Binary Endpoint Sample Size: Response Rates, Risk Difference and What Can Go Wrong
Binary Endpoint Sample Size: Response Rates, Risk Difference and What Can Go Wrong. Practical guidance for clinical-trial statisticians, programmers...
Aomics · Insights
Clinical data science, written plainly.
Practical articles on adaptive clinical trials, biostatistics, RBQM, statistical programming and AI in clinical research — by Dr. Sakshi Bharti and the Aomics team.
CDISC, SDTM and ADaM in plain language: why standards matter to statisticians
Data standards can feel bureaucratic until you inherit ten vendors, inconsistent variable names and years of history. Standards reduce interpretation.
Sample Size Fundamentals: Turning an Effect Assumption Into a Trial
Sample Size Fundamentals: Turning an Effect Assumption Into a Trial. Practical guidance for clinical-trial statisticians, programmers and...
Statistical programming in clinical research: from raw data to analysis-ready evidence
Clinical programming is not just writing code for tables. It is controlled transformation of study data into traceable analysis datasets and outputs.
Confidence Intervals and Credible Intervals: Similar Shape, Different Meaning
Confidence Intervals and Credible Intervals: Similar Shape, Different Meaning. Practical guidance for clinical-trial statisticians, programmers and...
Type I error, confidence intervals and multiplicity: the inferential basics behind confirmatory trials
A p-value is one small piece of an inferential system. Clinical statisticians need error control, effect estimates, uncertainty intervals and multiplicity.
Hypothesis Testing Without the Jargon: Alpha, Power and p-Values
Hypothesis Testing Without the Jargon: Alpha, Power and p-Values. Practical guidance for clinical-trial statisticians, programmers and adaptive-design...
Sample size and power: why “more participants” is not a complete statistical strategy
Sample-size calculations are not just formulas. They are structured arguments about effect size, variability, event rates, dropout, errors and clinical.
Randomization and Blinding: Why They Matter More Than They Look
Randomization and Blinding: Why They Matter More Than They Look. Practical guidance for clinical-trial statisticians, programmers and adaptive-design...
Randomization, blinding and controls: the design choices that make causal claims possible
Statistics can quantify uncertainty, but it cannot manufacture a fair comparison after a badly designed experiment. Randomization, masking and controls.
Endpoints in Clinical Trials: Continuous, Binary, Count and Time-to-Event
Endpoints in Clinical Trials: Continuous, Binary, Count and Time-to-Event. Practical guidance for clinical-trial statisticians, programmers and...
Endpoints and estimands without the jargon: what exactly are we trying to estimate?
A primary endpoint is not automatically the same thing as the treatment effect you want to estimate. That distinction is central to modern clinical.