Optimizing clinical trial design using prospective cohort study data: a case study in neuro-urology

Spinal Cord, 2021 · DOI: 10.1038/s41393-020-00588-z · Published: September 1, 2021

Spinal Cord Injury Urology Research Methodology & Design

Simple Explanation

This study uses data from a large group of spinal cord injury patients to improve the design of clinical trials. It helps researchers understand how many patients are likely to be eligible, how well the treatment groups are balanced, and what resources are needed. The researchers simulated different scenarios for a trial testing a nerve stimulation therapy for bladder control after spinal cord injury. This helped them anticipate challenges and make better decisions about how to run the trial. By using real-world data, the study shows how to make clinical trials more efficient and increase the chances of finding effective treatments for spinal cord injury patients.

Study Duration

3 years

Participants

640 patients with acute spinal cord injury

Evidence Level

Not specified

Key Findings

1
The recruitment target of 114 patients is obtainable within the originally envisioned three-year time period under the most favorable recruitment scenario examined.
2
The distribution of the primary prognostic factor produced imbalance in the randomization lists and informed further discussion of the cut-off values used in stratification.
3
Early discharge was related to the primary prognostic factor and study center, but is only anticipated in about 8% of participants.

Research Summary

This study demonstrates how prospective cohort data can be used to optimize the design of randomized controlled trials (RCTs), using a neuro-urology trial (TASCI) as a case study. Simulations based on data from 640 patients in the Swiss Spinal Cord Injury Cohort Study (SwiSCI) were used to assess the feasibility of recruitment targets, randomization schemes, and resource allocation. The study found that the recruitment target was achievable, but imbalances in prognostic factors and potential early discharges needed to be considered, informing adjustments to the trial design and resource planning.

Practical Implications

Improved Trial Feasibility

Using cohort data can help researchers accurately estimate recruitment rates and eligibility, reducing the risk of underpowered or prematurely terminated trials.

Optimized Resource Allocation

Understanding patient flow and potential operational challenges allows for better planning of personnel, equipment, and other resources.

Enhanced Trial Validity

Analyzing prognostic factors within cohort data enables researchers to refine randomization strategies and minimize imbalances between treatment groups.

Study Limitations

1
The study's findings are contingent on the cohort data accurately reflecting the target population.
2
Lack of urodynamic investigation data may limit the ability to fully investigate exclusion criteria.
3
The study could overestimate the number of available patients due to missing data.

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