A novel flexible exponent power-X family of distributions with applications to COVID-19 mortality rate in Mexico and Canada.

This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

Self-decisive algorithm for unconstrained optimization problems as in biomedical image analysis.

This study describes the construction of a new algorithm where image processing along with the two-step quasi-Newton methods is used in biomedical image analysis. It is a well-known fact that medical informatics is an essential component in the perspective of health care. Image processing and imaging technology are the recent advances in medical informatics, which include image content representation, image interpretation, and image acquisition, and focus on image information in the medical field. For this purpose, an algorithm was developed based on the image processing method that uses principle component analysis to find the image value of a particular test function and then direct the function toward its best method for evaluation. To validate the proposed algorithm, two functions, namely, the modified trigonometric and rosenbrock functions, are tested on variable space.

Are these data real? Statistical methods for the detection of data fabrication in clinical trials.

OBJECTIVES: To test the application of statistical methods to detect data fabrication in a clinical trial. SETTING: Data from two clinical trials: a trial of a dietary intervention for cardiovascular disease and a trial of a drug intervention for the same problem. OUTCOME MEASURES: Baseline comparisons of means and variances of cardiovascular risk factors; digit preference overall and its pattern by group. RESULTS: In the dietary intervention trial, variances for 16 of the 22 variables available at baseline were significantly different, and 10 significant differences were seen in means for these variables. Some of these P values were extraordinarily small. Distributions of the final recorded digit were significantly different between the intervention and the control group at baseline for 14/22 variables in the dietary trial. In the drug trial, only five variables were available, and no significant differences between the groups for baseline values in means or variances or digit preference were seen. CONCLUSIONS: Several statistical features of the data from the dietary trial are so strongly suggestive of data fabrication that no other explanation is likely.

The effect of scientific misconduct on the results of clinical trials: a Delphi survey.

OBJECTIVES: To discover what types of scientific misconduct are most likely to influence the results of a clinical trial. DESIGN: Delphi survey of expert opinion with three rounds of consultation. SETTING: Non-industry clinical trial "community". PARTICIPANTS: Experts identified from invitees to a previous MRC consultation on clinical trials. 32 out of the 40 experts approached agreed to participate. RESULTS: We identified thirteen forms of scientific misconduct for which there was majority agreement (>50%) that they would be likely or very likely to distort the results and majority agreement (>50%) that they would be likely or very likely to occur. Of these, the over-interpretation of 'significant' findings in small trials, selective reporting and inappropriate subgroup analyses were the main themes. CONCLUSIONS: According to this expert group, the most important forms of scientific misconduct in clinical trials are selective reporting and the opportunistic use of the play of chance. Data fabrication and falsification were not rated highly because it was considered that these were unlikely to occur. Registration and publication of detailed clinical trial protocols could make an important contribution to preventing scientific misconduct.