StatiCAL: an interactive tool for statistical analysis of biomedical data and scientific valorization.

BACKGROUND: In the realm of biomedical research, the growing volume, diversity and quantity of data has escalated the demand for statistical analysis as it is indispensable for synthesizing, interpreting, and publishing data. Hence the need for accessible analysis tools drastically increased. StatiCAL emerges as a user-friendly solution, enabling researchers to conduct basic analyses without necessitating extensive programming expertise. RESULTS: StatiCAL includes divers functionalities: data management, visualization on variables and statistical analysis. Data management functionalities allow the user to freely add or remove variables, to select sub-population and to visualise selected data to better perform the analysis. With this tool, users can freely perform statistical analysis such as descriptive, graphical, univariate, and multivariate analysis. All of this can be performed without the need to learn R coding as the software is a graphical user interface where all the action can be performed by clicking a button. CONCLUSIONS: StatiCAL represents a valuable contribution to the field of biomedical research. By being open-access and by providing an intuitive interface with robust features, StatiCAL allow researchers to gain autonomy in conducting their projects.

Italian and Middle Eastern adherence to Mediterranean diet in relation to Body Mass Index and non-communicable diseases: nutritional adequacy of simulated weekly food plans.

BACKGROUND: The Mediterranean diet (MD), known to prevent obesity, overweight and the related non communicable diseases (NCD), is based on typical dishes, foods and on a common cultural milieu. Although MD is the basis of dietary guidelines, the prevalence of obesity, overweight and NCD, is increasing both in Western regions, and even more in Middle Eastern regions (MER). This study aimed to analyze (i) the impact of different levels of adherence to the MD, in Italy and MER, on body mass index (BMI) (ii) the bromatological composition of a simulated 7-days food plan (7-DFP) based on Italian or MER typical meals, following MD criteria and the Italian or MER food base dietary guideline; (iii) the optimization of nutrients impacting on NCD. METHODS: The 7-DFPs were implemented using a dietary software. The association between adherence to MD and BMI was evaluated by pooled estimated ORs (with 95% confidence intervals and p-values). Pooled measures were obtained by the methods appropriate for meta-analysis. The different food-based guidelines have been compared. RESULTS: The pooled ORs of obese status comparing medium vs. high adherence to MD were: 1.19 (95% C.I.: 0.99; 1.42, p-value = 0.062) and 1.12 (95% C.I.: 0.90; 1.38, p-value = 0.311) for MER and Italy respectively. For the comparison of low vs. high adherence, the pooled ORs were 1.05 (95% C.I.: 0.88; 1.24, p-value = 0.598) for MER, and 1.20 (95% C.I.: 1.02; 1.41, p-value = 0.031) for Italy when outliers are removed. High adherence to the MD resulted as potential protective factor against obesity. In MER 7-DFP: total fats is higher (34.5 E%) vs. Italian 7-DFP (29.4 E%); EPA (20 mg) and DHA (40 mg) are lower than recommended (200 mg each); sugars (12.6 E%) are higher than recommended (< 10 E%). Calcium, Zinc, and vitamin D do not reach target values in both 7-DFPs. CONCLUSION: This study highlights that, even when 7-DFPs follow MD and refer to nutrient needs, it is necessary to verify nutrient excesses or deficits impacting on NCD. High MD adherence is protective toward NCDs. MD principles, and energy balance should be communicated according to socioeconomic and educational levels.

Meta-analysis accelerator: a comprehensive tool for statistical data conversion in systematic reviews with meta-analysis.

BACKGROUND: Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools. IMPLEMENTATION: Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool's primary goal of simplifying the meta-analysis process. RESULTS: Since its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users. CONCLUSIONS: Meta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool's ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.

Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

The empirical estimate of the survival and variance using a weighted composite endpoint.

