clinical-research-guide

Clinical Research Guide

A skill for designing clinical studies and reporting results according to established guidelines. Covers randomized controlled trials (CONSORT), observational studies (STROBE), diagnostic studies (STARD), and systematic reviews (PRISMA).

Study Design Selection

Hierarchy of Evidence

Systematic Reviews / Meta-analyses
       |
Randomized Controlled Trials (RCTs)
       |
Cohort Studies (prospective)
       |
Case-Control Studies
       |
Cross-Sectional Studies
       |
Case Reports / Case Series
       |
Expert Opinion

Choose the design that best answers your research question
given ethical, practical, and resource constraints.

Design Decision Framework

def select_study_design(research_question: str,
                        can_randomize: bool,
                        outcome_prevalence: str,
                        time_constraint: str) -> dict:
    """
    Guide selection of clinical study design.

    Args:
        research_question: The clinical question
        can_randomize: Whether randomization is ethical and feasible
        outcome_prevalence: 'common' or 'rare'
        time_constraint: 'short', 'medium', or 'long'
    """
    if can_randomize:
        design = {
            "recommended": "Randomized Controlled Trial (RCT)",
            "reporting": "CONSORT 2010",
            "strengths": "Strongest causal inference",
            "considerations": [
                "Need equipoise (genuine uncertainty about which is better)",
                "Blinding may or may not be feasible",
                "Intent-to-treat analysis is the primary approach",
                "Pre-register at ClinicalTrials.gov or ISRCTN"
            ]
        }
    elif outcome_prevalence == "rare":
        design = {
            "recommended": "Case-Control Study",
            "reporting": "STROBE",
            "strengths": "Efficient for rare outcomes",
            "considerations": [
                "Select controls carefully (matching, population-based)",
                "Recall bias is a major threat",
                "Can only calculate odds ratios, not incidence"
            ]
        }
    elif time_constraint == "short":
        design = {
            "recommended": "Cross-Sectional Study",
            "reporting": "STROBE (cross-sectional extension)",
            "strengths": "Quick, inexpensive, good for prevalence",
            "considerations": [
                "Cannot establish temporal sequence",
                "Prevalence bias (overrepresents chronic conditions)",
                "Useful for hypothesis generation"
            ]
        }
    else:
        design = {
            "recommended": "Prospective Cohort Study",
            "reporting": "STROBE",
            "strengths": "Can establish temporal sequence, multiple outcomes",
            "considerations": [
                "Loss to follow-up is the main threat",
                "Confounding must be addressed analytically",
                "Expensive and time-consuming"
            ]
        }

    return design

CONSORT for Randomized Trials

Essential CONSORT Checklist Items

Title and Abstract:
  - Identify as randomized trial in the title
  - Structured abstract with trial design, methods, results, conclusions

Methods:
  - Trial design (parallel, crossover, factorial, etc.)
  - Participants: Eligibility criteria, settings, locations
  - Interventions: Precise details of interventions for each group
  - Outcomes: Primary and secondary, how and when assessed
  - Sample size: Calculation with assumptions stated
  - Randomization: Sequence generation, allocation concealment
  - Blinding: Who was blinded, how blinding was maintained

Results:
  - CONSORT flow diagram (enrollment, allocation, follow-up, analysis)
  - Baseline demographic table (Table 1)
  - Primary outcome with effect size and confidence interval
  - Harms and adverse events

Discussion:
  - Limitations including sources of potential bias
  - Generalizability
  - Interpretation consistent with results

CONSORT Flow Diagram

                    Assessed for eligibility (n=...)
                              |
                    Excluded (n=...)
                    - Not meeting criteria (n=...)
                    - Declined to participate (n=...)
                    - Other reasons (n=...)
                              |
                    Randomized (n=...)
                    /                    \
            Allocated to               Allocated to
            intervention (n=...)       control (n=...)
                    |                        |
            Lost to follow-up          Lost to follow-up
            (n=..., reasons)           (n=..., reasons)
                    |                        |
            Analyzed (n=...)           Analyzed (n=...)
            Excluded from analysis     Excluded from analysis
            (n=..., reasons)           (n=..., reasons)

STROBE for Observational Studies

Key STROBE Requirements

Study design specific items:

Cohort:
  - Report follow-up time (person-years, median)
  - Report loss to follow-up with reasons
  - Use hazard ratios or incidence rate ratios

Case-Control:
  - Describe case definition and case ascertainment
  - Describe control selection (source, matching criteria)
  - Report odds ratios with confidence intervals

Cross-Sectional:
  - Report response rate and non-response analysis
  - Describe how the sample represents the target population
  - Report prevalence with confidence intervals

Sample Size and Power

Power Calculation

def power_analysis_rct(effect_size: float, alpha: float = 0.05,
                       power: float = 0.80, ratio: float = 1.0) -> dict:
    """
    Calculate required sample size for a two-arm RCT.

    Args:
        effect_size: Expected Cohen's d
        alpha: Significance level (two-sided)
        power: Desired statistical power
        ratio: Allocation ratio (control:treatment)
    """
    from scipy import stats
    import math

    z_alpha = stats.norm.ppf(1 - alpha / 2)
    z_beta = stats.norm.ppf(power)

    n_per_arm = math.ceil(
        ((z_alpha + z_beta) ** 2 * (1 + 1 / ratio)) / effect_size ** 2
    )

    return {
        "n_per_arm": n_per_arm,
        "total_n": n_per_arm + math.ceil(n_per_arm * ratio),
        "parameters": {
            "effect_size": effect_size,
            "alpha": alpha,
            "power": power,
            "allocation_ratio": f"1:{ratio}"
        },
        "note": "Add 10-20% for anticipated dropout"
    }

Other Reporting Guidelines

All checklists are available at the EQUATOR Network (equator-network.org). Most journals require submission of the relevant checklist with your manuscript. Completing the checklist during manuscript writing, not after, ensures comprehensive reporting.