Authors: Ilija Barukčić
Comments: 26 pages. Copyright © 2019 by Ilija Barukčić, Jever, Germany. All rights reserved. Published 15.2.2019 by Journal of Drug Delivery and Therapeutics, 9(1-s), 148-160. https://doi.org/10.22270/jddt.v9i1-s.2273
Objective: The aim of this study is to re-evaluate the relationship between smoking and lung cancer.
Methods: In order to clarify the relationship between cigarette smoking and lung cancer, a review and meta-analysis of appropriate studies with a total sample size of n = 48393 was conducted. The p-value was set to p < 0,05.
It was not possible to reject the null-hypothesis H0: without smoking no lung cancer. Furthermore, the null-hypothesis H0: No causal relationship between smoking and lung cancer was rejected.
Compared to the results from previous studies, the results of this study confirm previously published results. According the results of this study, without smoking no lung cancer. Smoking is the cause of lung cancer.
Keywords: Smoking, lung cancer, causal relationship
Authors: Ilija Barukčić
Comments: Comments: 70 pages. Copyright © 2019 by Ilija Barukčić, Jever, Germany. All rights reserved. Published by
Objective: Objective scientific knowledge for many authors more valuable than true subjective belief is determined by research on primary data but a renewed analysis of already recorded or published data is common too. Ever since, an appropriate experimental or study design is an important and often a seriously underappreciated aspect of the informativeness and the scientific value of any (medical) study. The significance of study design for the reliability of the conclusions drawn and the ability to generalize the results from the sample investigated for the whole population cannot be underestimated. In contrast to an inappropriate statistical evaluation of a medical study, it is difficult to correct errors in study design after the study has been completed. Various mathematical aspects of study design are discussed in this article.
Methods: In assessing the significance of a fair study design of a medical study, important measures of publication bias are introduced. Methods of data or publication bias analysis in different types of studies are illustrated through examples with fictive data. Formal mathematical requirements of a fair study design which can and should be fulfilled carefully with regard to the planning or evaluation of medical research are developed.
Various especially mathematical aspects of a fair study design are discussed in this article in detail. Depending on the particular question being asked, mathematical methods are developed which allow us to recognize data which are self-contradictory and to exclude these data from systematic literature reviews and meta-analyses. As a result, different individual studies can be summed up and evaluated with a higher degree of certainty.
This article is intended to give the reader guidance in evaluating the design of studies in medical research even ex post which should enable the reader to categorize medical studies better and to assess their scientific quality more accurately.
Keywords: study design, quality, study, study type, measuring technique, publication bias
Authors: Stephen P. Smith
Comments: 15 Pages.
A second order time series model is described, and generalized to the multivariate situation. The model is highly flexible, and is suitable for non-parametric regression, coming with unequal time steps. The resulting K-matrix is described, leading to its possible factorization and differentiation using general purpose software that was recently developed. This makes it possible to estimate variance matrices in the multivariate model corresponding the signal and noise components of the model, by restricted maximum likelihood. A nested iteration algorithm is presented for conducting the maximization, and an illustration of the methods are demonstrated on a 4-variate time series with 89 observations.