Clinical Biostatistics

Statistical methods for clinical research: trial design, diagnostic accuracy, agreement studies, and the specifics of regulatory analysis

Clinical biostatistics applies statistical theory to the questions that matter in medicine: does a treatment work, does a test diagnose correctly, do two raters agree, and does an effect generalise. The methods overlap heavily with general statistics but are applied under constraints – ethics, regulation, small effective sample sizes, interim monitoring – that reshape how each analysis is designed and reported.

This area covers the core topics a biostatistician supporting clinical trials or diagnostic studies is expected to know, with emphasis on the reporting standards (CONSORT, STARD, TRIPOD, STROBE) that guide manuscript preparation.

Coverage

• Randomised controlled trial designs: parallel-group, crossover, cluster, stepped wedge
• Randomisation methods: simple, block, stratified, minimisation
• Intention-to-treat, per-protocol, and modified ITT analyses
• Interim analyses and group sequential designs (O’Brien-Fleming, Pocock, alpha spending)
• Adaptive designs: sample-size re-estimation, response-adaptive randomisation, Bayesian adaptive
• Non-inferiority and equivalence trials: margin selection, TOST, assay sensitivity
• Diagnostic test accuracy: sensitivity, specificity, PPV, NPV, likelihood ratios
• ROC analysis, AUC, and comparison of curves (DeLong test)
• Agreement studies: Cohen’s kappa, weighted kappa, Fleiss’ kappa, Krippendorff’s alpha
• Intraclass correlation coefficient for continuous ratings; Bland-Altman limits of agreement
• Sample size for diagnostic accuracy, agreement, and survival endpoints
• Reporting checklists and figures: CONSORT flow diagrams, STARD checklists

Packages: pROC, DTComPair, cutpointr, irr, blandr, gsDesign, rpact, gtsummary.

Tutorials

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Adaptive Trial Designs

Pre-specified modifications to trial conduct based on interim data

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Alpha-Spending Functions

Flexible interim alpha allocation via Lan-DeMets spending functions

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Baseline Adjustment with ANCOVA

Adjusting for baseline covariates to improve precision and address regression to the mean

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Bland-Altman Limits of Agreement

Graphical comparison of two measurement methods on the same subjects

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Blinding Procedures

Masking participants, investigators, assessors, and analysts

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Block Randomisation

Random permutations within fixed blocks for balanced allocation

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Clinical Equivalence Trials

Two one-sided tests (TOST) for bioequivalence and clinical equivalence

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Cluster-Randomised Trials

Randomisation of clusters (clinics, schools) rather than individuals

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Cohen’s Kappa

Chance-corrected agreement between two raters on categorical data

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Conditional Power

Probability of eventual trial success given interim data

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Crossover RCT Design

Within-subject comparison across treatment periods with washout

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Cutpoint Selection

Youden’s J, cost-based, and closest-to-(0,1) criteria for diagnostic thresholds

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Diagnostic Test Accuracy

Sensitivity, specificity, predictive values, likelihood ratios, and ROC analysis for binary diagnostic tests

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Factorial Trials

Simultaneously evaluating multiple interventions via a factorial design

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ITT vs Per-Protocol Analysis

Primary analyses under the intent-to-treat and per-protocol principles

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Interim Analyses and Group Sequential Designs

Pre-planned interim looks with alpha-spending and early-stopping boundaries

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Intraclass Correlation Coefficient (ICC)

Absolute agreement and consistency for continuous inter-rater reliability

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Likelihood Ratios

LR+ and LR- as prevalence-independent summaries of diagnostic performance

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Minimisation Algorithm

Covariate-adaptive allocation that prospectively minimises imbalance

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Missing Data in RCTs

MCAR, MAR, MNAR and their implications for primary analysis

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Multiple Imputation

Imputing multiple plausible values and combining via Rubin’s rules

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Non-Inferiority Margin Selection

Defining the clinically acceptable maximum inferiority

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O’Brien-Fleming Boundary

Conservative early stopping boundary in group-sequential trials

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Parallel-Group RCT Design

The two-arm randomised controlled trial: structure, analysis, and reporting

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Pocock Boundary

Constant nominal alpha across interim analyses

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Predictive Values and Prevalence

How disease prevalence determines PPV and NPV via Bayes’ theorem

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ROC Analysis

Receiver Operating Characteristic curves and area under the curve

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Randomisation Methods

Simple, block, and stratified randomisation for RCT allocation

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Reliability and Cronbach’s Alpha

Internal consistency of multi-item scales

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Sample Size Re-Estimation

Updating trial sample size mid-study using blinded or unblinded nuisance parameter estimates

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Sensitivity Analyses in Clinical Trials

Exploring robustness of conclusions to assumptions about missing data and model choice

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Stepped-Wedge Trial

Sequential rollout cluster design with all clusters eventually treated

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Stratified Randomisation

Separate randomisation lists within levels of baseline covariates

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Subgroup Analyses

Pre-specified subgroup effects and treatment-by-subgroup interaction tests

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Subgroup Forest Plots

Visual summary of subgroup-specific effects and interaction tests

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Weighted Kappa

Ordinal inter-rater agreement with linear or quadratic weights