Medical Statistics for FRCR Part 1: A Practical Guide to Passing the Stats Module

The Medical Statistics module of FRCR Part 1 consists of 40 questions in 2 hours. While many candidates find this module intimidating, it's actually one where structured preparation pays off significantly—the concepts are finite and testable.

Understanding statistics isn't just about passing exams. As a clinical oncologist, you'll interpret trial results, counsel patients on treatment options, and contribute to research. These skills begin with FRCR Part 1.

Types of Data

Qualitative (Categorical) Data

• Nominal: Categories with no order (e.g., blood type, tumour site)
• Ordinal: Categories with a meaningful order (e.g., tumour grade, performance status)

Quantitative (Numerical) Data

• Discrete: Countable values (e.g., number of metastases)
• Continuous: Measurable values on a scale (e.g., tumour size, PSA level)

Descriptive Statistics

Measures of Central Tendency

• Mean: Sum of values ÷ number of values (affected by outliers)
• Median: Middle value when data is ordered (robust to outliers)
• Mode: Most frequent value

Measures of Spread

• Range: Maximum – minimum
• Standard deviation: Average distance from the mean
• Interquartile range (IQR): Range of middle 50% of data

Hypothesis Testing and P-values

The null hypothesis (H₀) typically states there is no difference between groups. The p-value is the probability of obtaining results at least as extreme as observed, assuming H₀ is true.

• p < 0.05: Conventionally considered "statistically significant"
• p < 0.001: Highly significant
• p ≥ 0.05: Not statistically significant (but may still be clinically meaningful)

Important: A small p-value doesn't mean a large effect. Statistical significance ≠ clinical significance.

Common Statistical Tests

Data Type	Comparison	Test
Continuous, normal distribution	2 groups	Student's t-test
Continuous, normal distribution	3+ groups	ANOVA
Continuous, non-normal	2 groups	Mann-Whitney U test
Continuous, non-normal	3+ groups	Kruskal-Wallis test
Categorical	2 groups	Chi-squared test
Survival data	2+ groups	Log-rank test

Sensitivity, Specificity, and Predictive Values

These concepts are heavily tested in FRCR Part 1:

• Sensitivity: True positives ÷ (True positives + False negatives) = ability to detect disease
• Specificity: True negatives ÷ (True negatives + False positives) = ability to rule out disease
• Positive Predictive Value (PPV): True positives ÷ All positive results
• Negative Predictive Value (NPV): True negatives ÷ All negative results

Key point: PPV and NPV depend on disease prevalence. In low-prevalence populations, even tests with high sensitivity/specificity have low PPV.

Clinical Trial Design

Phases of Clinical Trials

• Phase I: Safety, dose-finding (small numbers, often dose escalation)
• Phase II: Efficacy signal, further safety (larger numbers)
• Phase III: Comparative effectiveness (randomised, large scale)
• Phase IV: Post-marketing surveillance

Key Trial Concepts

• Randomisation: Eliminates selection bias, balances confounders
• Blinding: Single (participant), double (participant + investigator), triple (+ analyst)
• Intention-to-treat: Analyse patients in their assigned groups regardless of compliance
• Per-protocol: Analyse only patients who completed treatment as planned

Survival Analysis

Kaplan-Meier Curves

The Kaplan-Meier method estimates survival probability over time, accounting for censored data (patients lost to follow-up or still alive at analysis).

• Curves show probability of surviving beyond time t
• Median survival = time when curve crosses 50%
• Log-rank test compares curves between groups

Hazard Ratio (HR)

The hazard ratio compares the rate of events between groups:

• HR = 1: No difference
• HR < 1: Treatment reduces risk
• HR > 1: Treatment increases risk

Confidence Intervals

A 95% confidence interval means: if we repeated the study many times, 95% of calculated intervals would contain the true population value.

For hazard ratios: If the 95% CI crosses 1.0, the result is not statistically significant.

Exam Tips

1. Practice interpreting Kaplan-Meier curves and forest plots
2. Know when to use parametric vs non-parametric tests
3. Understand the relationship between prevalence and predictive values
4. Learn the hierarchy of evidence (RCT > cohort > case-control > case series)
5. Be able to calculate NNT (number needed to treat) = 1/ARR

Practice with PassOncology

Our Medical Statistics module includes 200+ questions covering all these concepts with step-by-step explanations. Don't let statistics be your weak point—with the right practice, it can become your strongest module.

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