BACKGROUND: Composite endpoints for estimating treatment efficacy are routinely used in several therapeutic areas and have become complex in the number and types of component outcomes included. It is assumed that its components are of similar asperity and chronology between both treatment arms as well as uniform in magnitude of the treatment effect. However, these assumptions are rarely satisfied. Understanding this heterogeneity is important in developing a meaningful assessment of the treatment effect. METHODS: We developed the Weighted Composite Endpoint (WCE) method which uses weights derived from stakeholder values for each event type in the composite endpoint. The derivation for the product limit estimator and the variance of the estimate are presented. The method was then tested using data simulated from parameters based on a large cardiovascular trial. Variances from the estimated and traditional approach are compared through increasing sample size. RESULTS: The WCE method used all of the events through follow-up and generated a multiple recurrent event survival. The treatment effect was measured as the difference in mean survivals between two treatment arms and corresponding 95% confidence interval, providing a less conservative estimate of survival and variance, giving a higher survival with a narrower confidence interval compared to the traditional time-to-first-event analysis. CONCLUSIONS: The WCE method embraces the clinical texture of events types by incorporating stakeholder values as well as all events during follow-up. While the effective number of events is lower in the WCE analysis, the reduction in variance enhances the ability to detect a treatment effect in clinical trials.

Master of Science in Medical Statistics Programme at Universiti Sains Malaysia: 20 Years Ongoing.

Health and medical research are important parts of the curriculum of medical and health programmes in universities and play an important role in the functioning of organisations related to health care. There is a shortage of well-trained health and medical research statisticians. This article describes the courses and structure of the Master of Science in Medical Statistics programme at Universiti Sains Malaysia (USM), as well as the graduates' achievements. It is a 2-year programme that prepares qualified and competent graduates in statistical methods and data analysis for research in health and medical sciences. The Biostatistics and Research Methodology Unit, School of Medical Sciences, USM has been running the programme since 2003. It is currently the only medical statistics programme available in Malaysia. There have been 97 graduates since 2005, with an employment rate of 96.7% and a successful subsequent doctorate rate of 21.1%. Most of the students returned to their previous employments, mainly with the Ministry of Health of Malaysia and several others became lecturers, statisticians or research officers. The employability of graduates from this programme is very high and their professional future is bright. We hope our graduates will impart their knowledge and skills to the nation.

[THE STATUS OF THE SERVICE OF MEDICAL STATISTICS ACCORDING TO THE STAFF SURVEY RESULTS].

INRODUCTION: The Medical Statistics Service plays an important role in providing a medical organization with timely, high-quality statistical information. Therefore, it is important to study the opinions of specialists on the organization of this work. PURPOSE: To analyze the opinions of the staff of departments (offices) of medical statistics on the current work organization. MATERIAL AND METHODS: A survey was conducted among employees of the departments (offices) of medical statistics of the Moscow inpatient facilities. The survey results are presented as a frequency (in percent) of the choice of the answer options with a 95% confidence interval. Frequency confidence intervals were calculated using the EpiInfo 7 program. RESULTS: A total of 103 employees of the departments of medical statistics of the Moscow inpatient facilities took part in the survey. The majority of the respondents has a 20-year experience or more (29.1%). 61.3% of the heads of the departments of medical statistics, 70.4% of the doctors-statisticians and 74.2% of the medical statisticians stated a lack of a qualification category. The majority of the respondents were positive in their evaluation of the work organization of the medical statistics service (79.6%). The significance (importance) of the medical statistics service is unconditionally understood by 86.4% of the respondents. The respondents demonstrated a high degree of job satisfaction and work environment (10 scores out of 10). CONCLUSION: The study has identified a fairly positive attitude of the respondents towards their work, and the medical statistics service in general. However, the results obtained have also raised new questions, in particular, why specialists in this area do not have qualification categories, why they are not satisfied with the possibility of career growth, etc.

[The application of statistical programs and methods of data mathematical processing in obstetrics and gynecology].

