BackgroundThere is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way.MethodsA checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007–2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument.ResultsThe scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%.ConclusionsA reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review.

PRISMA Checklist. (DOC)

File S1 includes Appendix S1, Appendix S2, Appendix S3, Appendix S4. Appendix S1: Search terms used to identify studies of one year mortality on antiretroviral therapy. Appendix S2: Full citations for studies reviewed. Appendix S3: Illustration of a distribution used to impute CD4 count with bands. Appendix S4: CD4 coefficient (bottom) and model fit (F-statistic – top) for the relationship between one year mortality on ART and baseline CD4 count using varying assumptions about the amount of mortality among those lost to follow-up. (DOCX)

Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets. Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning. Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools' increasing popularity can be attributed to features like sophisticated modeling and predictive analytics. A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential. Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software. Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques. Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this. Big Data Analytics's Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data. Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities. Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector. Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

Background and PurposeSpontaneous intracerebral hemorrhage (ICH) is a devastating form of stroke with a poor prognosis overall. We conducted a systematic review and meta-analysis to identify and describe factors associated with early neurologic deterioration (END) after ICH.MethodsWe sought to identify any factor which could be prognostic in the absence of an intervention. The Cochrane Library, EMBASE, the Global Health Library, and PubMed were searched for primary studies from the years 1966 to 2012 with no restrictions on language or study design. Studies of patients who received a surgical intervention or specific experimental therapies were excluded. END was defined as death, or worsening on a reliable outcome scale within seven days after onset.Results7,172 abstracts were reviewed, 1,579 full-text papers were obtained and screened. 14 studies were identified; including 2088 patients. Indices of ICH severity such as ICH volume (univariate combined OR per ml:1.37, 95%CI: 1.12–1.68), presence of intraventricular hemorrhage (2.95, 95%CI: 1.57–5.55), glucose concentration (per mmol/l: 2.14, 95%CI: 1.03–4.47), fibrinogen concentration (per g/l: 1.83, 95%CI: 1.03–3.25), and d-dimer concentration at hospital admission (per mg/l: 4.19, 95%CI: 1.88–9.34) were significantly associated with END after random-effects analyses. Whereas commonly described risk factors for ICH progression such as blood pressure, history of hypertension, and ICH growth were not.ConclusionsThis study summarizes the evidence to date on early ICH prognosis and highlights that the amount and distribution of the initial bleed at hospital admission may be the most important factors to consider when predicting early clinical outcomes.

Evidence-based medicine: assessment of

knowledge of basic epidemiological and research methods among medical doctors

Submitted to Venera ma'am by Roshan Shinde Group 32

EVIDENCE BASED MEDICINE is the main source of new knowledge for doctors in this era. The main objectives of EBM are as follows,

To evaluate the knowledge of basic research methods and data analysis among medical doctors. To assess factors such as the country of the medical school graduation profession.

Importance of Research Competence:

1. The study emphasizes that a solid understanding of epidemiology and biostatistics is essential for doctors to critically appraise medical literature and make informed clinical decisions.

2. Previous Findings: Prior studies indicated that many doctors lack proficiency in research methods, with significant gaps in understanding key concepts of evidence-based medicine (EBM).

Materials and Methods

Data collection and study population

The study involved 40 departments and employed around 500 doctors.

A random selection of 15 departments was made for participant recruitment.

Data collection

A supervised, self-administered questionnaire was distributed during morning staff meetings.

The questionnaire consisted of 10 multiple-choice questions focused on basic epidemiology and statistics, along with demographic data.

Participants were divided into two groups based on their country of medical school graduation: those from the former Soviet Union (Eastern education) and those from other countries (Western education).

The questionnaire was completed anonymously, and all participants were efficient in Hebrew.

Questionnaire

1. Sections of the Questionnaire:

Personal Details: This section collected demographic information about the doctors, including:

• Country of graduation

• Year of graduation from medical school

• Professional status (whether they are specialists or residents)

• Reading and writing habits related to medical literature.

Knowledge Assessment: This section consisted of 10 multiple-choice questions focused on basic research methods and statistics, divided as follows:

Statistics: 5 questions

Epidemiology: 5 questions

2. Basis for Statistical Questions:

The questions on statistics were derived from a list of commonly used statistical methods identified by Emerson and Colditz in 1983. This list was previously utilized for quality evaluations of articles published in the New England Journal of Medicine and referenced in a similar study by Horton and Switzee. This approach ensures that the questions are relevant and grounded in established research practices.

3. Scoring Methodology:

• Any missing answers to questions on epidemiological and statistical methods were considered incorrect. This scoring method emphasizes the importance of attempting to answer all questions and reflects a strict approach to assessing knowledge.

