What is the role of biostatistics in medical research and public health?

The Role of Biostatistics in Medical Research and Public Health

Biostatistics provides essential principles for appropriate clinical trial design, interpretation of trial results, and effective use of screening, diagnostic, and prognostic tools in medical practice and public health. 1

Core Functions in Medical Research

Biostatistics serves as the quantitative foundation for translating medical information into actionable knowledge across multiple domains:

Clinical Trial Design and Analysis

• Biostatistics is critical for proper design of clinical trials, including determining appropriate sample sizes, randomization strategies, and analysis plans to evaluate both efficacy and safety endpoints. 1
• The field enables proper interpretation of trial results by accounting for Type I and Type II errors, understanding confidence intervals, and assessing the strength and generalizability of evidence from clinical trials. 1
• Meta-biostatisticians must be involved throughout the research process—not just during analysis—to ensure correct data structure, appropriate study designs, and valid conclusions. 1

Evidence Synthesis and Meta-Analysis

• Biostatistics provides the methodological framework for systematic reviews and meta-analyses, requiring expertise in pooling effect sizes, assessing heterogeneity, and managing publication bias. 1
• For diagnostic test accuracy questions, specialized statistical modeling is required because sensitivity and specificity are interdependent measures that cannot be analyzed separately. 1
• The meta-biostatistician must determine whether questions relate to practice interventions, diagnostic test accuracy, or prognosis, as each requires fundamentally different data structures and analytical approaches. 1

Essential Statistical Competencies for Medical Practice

Medical practitioners require specific biostatistical knowledge to interpret and apply research findings:

Descriptive Statistics and Study Design

• Understanding measures of central tendency (mean, median, mode) and dispersion (standard deviation, standard error, percentiles) for population distributions. 1
• Familiarity with experimental designs (randomized, non-randomized, non-inferiority trials) and observational designs (cohort, case-control, cross-sectional studies), including their inherent strengths and limitations. 1

Diagnostic Test Evaluation

• Competence in interpreting sensitivity, specificity, accuracy, and predictive values (positive and negative) is essential for evaluating screening and diagnostic tests. 1
• Understanding how threshold values affect test performance and how to apply these concepts in clinical decision-making. 1

Statistical Analyses and Risk Assessment

• Knowledge of common analytical methods including t-tests, chi-square tests, multiple regression, Kaplan-Meier survival analysis, and Cox proportional hazards analysis. 1
• Understanding concepts of relative and absolute risk, number needed to treat, number needed to harm, and population attributable risk. 1

Public Health Applications

Epidemiological Surveillance

• Clinical epidemiology, supported by biostatistical methods, provides crucial information for enumerating disease events, rates, trends, and outcomes in defined populations and their subgroups. 1
• Biostatistics enables identification of populations at different risk levels and detection of health disparities across demographic groups. 1
• Surveillance components provide early detection of emerging health threats and permit assessment of intervention effectiveness. 1

Cost-Effectiveness and Health Policy

• Modern epidemiologic studies increasingly incorporate quality metrics, economic endpoints, and modeling to inform clinical practice about cost-effectiveness and health impact of alternative preventive strategies. 1
• Biostatistical analysis supports evidence-based policy decisions by quantifying population burden of disease attributable to specific risk factors. 1

Critical Infrastructure Needs

Reproducibility and Standardization

• Version control, containerized code, workflow management software, and validation criteria against known standard datasets are essential for ensuring reproducible bioinformatics analyses in public health. 1
• Lack of standardization across bioinformatics pipelines affects data comparability across agencies, necessitating government-regulated standards for sequencing assays. 1

Collaborative Research Models

• Multidisciplinary teams including epidemiologists, clinicians, biostatisticians, and bioinformaticists are essential for advancing cancer outcomes research and improving clinical care. 1
• Academic health centers should establish centralized biostatistics units rather than dispersing resources across departments, as this approach better supports both investigators and biostatisticians' professional development. 2

Common Pitfalls and Quality Concerns

Misuse of Statistical Methods

• The explosion of scientific research has led to widespread misuse and abuse of statistical methods, requiring that common sense and clinical judgment always prevail over purely mathematical considerations. 3
• While sophisticated software makes statistical testing easy, investigators must understand biostatistics basics to choose appropriate tests and interpret results correctly. 4

Safety Endpoint Analysis

• When regulatory applications rely on early efficacy endpoints like progression-free survival, overall survival data remains essential for assessing safety, as improvements in early endpoints do not always predict treatment effects or potential harms. 1
• Prospective collection of overall survival data, establishment of fit-for-purpose definitions of detriment, and prespecified analysis plans are necessary to evaluate potential harm in clinical trials. 1

Data Interpretation Challenges

• Developing analytical tools that properly account for uncertainty in sampling processes and creating visualizations that convey uncertainty to both experts and non-experts are ongoing needs. 1
• Many public health practitioners lack genomic epidemiology backgrounds, requiring data exploration tools that effectively communicate complex findings to multidisciplinary teams. 1