Key Steps in the Design of Studies

From a statistical perspective, the most important steps in study design ensure that the data collected will be valid, reliable, reproducible and appropriately analyzed to answer the research question.

You will find below a breakdown of the key statistical steps in designing a study:

1. Define a Precise Research Question

• A vague question leads to poor choice of outcomes, variables, and analysis.
• A clear research question helps determine the type of comparison (e.g., mean difference, odds ratio, hazard ratio, etc.) and hypothesis tests needed.
• Also need to clearly define the study population – scope for statistical inference.

1a. Frameworks to Formulate Research Questions

PICO (Clinical & Interventional Research)

Common in clinical/biomedical studies.

• P: Population or Problem
• I: Intervention (or exposure)
• C: Comparator
• O: Outcome

🔹 Example:

In adults with Type 2 diabetes (P), does intermittent fasting (I), compared to calorie restriction (C), result in greater weight loss (O) after 3 months?

1b. PEO (Observational or Qualitative Research)

• P: Population
• E: Exposure
• O: Outcome

🔹 Example:

Among healthcare workers (P), does frequent exposure to COVID-19 patients (E) increase risk of anxiety symptoms (O)?

2. Specify the Primary Outcome(s)

• Determines what statistical test is used and what the study is powered to detect.
• Helps avoid multiplicity and controls Type I error (false positives).

3. Define the Study Population and Sampling Strategy

• Impacts generalizability and statistical validity.
• Affects assumptions such as independence and variance homogeneity. Important for avoiding selection bias and pseudoreplication.

4. Choose the Appropriate Study Design

(e.g., randomized controlled trial, cohort, cross-sectional)

• Different designs require different statistical methods.
• Determines assumptions, control for confounding, and need for repeated measures analysis.

5. Calculate the Required Sample Size & Power

• Avoids underpowered or unnecessarily large studies.
• Balances Type I (false positive) and Type II (false negative) error, and ensures estimation precision.

6. Plan for Randomization & Control of Confounding

• Ensures comparability between groups.
• Prevents confounding bias and supports valid causal inference.

7. Specify Statistical Analysis Plan (SAP)

• Pre-specify:
  • Tests and models to be used
  • Handling of missing data
  • Subgroup and sensitivity analyses
• Prevents data-driven (post hoc) analyses and p-hacking.

8. Ensure Reliable Measurement & Data Quality

• Define how variables will be measured
• Use validated tools
• Plan for blinding if relevant
• Poor measurement increases noise, reduces power, and biases estimates.

9. Address Issues of Multiplicity

• Especially if there are multiple outcomes, time points, or subgroups.
• Use correction methods (e.g., Bonferroni, FDR) or hierarchical analysis plans.

10. Plan for Data Monitoring & Interim Analyses (if needed)

• In clinical trials or long studies.
• Requires adjustment of significance thresholds (e.g., using group sequential methods).

Summary Table