Types of Data in Biostatistics
A practical guide to types of data in biostatistics, including categorical, binary, nominal, ordinal, continuous, discrete, count, interval, ratio, time-to-event, longitudinal, and repeated-measures data, with clinical examples and statistical methods.
ANOVA for Comparing Multiple Treatment Groups
A practical guide to one-way ANOVA for comparing multiple treatment groups, including hypotheses, sums of squares, the F statistic, assumptions, a complete clinical-trial worked example, post hoc comparisons, effect sizes, R and SAS implementation, and common mistakes.
Zero-Inflated Models for Clinical Count Outcomes
A practical guide to zero-inflated Poisson and zero-inflated negative binomial models for clinical count outcomes, including structural zeros, excess zeros, model formulation, interpretation, likelihoods, model selection, R implementation, diagnostics, and a complete worked example.
Understanding the Normal Distribution in Biostatistics
A practical guide to the normal distribution in biostatistics, including the bell-shaped curve, mean and standard deviation, standardization and z-scores, empirical rules, percentiles, clinical examples, confidence intervals, and common mistakes.
Understanding P-Values in Clinical Research
A practical guide to understanding p-values in clinical research, including null hypotheses, statistical significance, confidence intervals, effect sizes, multiplicity, power, subgroup analyses, and clinical interpretation.
Types of Missingness: MCAR, MAR, and MNAR Explained
A practical guide to Missing Completely at Random (MCAR), including the formal definition, examples, diagnostics, implications for clinical trials, complete-case analysis, multiple imputation, likelihood-based methods, and how MCAR differs from MAR and MNAR.
Type I and Type II Errors in Hypothesis Testing
A practical guide to Type I and Type II errors in hypothesis testing, including false positives, false negatives, significance levels, power, sample size, clinical trial examples, and the relationship between alpha, beta, and statistical power.
Transitioning from Academia to Pharma Biostatistics
A practical guide for statisticians transitioning from academia to pharmaceutical biostatistics, covering clinical development, study teams, regulatory expectations, statistical programming, communication, interviews, CV positioning, and the skills needed to succeed in industry.
Tipping Point Analysis for Sensitivity to Missing Data
A practical guide to tipping point analysis for sensitivity to missing data in clinical trials, including missing-not-at-random assumptions, reference-based imputation, controlled pattern-mixture models, delta adjustments, treatment effects, and R implementation.
Time-to-First-Event vs. Recurrent Event Analysis
A practical guide to choosing between time-to-first-event and recurrent event analysis in clinical trials, including estimands, censoring, information loss, Andersen-Gill models, Prentice-Williams-Peterson models, frailty models, mean cumulative function, worked examples, and reporting considerations.