Webinar Playback
Selecting the Right Clinical Trial Design
And How to Maximise your Study Power
In this webinar, Ronan Fitzpatrick, Lead Statistician at nQuery, explores how to select the right clinical trial design and maximise study power by focusing on three critical areas:
Trial Objectives & Data Alignment
Avoiding Common Design Pitfalls
Using Tools to Streamline Design Selection
This guide provides a ground-up overview of choosing a clinical trial design which suits the objectives and characteristics of your clinical trial.
Clinical trials are evolving faster than ever in terms of clinical trial design in areas ranging from adaptive designs, analysis tools, to endpoint selection. This complexity can easily lead to sub-optimal clinical trial design choices, which means a sure footing in selecting the right design for your trial is more important than ever.
Frameworks for aligning trial design with study objectives and available data
Considerations for choosing between parallel, crossover, and adaptive designs
Using decision trees and structured evaluation to guide design choice
Identifying causes of inflated sample sizes and low statistical power
Understanding the impact of endpoint selection and population assumptions
Lessons from real-world examples of sub-optimal design choices
Strategies to optimise trial power without excessive sample inflation
Techniques for balancing design complexity with feasibility
Demonstration of nQuery tools for efficient power and sample size planning
Frequently Asked Questions About Clinical Trial Design
The design of a clinical trial determines everything from the efficiency of data collection to the validity of the conclusions. A poorly chosen design can result in underpowered studies, ethical issues, or even trial failure. With increasing complexity in therapies, endpoints, and regulations, getting the design right is more important than ever.
Design decisions—such as endpoint choice, analysis method, and stratification—directly affect power calculations and sample size. A suboptimal design may require unnecessarily large samples to reach significance or risk failing to detect a true effect. In contrast, an appropriate design optimizes statistical power without inflating resource use.
Frequent errors include:
Failing to align design with the primary research objective
Overlooking key sources of variability that influence outcome measures
Using fixed designs when adaptive alternatives might improve efficiency
Choosing inappropriate endpoints or analysis methods for the data
These oversights can lead to misleading results or missed opportunities.
Adaptive designs allow pre-planned modifications based on interim data, improving flexibility and resource use. Examples include sample size re-estimation, dropping ineffective arms, or adjusting randomization ratios. They help avoid wasted effort on underperforming treatments while preserving statistical integrity.
Choosing the right design starts with a clear understanding of your primary objective (e.g. efficacy, safety, non-inferiority) and the nature of your data. This involves:
Clarifying hypotheses and decision rules
Matching the endpoint with the analytical framework
Considering trial phase and regulatory context
Tools like the nQuery Table Select Overhaul and feature selection utilities can simplify this process by matching trial goals with appropriate design strategies.
Endpoints define how success is measured and should reflect meaningful clinical outcomes. Poorly chosen endpoints can make results irrelevant or uninterpretable. The right endpoint improves statistical efficiency and regulatory acceptance, and often determines the most suitable analysis method and sample size.
This tutorial offers a practical framework for aligning trial objectives with suitable designs. It reviews common pitfalls, showcases the new nQuery Table Select Overhaul, and includes real-world examples of how thoughtful design can improve power, efficiency, and trial success.
nQuery helps make your clinical trials faster, less costly and more successful. It is an end-to-end platform covering Frequentist, Bayesian, and Adaptive designs with 1000+ sample size procedures.
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