This post originally appeared on Fast Data Science’s blog on LinkedIn.
Budgeting for clinical trials is crucial to ensure all study aspects are covered and adequately funded. The process involves detailed planning, considering the scope of the work, and addressing specific trial needs, such as personnel, procedures, and participant-related costs.
Personnel Costs:
Study Procedures:
Protocol-Related Fees:
Travel, Meetings, and Miscellaneous Costs:
Overall Budget: The Principal Investigator (PI) manages the entire budget for multi-site or single-site trials.
Site Budget: When the local PI negotiates the site-specific budget, including patient enrollment and closeout costs.
Define the Clinical Question:
Determine Per-Subject Costs:
Identify Ancillary Department Costs:
Estimate Personnel Costs:
Include Protocol-Related Fees:
Account for Hidden Costs:
Minimize Data Collection: Collect only necessary data points to reduce costs.
Don’t Underestimate Recruitment Time: Failing to recruit patients on schedule can increase costs as infrastructure expenses grow.
Pay for Specific Tasks: Instead of budgeting for full-time equivalents (FTE), pay for time spent on tasks.
Needs to be more accurate in the number of patients or the time required to complete the study.
Overlooking hidden costs, such as adverse events or additional patient monitoring.
Forgetting to budget for specific storage, audit, and protocol amendment fees.
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Creating a clinical trial budget involves careful planning, considering all possible costs, and aligning the budget with the trial’s scope. By focusing on essential tasks, minimizing unnecessary procedures, and allowing flexibility for unexpected costs, you can create a comprehensive and effective budget to keep your clinical trial on track.
References:
NIH Clinical Trial Budgeting https://www.nihstrokenet.org/
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction The success of a clinical trial is strongly dependent on the structure and coordination of the teams managing it. Given the high stakes and significant impact of every decision made during the trial, it is essential for each team member to collaborate efficiently in order to meet strict deadlines, comply with regulations, and ensure reliable results.
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Clinical trial protocols are detailed master-plans of a study – often 100–200 pages long – outlining objectives, design, procedures, eligibility and analysis. Reading them cover-to-cover can be daunting and time-consuming. Yet careful review is essential. Protocols are the “backbone” of good research, ensuring trials are safe for participants and scientifically valid [1]. Fortunately, there are systematic strategies to speed up review and keep it objective.
Introduction People have asked us often, how was the Clinical Trial Risk Tool trained? Does it just throw documents into ChatGPT? Or conversely, is it just an expert system, where we have painstakingly crafted keyword matching rules to look for important snippets of information in unstructured documents? Most of the tool is built using machine learning techniques. We either hand-annotated training data, or took training data from public sources. How We Trained the Models inside the Clinical Trial Risk Tool The different models inside the Clinical Trial Risk tool have been trained on real data, mostly taken from clinical trial repositories such as clinicaltrials.