In recent decades, evidence-based medicine acquired special importance in medicine. Therefore, proper presentation of data obtained in scientific research is extremely important. The statistical data processing, being an integral part of this process, often causes difficulties for researchers and its incorrect application results in distortion of results obtained. The purpose of the study is to comparatively analyze programs and methods of statistical data processing applied in dissertations on obstetrics and gynecology in 2011-2021, to examine trends in choosing them depending on specificity of research issue and to identify shortcomings erred by authors in choosing or describing data processing methods. The sampling for analysis included 258 abstracts of candidate's dissertations in the specialty "obstetrics and gynecology", defended in 2011-2021. The analysis covered the programs and methods of mathematical data processing. Over the past decade, significant complication of statistical processing of results of clinical trials in obstetrics and gynecology occurred in part of methods applied. The application of binary logistic regression and discriminant analysis increased most significantly over the past decade. Such sophisticated methods of statistical data processing as factor analysis, decision trees, ordinal logistic regression and neural networks began to be used too. The trend of gradual replacement of parametric methods (Student's t-test, one-way analysis of variance) by such corresponding non-parametric methods as Mann-Whitney test, Kruskal-Wallis test. The Microsoft Excel and Statistica were used most often for data processing. In recent years, the software SPSS Statistics is actively applied. However, problems in describing statistical methods used in dissertations continue to be present. In significant part of dissertations information about statistical program applied, methods of assessing of quantitative data distribution and criteria of significance of obtained results is absent. The proper application of statistical programs, methods of information processing, adequate interpretation of results as well as provision of complete information about methodological support are the key points to carry out modern research resulting in trusted attitude to scientific work and its results.

Learning from errors? The impact of erroneous example elaboration on learning outcomes of medical statistics in Chinese medical students.

BACKGROUND: Constructivism theory has suggested that constructing students' own meaning is essential to successful learning. The erroneous example can easily trigger learners' confusion and metacognition, which may "force" students to process the learning material and construct meaning deeply. However, some learners exhibit a low level of elaboration activity and spend little time on each example. Providing instructional scaffolding and elaboration training may be an efficient method for addressing this issue. The current study conducted a randomized controlled trial to examine the effectiveness of erroneous example elaboration training on learning outcomes and the mediating effects of metacognitive load for Chinese students in medical statistics during the COVID-19 pandemic. METHODS: Ninety-one third-year undergraduate medical students were randomly assigned to the training group (n = 47) and the control group (n = 44). Prerequisite course performance and learning motivation were collected as covariates. The mid-term exam and final exam were viewed as posttest and delayed-test to make sure the robustness of the training effect. The metacognitive load was measured as a mediating variable to explain the relationship between the training and academic performance. RESULTS: The training significantly improved both posttest and delayed-test performance compared with no training (Fposttest = 26.65, p < 0.001, Partial η2 = 0.23; Fdelayed test = 38.03, p < 0.001, Partial η2 = 0.30). The variation trend in metacognitive load in the two groups was significantly different (F = 2.24, p < 0.05, partial η2 = 0.20), but metacognitive load could not explain the positive association between the treatment and academic performance (ß = - 0.06, se = 0.24, 95% CI - 0.57 to 0.43). CONCLUSIONS: Erroneous example learning and metacognitive demonstrations are effective for academic performance in the domain of medical statistics, but their underlying mechanism merits further study.

[The statistics and public health in Russia (end of XIX - early XX centuries): foreign approaches in educational literature].

In Russia, since the end of XIX century the statistical method became the main method of cognition in public health (social hygiene). The article is devoted to the study of academic literature on theory of public health statistics in Russia and the USSR in the 1880s-1930s. The first experience in Russia of description of statistics in educational literature on hygiene is expounded in the lectures of F. F. Erisman (1887). In the 1920s, the German literature was used to compose Russian educational literature on social hygiene statistics. The English scientific tradition in statistics was reflected in the works of Yu. L. Pomorsky, compiled on the basis of the works of R. Fisher. In the 1920s and 1930s, complex mathematical and statistical methods were not claimed in social hygienic research in the USSR due to political attitudes.

