clinical-trial-design-guide

Clinical Trial Design Guide

A skill for designing and analyzing clinical trials, covering study design selection, sample size calculation, randomization methods, interim analysis, survival endpoints, and regulatory considerations. Essential for pharmaceutical researchers, biostatisticians, and clinical scientists.

Clinical Trial Phases

Phase Overview

Phase	Objective	Typical N	Duration	Primary Endpoints
Phase I	Safety, dose-finding	20-80	Months	MTD, DLT, PK profile
Phase II	Efficacy signal, dosing	100-300	1-2 years	Response rate, biomarker
Phase III	Confirmatory efficacy	300-3,000+	2-4 years	OS, PFS, clinical outcome
Phase IV	Post-marketing surveillance	1,000+	Ongoing	Safety, real-world effectiveness

Study Design Selection

Common Designs

Parallel Group (most common Phase III):
  R --> Treatment A --> Outcome assessment
  R --> Treatment B --> Outcome assessment

Crossover:
  R --> Treatment A --> Washout --> Treatment B --> Outcome
  R --> Treatment B --> Washout --> Treatment A --> Outcome

Factorial (2x2):
  R --> Drug A + Drug B
  R --> Drug A + Placebo B
  R --> Placebo A + Drug B
  R --> Placebo A + Placebo B

Adaptive:
  Stage 1: Enroll n1 patients --> Interim analysis
  Stage 2: Modify design (dose, sample size, arm dropping) --> Continue

Design Selection Criteria

Factor	Recommended Design
Chronic disease, stable condition	Crossover (within-subject comparison)
Acute condition, one-time treatment	Parallel group
Multiple drugs to evaluate	Factorial or multi-arm
High uncertainty in effect size	Adaptive (sample size re-estimation)
Rare disease, limited patients	Bayesian adaptive, single-arm with historical control

Sample Size Calculation

Two-Sample Comparison of Means

from scipy.stats import norm
import numpy as np

def sample_size_two_means(delta: float, sigma: float,
                           alpha: float = 0.05, power: float = 0.80,
                           ratio: float = 1.0) -> dict:
    """
    Sample size for comparing two group means (two-sided test).
    delta: minimum clinically important difference
    sigma: pooled standard deviation
    alpha: type I error rate
    power: desired power (1 - beta)
    ratio: allocation ratio (n2/n1)
    """
    z_alpha = norm.ppf(1 - alpha / 2)
    z_beta = norm.ppf(power)
    effect = delta / sigma

    n1 = ((z_alpha + z_beta) ** 2 * (1 + 1 / ratio)) / effect ** 2
    n2 = ratio * n1

    return {
        "n_per_group_1": int(np.ceil(n1)),
        "n_per_group_2": int(np.ceil(n2)),
        "total": int(np.ceil(n1) + np.ceil(n2)),
        "effect_size": round(effect, 3),
    }

# Example: detect 5-point difference, SD=15, 80% power
result = sample_size_two_means(delta=5, sigma=15)
print(f"Required: {result['total']} total patients")