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.

A dataset that is useful for training in applied biostatistics, in the context of biomedical research

Financial overview and grant giving statistics of Boston Biostatistics Research Fdn Inc.

The biostatistics software market is experiencing robust growth, driven by the increasing adoption of data-driven approaches in pharmaceutical research, clinical trials, and academic studies. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume of complex biological data necessitates sophisticated software solutions for analysis and interpretation. Secondly, advancements in machine learning and artificial intelligence are enhancing the capabilities of biostatistics software, enabling more accurate and efficient data processing. Thirdly, regulatory pressures demanding robust data analysis in the pharmaceutical and healthcare sectors are boosting demand for validated and compliant biostatistics tools. The market is segmented by software type (general-purpose versus specialized) and end-user (pharmaceutical companies, academic institutions, and others). Pharmaceutical companies represent a significant portion of the market due to their extensive reliance on clinical trial data analysis. However, the academic and research segments are also exhibiting strong growth due to increased research activities and funding. Geographically, North America and Europe currently dominate the market, but Asia-Pacific is expected to witness substantial growth in the coming years due to increasing healthcare spending and technological advancements in the region. The competitive landscape is characterized by a mix of established players offering comprehensive suites and specialized niche vendors. While leading players like IBM SPSS Statistics and Minitab enjoy significant market share based on their brand recognition and established user bases, smaller companies specializing in specific statistical methods or user interfaces are gaining traction by catering to niche demands. This competitive dynamic will likely drive innovation and further segmentation within the market, resulting in specialized software offerings tailored to particular research areas and user requirements. The challenges the market faces include the high cost of software licensing, the need for specialized training for effective utilization, and the potential integration complexities with existing data management systems. However, the overall growth trajectory remains positive, driven by the inherent need for sophisticated biostatistical analysis in various sectors.

The biostatistics software market is experiencing robust growth, driven by the increasing adoption of advanced analytical techniques in the pharmaceutical, healthcare, and research sectors. The market's expansion is fueled by the rising volume of complex biological data generated from genomic sequencing, clinical trials, and epidemiological studies. Researchers and analysts require sophisticated software to manage, analyze, and interpret this data effectively, leading to a surge in demand for biostatistics software solutions. Furthermore, the growing prevalence of chronic diseases and the increasing focus on personalized medicine are contributing factors. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is projected to continue as advancements in artificial intelligence (AI) and machine learning (ML) integrate into biostatistics software, enhancing its capabilities and attracting wider user adoption. The market is segmented into general biostatistics software and specialized biostatistics software. General software caters to a broader range of analytical needs, while specialized software targets specific applications within genomics, clinical trials, or epidemiology. The North American region currently holds the largest market share due to the presence of major pharmaceutical companies, well-funded research institutions, and advanced healthcare infrastructure. However, Asia-Pacific is expected to witness significant growth in the coming years driven by expanding healthcare budgets and increasing investments in research and development activities. Despite this overall positive outlook, regulatory hurdles and the high cost of software licenses pose challenges to market penetration, particularly in developing economies. The competitive landscape is relatively consolidated, with established players like IBM SPSS Statistics and Minitab facing competition from newer entrants offering specialized solutions and cloud-based platforms.

These are the tab and csv files from the Methods in Biostatistics with R Book

Financial overview and grant giving statistics of Biostatistics and Research Awareness Initiatives Network Inc.

Get key insights from Market Research Intellect's Biostatistics Consulting Service Market Report, valued at USD 5.2 billion in 2024, and forecast to grow to USD 9.1 billion by 2033, with a CAGR of 7.4% (2026-2033).

The biostatistics consulting service market offers a range of services, including:

Project Management: Managing the entire biostatistical process, from study design to data analysis and reporting. Data Management: Cleaning and preparing data for analysis, and managing data throughout the study lifecycle.

