{"schemaVersion":"1.0","exportedAt":"2026-05-15T12:40:10.568Z","occupation":{"soc":"15-2041.01","title":"Biostatisticians","group":"Computer & Mathematical","sector":"54","jobZone":5,"jobZoneInferred":false},"framework":{"version":"v.26.05","description":"","contextCovered":"This framework covers biostatistical practice across pharmaceutical clinical trials, academic medical research, epidemiological studies, and regulatory submissions, calibrated to Job Zone 5 advanced-degree practitioners.","levels":{"emerging":{"label":"Emerging","statements":["Descriptive statistics and summary tables — compute and interpret under faculty or senior biostatistician supervision for assigned clinical or survey datasets.","Statistical analysis software (SAS, R, or Python) — execute pre-written program code and verify output under guidance in a research computing environment.","Sample size calculations — apply standard formulas to estimate requirements for straightforward clinical study designs with direction from a senior statistician.","Longitudinal and cross-sectional data structures — distinguish and describe appropriate analytical approaches during structured research team meetings.","Graphs and tables for clinical data — prepare draft visualizations using spreadsheet or graphics software in accordance with sponsor or journal style guidelines.","Peer-reviewed biostatistics literature — read and summarize recent methodological articles to support team awareness of current analytical developments.","Statistical analysis plans (SAPs) — contribute assigned sections to draft documents under close review by a senior biostatistician.","Data quality and anomalies — identify and flag inconsistencies in clinical or survey datasets during routine data-cleaning tasks.","Research protocols — review assigned sections to extract analytical requirements under the direction of the lead study statistician.","Regulatory and ethical standards for data handling — follow established protocols for data privacy and integrity in a pharmaceutical or academic research setting."]},"developing":{"label":"Developing","statements":["Logistic regression and mixed-effects models — implement and interpret independently for moderately complex clinical or epidemiological datasets with periodic peer review.","Statistical program code — write, test, and document analysis scripts in R or SAS for assigned study objectives in a collaborative research environment.","Clinical study sample size requirements — calculate and justify estimates for standard Phase II or III trial designs, presenting assumptions to the research team.","Analysis plans and methods sections — draft complete, detailed SAPs for research protocols and revise based on feedback from principal investigators.","Predictive conclusions from model outputs — draw and communicate data-driven inferences to cross-functional teams including clinicians and life scientists.","Model-building and variable selection techniques — apply stepwise, LASSO, or information-criterion approaches for multivariable analyses in observational studies.","Tables and figures for study reports — produce publication-quality displays that comply with regulatory submission or manuscript formatting standards.","Professional conferences and methodology workshops — attend and synthesize emerging approaches in biostatistics and pharmacology for internal team knowledge sharing.","Collaborative study design — contribute statistical expertise to protocol development discussions with physicians and life scientists on multi-site research projects.","Version-controlled code repositories — manage analysis scripts using Git or equivalent file-versioning software within a shared research infrastructure."]},"proficient":{"label":"Proficient","statements":["Complex longitudinal and survival analyses — design, execute, and interpret independently across the full analytical scope of Phase I–IV clinical trials or large population-based studies.","Advanced model-building strategies — select and validate mixed-effect, Bayesian, or machine-learning approaches for non-routine biostatistical challenges in drug development or public health research.","Comprehensive statistical analysis plans — author end-to-end SAPs for multi-arm, adaptive, or complex observational study designs without supervisory review.","Sample size and power determinations for novel designs — derive requirements for adaptive, platform, or basket trial designs, accounting for multiple endpoints and interim analyses.","Quantitative predictions and regulatory conclusions — synthesize statistical evidence and articulate actionable findings for FDA submissions, IRB reports, or peer-reviewed publications.","Custom analytical software tools — develop and validate reproducible R packages, SAS macros, or Python modules that extend team analytical capabilities across multiple projects.","Data mining and integrated database queries — extract, link, and harmonize data from electronic health records, claims, or genomic databases to support research objectives.","Methodological literature — critically evaluate and adapt novel statistical methods from current publications for immediate application in ongoing research programs.","Cross-disciplinary study design — lead collaborative protocol development with clinicians, pharmacologists, and regulatory scientists to ensure statistical rigor from conception through dissemination.","Scientific writing and oral presentation — produce high-quality manuscripts, technical reports, and conference presentations that communicate complex biostatistical findings to diverse audiences."]},"advanced":{"label":"Advanced","statements":["Organizational biostatistics strategy — define methodological standards, tool selection, and analytical governance frameworks across an enterprise research portfolio or academic department.","Emerging quantitative methods — evaluate, pilot, and institutionalize novel approaches (e.g., estimands, real-world evidence frameworks, causal inference methods) to advance the organization's scientific capabilities.","Junior and mid-level biostatisticians — mentor, train, and evaluate staff through structured coaching, code review, and professional development planning in a large research organization.","Regulatory strategy and agency interactions — lead statistical discussions with FDA, EMA, or comparable bodies, representing the organization's analytical positions in pre-submission and advisory meetings.","Cross-functional research partnerships — establish and steward collaborative relationships with clinical development, epidemiology, bioinformatics, and commercial teams to align statistical methodology with business and scientific objectives.","Grant and contract proposals — provide strategic statistical leadership for large multi-site NIH, industry, or government funding applications, ensuring scientific rigor and competitive differentiation.","Institutional data infrastructure and systems — oversee design and evaluation of enterprise analytical platforms, database environments, and reproducible research pipelines at organizational scale.","Publication and dissemination programs — set quality and integrity standards for statistical content across all manuscripts, regulatory dossiers, and public-facing scientific communications.","Field-level thought leadership — represent the organization at national conferences, editorial boards, and professional societies, shaping methodological discourse in biostatistics and life sciences.","Ethical and compliance culture — champion principles of statistical integrity, transparency in reporting, and responsible data use across research teams and institutional governance structures."]}}},"sources":{"onet":"v30.2 (CC BY 4.0)","crosswalk":"https://skillscrosswalk.com","generator":"LER.me"},"attribution":"© EBSCOed"}