Parallel-group randomized trial
What is the effect of assigning participants to one intervention rather than another?
When to use this project
Compare interventions by randomly assigning individual participants to parallel groups and following them over a common period. Define the intended treatment effect before analysis.
How it differs
Each participant follows one assigned strategy, unlike crossover. Allocation is at participant level, unlike cluster trials; randomization differs from the stated nonrandom assignment used in a nonrandomized study.
Decisions before you start
- Specify allocation concealment, random sequence generation and blinding as separate procedures.
- Define the estimand, including intercurrent events, and justify superiority, noninferiority or equivalence claims.
- Plan multiplicity, missing outcomes and analysis by assignment. Noninferiority margins need clinical and evidence-based justification.
How the flows work together
Each flow below links to its diagram, steps and tables. Flow order organizes the project; it does not imply that all work is strictly sequential. Data assessment, analysis planning and protocol refinement may inform one another.
1. Trial overview
Overview · Question through randomized trial reporting
Use this overview to state the purpose and link to detailed decisions. Keep a versioned protocol and decision register, and add evidence references for the rationale.
2. Recruitment and allocation
Selection · Track enrollment assignment and treatment
Random sequence generation, allocation concealment and blinding are separate protections. Concealment prevents knowledge of upcoming assignments; blinding concerns information after assignment. Track treatment receipt without replacing randomized assignment.
3. Endpoints and estimands
Design · Define objectives outcomes and effects
An estimand specifies the population, treatment conditions, outcome variable, handling of intercurrent events and population-level summary. An intercurrent event changes interpretation or existence of an outcome; it is not synonymous with a missing measurement.
4. Sample size and testing
Statistics · Specify scenarios hypotheses and multiplicity
Choose the claim before the calculation. Superiority, noninferiority and equivalence require different success rules; justify margins clinically and from evidence. Account for multiple claims and assumptions about missingness and event frequency.
5. Follow-up and safety
Conduct · Plan assessments monitoring and disposition
Stopping treatment need not end outcome follow-up. Distinguish adverse-event seriousness from severity, define ascertainment windows and keep clinical safety review separate from statistical efficacy monitoring.
6. Data and analysis populations
Data · Finalize data and derive populations
Analysis populations can overlap. Define assignment-based, safety and per-protocol populations without treating them as disjoint partitions. Excluding nonadherent participants can introduce selection and does not by itself estimate a causal per-protocol effect.
7. Statistical analysis plan
Statistics · Specify estimation missingness and sensitivity
Match the estimator to the estimand and randomization. Address missing outcomes separately from strategies for intercurrent events. Sensitivity analyses probe assumptions for the same estimand; supplementary analyses may target a different question.
Equations and statistical interpretation
These explanations describe notation and assumptions. Flow does not calculate these values.
Risk difference at a fixed horizon
xₐ is the event count among nₐ participants in assigned group a, with complete outcome ascertainment at the specified horizon.
The unadjusted difference is an illustrative estimator. Missing outcomes, censoring, covariate adjustment or clustering require compatible methods. Define the event direction before interpreting benefit.
Published example
SPRINT Research Group (2015), A Randomized Trial of Intensive versus Standard Blood-Pressure Control
Trace randomization, the primary outcome, follow-up and stopping decisions. Evaluate the findings in the enrolled population rather than treating the targets as universal clinical advice.
This publication illustrates a study design, not an endorsement of every method or a reproduction of its results. Follow its protocol, supplement and update history when studying the example.
Hypothetical scenario
A clinical team wants to compare two follow-up strategies for blood-pressure management.
- Trial overview: Specify the treatment question, approvals and prospective trial registration.
- Recruitment and allocation: Recruit and randomize with concealed allocation, tracking assigned and received treatment separately.
- Endpoints and estimands: Define the primary endpoint and estimand, including treatment changes and death.
- Sample size and testing: Justify sample size and testing strategy using a verified calculation and sensitivity scenarios.
- Follow-up and safety: Collect outcomes and adverse events consistently after assignment.
- Data and analysis populations: Finalize derivations and analysis populations without treating overlapping populations as disjoint counts.
- Statistical analysis plan: Estimate the planned effect and uncertainty, and assess sensitivity to missing outcomes and other assumptions.
Deliver a populated protocol, decision log, data specification, and planned outputs. Enter numerical findings only after analysis and verification. Keep decisions made before seeing results distinguishable from later amendments.
Sources
- CONSORT 2025: reporting randomized trials
- CONSORT 2025: explanation and elaboration
- SPIRIT 2025: randomized trial protocols
- ICH E9(R1): estimands and sensitivity analysis (2019)
- WHO: clinical trials and prospective registration
- WHO: primary registries in the trial registry network
- ISRCTN: what the clinical study registry records
- ClinicalTrials.gov: about the registry
Source check: 2026-09-17. Record the version of each reporting or appraisal tool used in your actual protocol.
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