• The decision to mark unanswered questions as incorrect may encourage participants to engage more thoughtfully with the questionnaire, although it could also discourage some from attempting to answer if they are unsure

To ensure validity of the questionnaire, the 10 questions assessing knowledge were given to 15 members of the Epidemiology Department, Ben‐Gurion University. All of them correctly answered all the questions.

Results:

Response Rate: Out of 260 eligible doctors, 219 completed the questionnaire (84.2% response rate).

Statistical methods

1. Comparison of Categorical Variables:

Chi-Squared Test (x²): This test was used to examine differences between categorical variables. It assesses whether the observed frequencies in each category differ from what would be expected under the null hypothesis.

Fisher's Exact Test: This test was employed when sample sizes were small or when the assumptions of the chi-squared test were not met. It is particularly useful for 2×2 contingency tables.

2. Comparison of Ordinal Variables:

Mann-Whitney U Test: This non-parametric test was used to compare ordinal variables with multiple values, such as the scores obtained from the questionnaire. It assesses whether the distributions of two independent samples differ.

3. Paired Comparisons:

Wilcoxon's Signed Rank Test: This non-parametric test was used for paired comparisons of scores. It evaluates whether the median of the differences between paired observations is significantly different from zero.

4. Correlation Analysis:

Spearman's Rank Correlation Coefficient: This test was used to estimate the correlation between continuous variables. It assesses how well the relationship between two variables can be described using a monotonic function.

5. Multivariable Analysis:

Linear Regression: This method was used to explain the final score based on multiple variables. The analysis adjusted for all variables that were found to be related in the univariable analysis with a p-value of less than 0.1. This approach helps to identify the independent effects of each variable on the outcome.

6. Significance Level:

A p-value of 0.05 was considered statistically significant, indicating that there is less than a 5% probability that the observed results occurred by chance.

7. Data Presentation:

Normally distributed variables were expressed as mean (standard deviation, SD), while non-normally distributed variables were presented as median and interquartile range (IQR). This distinction is important for accurately representing the data's distribution.

Table 2 depicts doctors' professional characteristics according to the country of medical school graduation. Of 219 participants, 84 (38.4%) graduated from the former Soviet republics. The remaining 135 doctors were distributed by the country of graduation as follows: Israel, 100 (45.7%); West and Central Europe, 22 (10.0%); Italy, 8; Germany, 3; Czech Republic, 3; Hungary, 3; Netherlands, 1; Romania, 4; South America, 10 (4.6%); Argentina, 5; Cuba, 3; Uruguay, 1; Brazil, 1; and North America, 3 (1.4%).

Time Elapsed Since Graduation:

• Doctors from Israel and other countries had a shorter time since graduation compared to those from the former Soviet Union:

• Foreign Graduates: 8 years

(Interquartile Range (IQR) 4-19)

Former Soviet Union Graduates: 10 years (IQR 6-19)

• The difference was statistically significant (p = 0.02), indicating that foreign graduates tended to have graduated more recently.

Professional Status:

There were fewer specialists among foreign graduates compared to those who graduated from Israel

Foreign Graduates: 32.8% were specialists

Israeli Graduates: 48.0% were specialists

This difference was also statistically significant (p = 0.02).

Choice of Residency:

There were notable differences in the choice of residency between the two groups:

Domestic Graduates: 29.3% chose pediatrics or obstetrics and gynecology

Conclusion

The analysis of doctors' professional characteristics based on their country of medical school graduation reveals important insights into the diversity of medical training backgrounds and their implications for specialization and residency choices. These findings underscore the need for ongoing evaluation of medical education and training systems to ensure that all graduates, regardless of their background, are adequately prepared to meet the healthcare needs of the population

Table 3 describes the reading and publishing habits of the participants. A total of 96% of the participants reported reading at least one article per week, whereas 35.2% usually read at least three articles. Specialists read significantly more articles per week—52.3% of them read at least three articles, compared with only 23.8% of the residents; p<0.001. Most of the doctors, 63.6%, participated in the writing of ⩽5 articles. Similar to the reading pattern, only 21.1% of the residents wrote ⩾6 articles compared with 44.0% of the specialists; p<0.001. The Spearman correlation value between reading and writing variables was 0.35; p<0.001

Conclusion

The analysis of reading and publishing habits among the study participants reveals important insights into the professional engagement of doctors with medical literature. The differences between specialists and residents, along with the positive correlation between reading and writing, underscore the need for targeted educational initiatives to enhance research literacy and foster a culture of inquiry within the medical community. Encouraging both reading and writing can contribute to the overall quality of medical practice and the advancement of evidence-based medicine.