The effect of formal statistical courses attitudes on learning outcomes in a cohort of undergraduate dental students.

INTRODUCTION: Whilst statistical knowledge is essential for dental students' academic or professional careers, only a few studies have measured the attitudes of these students towards statistics courses. This study aimed to investigate the attitudes of a cohort of dental students towards a formal statistical course and explore the factors that are potentially related to these attitudes. METHODS: A survey was performed amongst dental students of 2017 entry at Fujian Medical University, China. The questionnaire covers three aspects: demographic characteristics, educational background and attitudes towards formal statistics courses. RESULTS: A total of 103 dental students enrolled for the survey, and the response rate was 100.0%. 44.7% of dental students had positive attitudes towards formal statistics courses with an overall average of 25.7 (SD = 2.2, out of 30). Students' computer skills, expectations of course achievement, attention in class and learning atmosphere of the class were significantly associated with the attitudes towards formal statistics courses. Moreover, students with positive attitudes experienced a greater improvement in the statistical cognition and application ability of statistical methods after the course than those with negative/neutral attitudes. Additionally, a positive correlation was observed between students' attitudes and achievement in the examination. CONCLUSION: These results suggest that attitudes are critical to the learning effectiveness in formal statistics courses amongst undergraduate dental students. All the educators involved should monitor the students' attitudes in the teaching process and make effective interventions to improve students' attitudes towards formal statistics courses.

[The transformation of medical statistic during pandemic of new coronavirus infection (COVID-19)].

The pandemic of new coronavirus infection (COVID-19) directly effected medical statistics service. The amendments to the Federal Law "On the Official Statistical Accounting and the System of State Statistics in the Russian Federation" adopted in December 2020, regulated the provision of primary statistical data according forms of Federal and industrial statistical observation in the format of electronic document signed with electronic signature. This required the development of system of collecting and processing statistical data at the Federal level applying new technological solutions. The purpose of the study. To analyze the changes in the system of collecting and processing annual reporting on medical statistics for 2020 during the pandemic of new coronavirus infection COVID-19. The analysis was made concerning both normative legal base regulating implementation of information systems and system of receiving annual reports for current and previous years. Also content analysis was applied and materials distributed Internet were used. In conditions of new coronavirus infection (COVID-19) pandemic, instead of classical system of informational interaction at face-to-face coordination of annual report data, in extremely short terms new model of informational interaction of remote coordination and processing of annual report data was developed. The updated technological scheme was applied that included data transmission, remote coordination in VKS format, informing thriugh Telegram-channels and signing finalized forms with enhanced electronic digital signature. The complicated epidemiological situation regarding morbidity of new coronavirus infection (COVID-19) and as well as adoption of amendments in Federal legislation regarding provision of statistical observation forms in format of electronic document signed with electronic signature, required revision of format of statistical reports reception in 2020. The application of developed technology of collecting and processing annual reports data on medical statistics in online format permitted to dispense with both provision of hard copies versions of forms and reports and business trips of specialists from the subjects of the Russian Federation to Moscow that reduced expenses of the subjects of the Russian Federation when submitting annual reports. The developed mechanism of signing with unqualified electronic digital signature permitted to control data integrity. The applied mechanism for signing finalized concerted forms with enhanced electronic digital signature of public authority of the subject of the Russian Federation in the field of health protection ensured juridical significance of document provided. The establishment of base for further modernization of system of collecting statistical information from primary data, including subsequent implementation of structured electronic medical documents is proposed.

[The becoming and development of departmental medical statistic in Bavaria and Prussia (1800-1871)].