Financial overview and grant giving statistics of Cancer Research and Biostatistics

The biostatistical consulting services market is experiencing robust growth, driven by the increasing complexity of clinical trials and the rising demand for data-driven decision-making in the pharmaceutical and medical device industries. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $4.2 billion by 2033. This expansion is fueled by several key factors. Firstly, the burgeoning number of clinical trials across therapeutic areas necessitates advanced statistical analysis to ensure the rigor and validity of research findings. Secondly, the growing adoption of advanced analytical techniques, such as machine learning and artificial intelligence, is enhancing the efficiency and precision of biostatistical consulting services. Finally, the increasing outsourcing of biostatistical functions by pharmaceutical and biotech companies due to cost optimization strategies and access to specialized expertise further contributes to market growth. Significant segmentation exists within the market. Pharmaceutical companies constitute the largest segment among application types, followed by medical device companies and contract research organizations (CROs). In terms of service types, data management and delivery services command the largest market share, reflecting the massive volume of data generated in clinical trials. Regional analysis reveals a concentrated market presence in North America, driven by substantial investments in pharmaceutical research and development, followed by Europe and Asia-Pacific. However, emerging markets in Asia-Pacific are poised for significant growth, driven by increasing healthcare spending and a rising prevalence of chronic diseases. While the market faces certain restraints such as regulatory hurdles and the need for skilled professionals, the overall outlook remains positive, with considerable growth potential projected throughout the forecast period.

This dataset contains the quiz data and survey data for "Ethics in Clinical Research, E-Module Versus Traditional Online Lecture, a Randomized Study"

aWeight change z-score from birth to 6, 9 or 12 months in equation 1, 2 and 3 samples respectively.bGSCE = General Certificate of Secondary Education; A-level = Advanced level.cDichotomised Yes/No variable. Numbers are for Yes.dInfant BMI >91st centile and growth from birth to 2 years of age ≥1 centile band.Values are numbers (percentages) unless stated otherwise.

A three day statistics course written in Spanish for undergraduate biology students in Latin America

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)

This dataset provides comprehensive, subject-level records from simulated clinical trials, including patient demographics, treatment assignments, progress tracking, and outcome measures. It supports robust biostatistical analyses and machine learning applications for clinical research, safety monitoring, and predictive modeling of trial outcomes.

The dataset consists of 1763 observations, each representing a unique patient, and 12 different attributes associated with heart disease. This dataset is a critical resource for researchers focusing on predictive analytics in cardiovascular diseases.

Variables Overview: 1. Age: A continuous variable indicating the age of the patient. 2. Sex: A categorical variable with two levels ('Male', 'Female'), indicating the gender of the patient. 3. CP (Chest Pain type): A categorical variable describing the type of chest pain experienced by the patient, with categories such as 'Asymptomatic', 'Atypical Angina', 'Typical Angina', and 'Non-Angina'. 4. TRTBPS (Resting Blood Pressure): A continuous variable indicating the resting blood pressure (in mm Hg) on admission to the hospital. 5. Chol (Serum Cholesterol): A continuous variable measuring the serum cholesterol in mg/dl. 6. FBS (Fasting Blood Sugar): A binary variable where 1 represents fasting blood sugar > 120 mg/dl, and 0 otherwise. 7. Rest ECG (Resting Electrocardiographic Results): Categorizes the resting electrocardiographic results of the patient into 'Normal', 'ST Elevation', and other categories. 8. Thalachh (Maximum Heart Rate Achieved): A continuous variable indicating the maximum heart rate achieved by the patient. 9. Exng (Exercise Induced Angina): A binary variable where 1 indicates the presence of exercise-induced angina, and 0 otherwise. 10. Oldpeak (ST Depression Induced by Exercise Relative to Rest): A continuous variable indicating the ST depression induced by exercise relative to rest. 11. Slope (Slope of the Peak Exercise ST Segment): A categorical variable with levels such as 'Flat', 'Up Sloping', representing the slope of the peak exercise ST segment. 14. Target: A binary target variable indicating the presence (1) or absence (0) of heart disease.

Descriptive Statistics: The patients' age ranges from 29 to 77 years, with a mean age of approximately 54 years. The resting blood pressure spans from 94 to 200 mm Hg, and the average cholesterol level is about 246 mg/dl. The maximum heart rate achieved varies widely among patients, from 71 to 202 beats per minute.

Importance for Research: This dataset provides a comprehensive view of various factors that could potentially be linked to heart disease, making it an invaluable resource for developing predictive models. By analyzing relationships and patterns within these variables, researchers can identify key predictors of heart disease and enhance the accuracy of diagnostic tools. This could lead to better preventive measures and treatment strategies, ultimately improving patient outcomes in the realm of cardiovascular health

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Some characteristics could be estimated for all networks (186, published until 12/2012) whereas some other characteristics require outcome data and were estimated from 88 networks published until 3/2011 or their subsets. The exact number of networks evaluated in each case is given in square brackets. In parenthesis we present the interquartile range.

Overall alpha = .911; Scale Mean = 25.26; SD = 10.78.