Figure 1

The figure describes the average of correct answers to 10 questions in understanding different aspects of basic research methods. Two populations of doctors are compared: those who graduated in the former Soviet Union (Eastern type of education) and those who graduated in Israel, USA, Western and Central Europe,

Objectives: Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting: Data taken from employees at 3 different industrial sites in Australia. Participants: 7915 observations were included. Materials and methods: The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results: CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the number of visits, and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured as compared to structured missingness. Discussion: Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusions: Researchers are encouraged to use CART and BRT models to explore and understand missing data.

Three files are included in this zip file.

List of the 28 matched indicators and the corresponding questions used in the community questionnaire, in comparison with those used in the DHS and MICS. (DOCX)

Basic statistical methods used in medical research by research goal and type of outcome variable.

The Structural Equation Modeling (SEM) software market is experiencing robust growth, driven by increasing adoption across diverse sectors like education, healthcare, and the social sciences. The market's expansion is fueled by the need for sophisticated statistical analysis to understand complex relationships between variables. Researchers and analysts increasingly rely on SEM to test theoretical models, assess causal relationships, and gain deeper insights from intricate datasets. While the specific market size for 2025 isn't provided, a reasonable estimate, considering the growth in data analytics and the increasing complexity of research questions, places the market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 8% seems plausible, reflecting steady but not explosive growth within a niche but essential software market. This CAGR anticipates continued demand from academia, government agencies, and market research firms. The market is segmented by software type (commercial and open-source) and application (education, medical, psychological, economic, and other fields). Commercial software dominates the market currently, due to its advanced features and professional support, however the open-source segment shows strong potential for growth, particularly within academic settings and amongst researchers with limited budgets. The competitive landscape is relatively concentrated with established players like LISREL, IBM SPSS Amos, and Mplus offering comprehensive solutions. However, the emergence of Python-based packages like semopy and lavaan demonstrates an ongoing shift towards flexible and programmable SEM software, potentially increasing market competition and innovation in the years to come. Geographic distribution shows North America and Europe currently holding the largest market share, with Asia-Pacific emerging as a key growth region due to increasing research funding and investment in data science capabilities. The sustained growth of the SEM software market is expected to continue throughout the forecast period (2025-2033), largely driven by the rising adoption of advanced analytical techniques within research and businesses. Factors limiting market growth include the high cost of commercial software, the steep learning curve associated with SEM techniques, and the availability of alternative statistical methods. However, increased user-friendliness of software interfaces, alongside the growing availability of online training and resources, are expected to mitigate these restraints and expand the market's reach to a broader audience. Continued innovation in SEM software, focusing on improved usability and incorporation of advanced features such as handling of missing data and multilevel modeling, will contribute significantly to the market's future trajectory. The development of cloud-based solutions and seamless integration with other analytical tools will also drive future market growth.

PRISMA checklist. PRISMA 2009 checklist of information to include when reporting a systematic review. (DOC)

Characteristics of baseline covariates and standardized bias before and after PS adjusted using weighting by the odds in 20% of the total respondents, a cross sectional study in five cities, china, 2007–2008 (n = 3,179). (PDF)

Search strategies. (DOC)

Positive agreement denotes agreement regarding acceptance, negative agreement refers to agreement regarding rejection of a manuscript.

*“grey literature” indicate unpublished studies, studies reported as meeting abstracts, book chapters, letters or studies published in non-English language journals.

Baseline characteristics of PROSPER cohorts by treatment allocation–full cohort & Scottish cohort. (DOCX)

A PRISMA checklist for this meta-analysis. (DOC)

1Probability (“risk”) of a manuscript in this group being published compared to the probability of a manuscript in both other groups.

BackgroundEffects of beta-blockers on the prognosis of the heart failure patients with preserved ejection fraction (HFpEF) remain controversial. The aim of this meta-analysis was to determine the impact of beta-blockers on mortality and hospitalization in the patients with HFpEF.MethodsA search of MEDLINE, EMBASE, and the Cochrane Library databases from 2005 to June 2013 was conducted. Clinical studies reporting outcomes of mortality and/or hospitalization for patients with HFpEF (EF ≥ 40%), being assigned to beta-blockers treatment and non-beta-blockers control group were included.ResultsA total of 12 clinical studies (2 randomized controlled trials and 10 observational studies) involving 21,206 HFpEF patients were included for this meta-analysis. The pooled analysis demonstrated that beta-blocker exposure was associated with a 9% reduction in relative risk for all-cause mortality in patients with HFpEF (95% CI: 0.87 – 0.95; P < 0.001). Whereas, the all-cause hospitalization, HF hospitalization and composite outcomes (mortality and hospitalization) were not affected by this treatment (P = 0.26, P = 0.97, and P = 0.88 respectively).ConclusionsThe beta-blockers treatment for the patients with HFpEF was associated with a lower risk of all-cause mortality, but not with a lower risk of hospitalization. These finding were mainly obtained from observational studies, and further investigations are needed to make an assertion.