The article is devoted to the history of the emergence and functioning of departmental medical statistics in the German states in 1800-1871. The authors, on the basis of the analysis of German historiography, identify main actors, goals, methods of administration and representation of health statistics. Starting from the specifics of German terminology concerning the umbrella term "medical statistics" ("medizinische Statistik", "Medizinalstatistik", "sanitäre Statistik", "Gesundheitsstatistik"), the authors elaborate in detail on the cases of the Bavarian and Prussian kingdoms. By the beginning of the XIX century, the mechanisms of building modern state were started up in these countries. In its functioning, it was primarily based on statistical surveys of people and territories. The gradual institutionalization of medical statistics, its complexification and enhancement are considered in the context of state bureaucratic system reforming. The analysis of historical research results permitted to generate cumulative picture of becoming and development of medical statistics in Bavaria and Prussia. The study of transformation of reporting forms allowed to see how the bureaucratic institutions, by means of statistical methods, sought to rate the "death" and "health" of population.

MEPHAS: an interactive graphical user interface for medical and pharmaceutical statistical analysis with R and Shiny.

BACKGROUND: Even though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS. RESULTS: MEPHAS is a web-based GUI (https://alain003.phs.osaka-u.ac.jp/mephas/) that is based on a shiny framework. We also created the corresponding R package mephas (https://mephas.github.io/). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files. CONCLUSION: MEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results.

Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies.

OBJECTIVE: To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts. DESIGN: Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability). RESULTS: The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC. CONCLUSION: Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

Comparison of the efficacy of available statistical methods for prediction of the hospitalizations number: proof of concept and validation based on the analysis of Polish National Health Fund data in the years 2009-2017.

The aim of the study was to choose and validate the tool(s) to predict the number of hospitalized patients by testing three predictive algorithms: a linear regression model, Auto-Regressive Moving Average (ARMA) model, and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. The study used data from the collection of data on inflammatory bowel diseases (IBD) from the public database of the National Health Fund for the years 2009-2017, data recalculation taking into account the population of provinces and the country in particular years, and prediction making for the number of patients who would require hospitalization in 2017. The anticipated numbers were compared with real data and percentage prediction errors were calculated. Results of prediction for 2017 indicated the number of hospitalizations for Crohn's disease (CD) and ulcerative colitis (UC) at 17 and 16 respectively per 100,000 persons and 72 per 100,000 persons for all IBD cases. The actual outcomes were 21 for both CD and UC (81% and 75% accuracy of prediction, respectively), and 99 for all IBD cases (73% accuracy). The prediction results do not differ significantly from the actual outcome, this means that the prediction tool (in the form of a linear regression) actually gives good results. Our study showed that the newly developed tool may be used to predict with good enough accuracy the number of patients hospitalized due to IBD in order to organize appropriate therapeutic resources.

[The main directions of reforming the service of medical statistics in Ukraine].

OBJECTIVE: Introduction: Implementation of new methods of information support of managerial decision-making should ensure of the effective health system reform and create conditions for improving the quality of operational management, reasonable planning of medical care and increasing the efficiency of the use of system resources. Reforming of Medical Statistics Service of Ukraine should be considered only in the context of the reform of the entire health system. The aim: This work is an analysis of the current situation and justification of the main directions of reforming of Medical Statistics Service of Ukraine. PATIENTS AND METHODS: Material and methods: In the work is used a range of methods: content analysis, bibliosemantic, systematic approach. The information base of the research became: WHO strategic and program documents, data of the Medical Statistics Center of the Ministry of Health of Ukraine. RESULTS: Review: The Medical Statistics Service of Ukraine has a completed and effective structure, headed by the State Institution "Medical Statistics Center of the Ministry of Health of Ukraine." This institution reports on behalf of the Ministry of Health of Ukraine to the State Statistical Service of Ukraine, the WHO European Office and other international organizations. An analysis of the current situation showed that to achieve this goal it is necessary: to improve the system of statistical indicators for an adequate assessment of the performance of health institutions, including in the economic aspect; creation of a developed medical and statistical base of administrative territories; change of existing technologies for the formation of information resources; strengthening the material-technical base of the structural units of Medical Statistics Service; improvement of the system of training and retraining of personnel for the service of medical statistics; development of international cooperation in the field of methodology and practice of medical statistics, implementation of internationally accepted methods for collecting, processing, analyzing and disseminating medical and statistical information; the creation of a medical and statistical service that adapted to the specifics of market relations in health care, flexible and sensitive to changes in international methodologies and standards. CONCLUSION: Conclusions: The data of medical statistics are the basis for taking managerial decisions by managers at all levels of health care. Reform of Medical Statistics Service of Ukraine should be considered only in the context of the reform of the entire health system. The main directions of the reform of the medical statistics service in Ukraine are: the introduction of information technologies, the improvement of the training of personnel for the service, the improvement of material and technical equipment, the maximum reuse of the data obtained, which provides for the unification of primary data and a system of indicators. The most difficult area is the formation of information funds and the introduction of modern information technologies.

