Applied Biostatistics and Clinical Data Analysis
From Clinical Datasets to Scientific Publications
An intensive, practice-based program using real microscopIA research datasets and JMP to develop practical clinical data analysis skills
From Clinical Datasets to Scientific Publications
From Clinical Datasets to Scientific Publications
Applied Biostatistics and Clinical Data Analysis
What participants will learn
From Clinical Datasets to Scientific Publications
Master clinical research data analysis through an intensive, hands-on program built around real datasets from completed or ongoing microscopIA research projects.
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Over 52 hours of live training, participants will use JMP to reproduce statistical workflows for retrospective studies, prospective investigations, and clinical trials. Each session introduces practical research problems, guiding participants through data preparation, statistical decision-making, hypothesis testing, regression, interpretation, and the generation of publication-ready tables and figures.
Rather than following a conventional lecture-based syllabus, the course develops analytical independence through supervised exercises, research challenges, and practical applications of statistical methods.
Learn to inspect clinical datasets, identify variable types, detect inconsistencies, manage missing values, explore distributions, define outcomes, and prepare data for statistical analysis using JMP.
Learn to choose statistical methods according to study design, variable characteristics, research objectives, distributional assumptions, and the structure of the available data. Apply descriptive statistics, hypothesis tests, effect estimation, confidence intervals, and nonparametric methods where appropriate.
Reproduce analyses involving observational clinical data, comparisons between patient groups, confounding, regression models, repeated measurements, and time-to-event outcomes. Interpret results in relation to study design and methodological limitations.
Work with trial-style datasets to compare interventions, define analytical populations, estimate treatment effects, interpret safety outcomes, and examine appropriate statistical approaches for randomized clinical investigations.
Generate descriptive and comparative tables, regression summaries, survival curves, and other figures relevant to clinical manuscripts. Learn to select meaningful results, report effect estimates and uncertainty, and communicate findings without overstating the evidence.
Integrate the complete process, from examining an unfamiliar dataset and defining an analysis strategy to executing statistical procedures, interpreting results, identifying limitations, and preparing the statistical components of a scientific manuscript.
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This is the space to describe the service and explain how customers or clients can benefit from it. It’s an opportunity to add a short description that includes relevant details, like pricing, duration, location and how to book the service.
This is the space to describe the service and explain how customers or clients can benefit from it. It’s an opportunity to add a short description that includes relevant details, like pricing, duration, location and how to book the service.
This is the space to describe the service and explain how customers or clients can benefit from it. It’s an opportunity to add a short description that includes relevant details, like pricing, duration, location and how to book the service.
This is the space to describe the service and explain how customers or clients can benefit from it. It’s an opportunity to add a short description that includes relevant details, like pricing, duration, location and how to book the service.
This is the space to describe the service and explain how customers or clients can benefit from it. It’s an opportunity to add a short description that includes relevant details, like pricing, duration, location and how to book the service.