winter rapeseed genotypes (15 F1 CMS ogura hybrids, parental lines and two European cultivars: open pollinated Californium and F1 hybrid Hercules), evaluated at five locations in a randomized complete block design with four replicates. The seed glucosinolate content of the tested genotypes ranged from 5.53 to 16.80 μmol∙g-1 of seeds, with an average of 10.26 μmol∙g-1. In the AMMI analyses, 48.67% of the seed glucosinolate content variation was explained by environment, 13.07% by differences between genotypes, and 17.56% by genotype-by-environment interaction. The hybrid PN66×PN07 is recommended for further inclusion in the breeding program due to its low average seed glucosinolate content; the restorer line PN18, CMS ogura line PN66 and hybrids PN66×PN18 and PN66×PN21 are recommended because of their stability and low seed glucosinolate content.</abstract>

Genotype-by-environment interaction for seed glucosinolate content in winter oilseed rape (Brassica napus L.) using an additive main effects and multiplicative interaction model

AHEAD OF PRINT

Volume 60 (2023): Issue 2 (December 2023)

Volume 60 (2023): Issue 1 (June 2023)

Volume 59 (2022): Issue 2 (December 2022)

Volume 59 (2022): Issue 1 (June 2022)

Volume 58 (2021): Issue 2 (December 2021)

Volume 58 (2021): Issue 1 (June 2021)

Volume 57 (2020): Issue 2 (December 2020)

Volume 57 (2020): Issue 1 (June 2020)

Volume 56 (2019): Issue 2 (December 2019)

Volume 56 (2019): Issue 1 (June 2019)

Volume 55 (2018): Issue 2 (December 2018)

Volume 55 (2018): Issue 1 (June 2018)

Volume 54 (2017): Issue 2 (December 2017)

Volume 54 (2017): Issue 1 (June 2017)

Volume 53 (2016): Issue 2 (December 2016)

Volume 53 (2016): Issue 1 (June 2016)

Volume 52 (2015): Issue 2 (December 2015)

Volume 52 (2015): Issue 1 (June 2015)

Volume 51 (2014): Issue 2 (December 2014)

Volume 51 (2014): Issue 1 (June 2014)

Volume 50 (2013): Issue 2 (December 2013)

Volume 50 (2013): Issue 1 (June 2013)

Volume 49 (2012): Issue 2 (December 2012)

Volume 49 (2012): Issue 1 (June 2012)

Biometrical Letters

 Biometrical Letters is the official journal of the Polish Biometric Society, founded in 1964, now published with the financial support of the Ministry of Science and Higher Education. The anonymous double-blind peer review by the two independent reviewers is applied to all the papers.  The purposes of Biometrical Letters are:   publishing papers dealing with various aspects of the analysis and interpretation of results from biometrical experiments, using mathematical and statistical methods;  promoting and extending the use of statistical and mathematical methods in the principal disciplines of life sciences by reporting on the development and application of these methods;  presenting statistical and mathematical tools useful in biometry. Methodological developments should be motivated by interesting problems from life sciences, applied or applicable;  publishing review papers and papers presenting practical use of various methods of data analysis and statistical inference.   The core of most Biometrical Letters articles is statistical inference that sets scientific or policy objectives, motivates methodological development and demonstrates the operation of new methods.  The paper should include a description of the biometrical problem and a section detailing the application of the new methodology.  The Journal welcomes the submission of manuscripts from bioscience, mathematical and statistical theory, biology, genetics, medical statistics, agriculture, forestry, ecology, environmental sciences, biometrics and clinical trials. Case studies and articles on the use and development of statistical software are also welcome.  Rejection Rate   20%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 