[Evidence-based medicine: opportunities of the Propensity Score Matching (PSM) method in eliminating selection bias in retrospective neurosurgical studies]. / Dokazatel'naia meditsina: vozmozhnost' ispol'zovaniia metoda podbora bol'nykh po indeksu sootvetstviia (PSM) dlia ustraneniia sistematicheskoi oshibki otbora v retrospektivnykh neirokhirurgicheskikh issledovaniiakh.

To date, a large amount of retrospectively collected data about treatment of neurosurgical pathology have been accumulated. Modern methods of medical statistics are necessary for correct interpretation of the data. The article purpose is to demonstrate application of one of the modern methods, Propensity Score Matching (PSM), in neurosurgery. The use of PSM avoids misinterpretation of retrospectively collected data and obviates errors in planning further prospective studies. For the past 10 years, the number of published international PSM-based studies has increased more than 10-fold, with the number of articles by Russian authors accounting for less than 0.2%. In line with the tendencies of international studies, application of PSM in analysis of retrospectively collected data will enable testing of a number of hypotheses and correct planning of prospective randomized studies.

[The Problems of Analysis of Medical Statistics Data].

The article presents the results of usability of medical statistics in complex automated analysis. It is demonstrated that processing of data of large volume is necessary both for stage of analysis and procedures of its preliminary processing. The article summarizes and classifies problems limiting quality of complex analysis of data of medical statistics. The algorithm is proposed including sequentially applied procedures supporting correct preparation of indices for further analysis. The algorithm was applied to 1.5 million of records of medical statistics collected in the Medial Informational Analytical Center of the Health Care Department of the Primorskiy Krai in 2004-2014.

Blended learning with Moodle in medical statistics: an assessment of knowledge, attitudes and practices relating to e-learning.

BACKGROUND: Blended learning that combines a modular object-oriented dynamic learning environment (Moodle) with face-to-face teaching was applied to a medical statistics course to improve learning outcomes and evaluate the impact factors of students' knowledge, attitudes and practices (KAP) relating to e-learning. METHODS: The same real-name questionnaire was administered before and after the intervention. The summed scores of every part (knowledge, attitude and practice) were calculated using the entropy method. A mixed linear model was fitted using the SAS PROC MIXED procedure to analyse the impact factors of KAP. RESULTS: Educational reform, self-perceived character, registered permanent residence and hours spent online per day were significant impact factors of e-learning knowledge. Introversion and middle type respondents' average scores were higher than those of extroversion type respondents. Regarding e-learning attitudes, educational reform, community number, Internet age and hours spent online per day had a significant impact. Specifically, participants whose Internet age was no greater than 6 years scored 7.00 points lower than those whose Internet age was greater than 10 years. Regarding e-learning behaviour, educational reform and parents' literacy had a significant impact, as the average score increased 10.05 points (P < 0.0001). CONCLUSIONS: This educational reform that combined Moodle with a traditional class achieved good results in terms of students' e-learning KAP. Additionally, this type of blended course can be implemented in many other curriculums.