The course consists of 22 practical sessions, identified in the booking calendar as Exercise 01 through Exercise 22.Each session introduces a clinical research problem and an approved microscopIA dataset or a dataset generated specifically for training.Participants progressively develop their statistical skills by applying the methods required to answer each research question. An exercise may integrate several statistical methods, and certain competencies may be revisited using different research designs.The sequence is flexible and may be adapted according to the research datasets and analytical challenges selected by the instructor.

Participants seeking certification must pass a supervised practical examination involving the independent, real-time analysis of an approved microscopIA clinical research dataset.The minimum passing score is 90%, and each participant is permitted one examination attempt.Three examination time slots will be offered to accommodate different schedules, from December 16-20, 2026.Successful participants will receive a microscopIA Certificate of Assessed Completion in Applied Biostatistics and Clinical Data Analysis.

The sessions may take place the following days based on your country. Check your city/region below for your local time and day.ContinentRegionReference citiesLocal daysLocal timeNorth AmericaWesternLos Angeles, VancouverTue & Thu5–7 p.m. / 4–6 p.m.*North AmericaEasternNew York, TorontoTue & Thu8–10 p.m. / 7–9 p.m.*Central AmericaCentralMexico City, Guatemala CityTue & Thu6–8 p.m.South AmericaAndeanBogotá, LimaTue & Thu7–9 p.m.South AmericaSouthernBuenos Aires, São PauloTue & Thu9–11 p.m.EuropeWesternLondon, LisbonWed & Fri1–3 a.m. / 12–2 a.m.*EuropeCentralParis, GenevaWed & Fri2–4 a.m. / 1–3 a.m.*AfricaWesternLagos, KinshasaWed & Fri1–3 a.m.AfricaEasternNairobi, Addis AbabaWed & Fri3–5 a.m.AsiaMiddle EastDubai, MuscatWed & Fri4–6 a.m.AsiaSouthernNew Delhi, ColomboWed & Fri5:30–7:30 a.m.AsiaEast & SoutheastSingapore, Manila, BeijingWed & Fri8–10 a.m.AsiaFar EastTokyo, SeoulWed & Fri9–11 a.m.OceaniaAustraliaSydney, MelbourneWed & Fri10 a.m.–12 p.m. on Oct 2; 11 a.m.–1 p.m. thereafterOceaniaNew ZealandAuckland, WellingtonWed & Fri1–3 p.m.AntarcticaResearch stationsMcMurdo, South PoleWed & Fri1–3 p.m.*For North America, the first time applies through October 29 and the second from November 3. For Europe, the first applies through October 23 and the second from October 28. Sydney and Melbourne change clocks on October 4. Antarctic times apply only to the research stations listed.

No. Live attendance is optional.Participants may attend live, study through recorded sessions, or combine both approaches according to their schedules.Certification is based on performance in the final practical examination rather than attendance.

Yes. All live sessions will be recorded and published on this page within 48 hours after each session.Recordings will be available to enrolled participants with an active Research & Career Elite Membership. Participants can revisit the exercises, reproduce the statistical analyses, and study at their own pace.

JMP will be the statistical software used throughout the program.Participants will learn to prepare clinical datasets, perform statistical analyses, evaluate analytical assumptions, interpret outputs, and generate tables and figures using JMP.Participants must have access to a suitable installation and license before completing the practical exercises.You can activate a free trial by clicking here.

Learn to evaluate unfamiliar clinical databases, identify variable types, define outcomes and exposures, detect inconsistencies, investigate missing values, and prepare analysis-ready datasets using JMP.Develop the ability to identify data-quality problems and determine whether a dataset is suitable for statistical analysis.

Learn to select appropriate descriptive statistics, examine distributions, summarize categorical and continuous variables, identify outliers, and calculate confidence intervals.Generate baseline-characteristics tables and exploratory figures suitable for retrospective studies, prospective cohorts, and clinical trials.

Develop the ability to choose statistical tests according to the research question, variable type, distribution, number of groups, and independence of observations.Apply parametric and nonparametric methods, including t tests, analysis of variance, chi-square tests, Fisher's exact tests, paired comparisons, and appropriate post-hoc procedures.Learn to evaluate statistical assumptions and distinguish statistical significance from clinical relevance.

Calculate and interpret mean differences, standardized effect sizes, confidence intervals, relative risks, absolute risk differences, odds ratios, and number needed to treat when appropriate.Explore sample-size estimation, statistical power, expected effect sizes, and the relationship between statistical precision and clinically meaningful findings.

Develop practical skills in linear, logistic, and other regression methods appropriate for continuous, binary, ordinal, and count outcomes.Learn to select predictors, investigate confounding, evaluate model assumptions, identify multicollinearity, assess model fit, interpret adjusted estimates, and examine interaction effects.Generate and interpret publication-ready regression tables.

Work with clinical research datasets to identify potential confounders, investigate associations, and select appropriate adjusted analyses.Explore multivariable regression, stratification, propensity-score matching, weighting, covariate balance, and sensitivity analyses.Learn to recognize selection bias, measurement bias, and the limitations of causal interpretation in observational research.

Analyze clinical outcomes such as mortality, remission, recurrence, retreatment, and hospitalization when follow-up time varies between patients.Apply Kaplan–Meier estimation, log-rank testing, Cox proportional hazards regression, and relevant model diagnostics.Learn to interpret hazard ratios, evaluate censoring, investigate proportional-hazards assumptions, and generate publication-ready survival curves.

Analyze prospective research datasets involving repeated observations, changes over time, and correlated measurements.Apply paired analyses, repeated-measures methods, and mixed-effects models. Explore generalized estimating equations when appropriate.Learn to account for within-patient correlation, evaluate treatment-by-time interactions, and interpret longitudinal trajectories.

Work with educational clinical-trial datasets to compare interventions, estimate treatment effects, evaluate primary and secondary outcomes, and investigate safety findings.Explore intention-to-treat and per-protocol analyses, adjusted comparisons, subgroup interactions, multiple testing, and sensitivity analyses.When relevant, examine statistical approaches for superiority, noninferiority, and equivalence trials.

Evaluate diagnostic and predictive performance using sensitivity, specificity, predictive values, likelihood ratios, receiver operating characteristic curves, and area under the curve.Learn to assess discrimination, calibration, diagnostic thresholds, and the limitations of clinical prediction models.

Investigate missing-data patterns, influential observations, outliers, and potential sources of analytical bias.Learn when complete-case analysis or imputation methods may be appropriate and how to evaluate alternative model specifications.Perform sensitivity analyses to determine how statistical decisions influence research findings.

Transform statistical outputs into clear scientific evidence suitable for biomedical manuscripts.Generate baseline-characteristics tables, comparative results tables, regression summaries, survival curves, forest plots, box plots, scatter plots, and other relevant visualizations.Learn to organize reproducible analytical workflows, document statistical decisions, write statistical methods, and communicate results without overstating conclusions.

Throughout the program, participants progressively integrate the analytical skills developed during previous exercises.Integrated research challenges involve examining an unfamiliar dataset, identifying an appropriate analytical strategy, preparing the data, selecting and executing statistical methods, evaluating assumptions, interpreting findings, and generating manuscript-oriented results.These challenges help participants prepare for the final practical certification examination.