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 Multi-Arm & Multi-Stage (MAMS) Clinical Trials Design Tips The design of clinical trials is increasingly challenged by the Rising Costs, limited availability of eligible patient populations, and the growing demand for timely therapeutic evaluation. Traditional parallel-group designs, which typically compare a single intervention to a control, are often insufficient to meet these pressures in terms of speed, efficiency, and resource utilization.
You can use the t-test when you want to compare the means (averages) of continuous data between two groups, such as blood pressure or maximum concentration of a drug in urine (Cmax). If you have data with a dichotomous outcome, you can use the Chi-Squared test instead - please try our Chi-Squared sample size calculator. The calculator below will calculate the minimum sample size for you. Your expected effect size d is the standardised effect size according to Cohen’s definition.
You can use the Chi-Squared test to analyse your trial data or A/B test data if you have two groups with a dichotomous outcome. For example, you have two arms in your trial: the placebo and the intervention arm, and your endpoint is either yes or no, such as “did the subject experience an adverse event during the trial”. The calculator below will calculate the minimum sample size for you. Your expected effect size w is the standardised effect size according to Cohen’s definition.
