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. Cohen’s d is defined as the difference between the means of the two groups being compared, divided by the pooled standard deviation.
where Mi are the means of the two groups and Sp is the pooled standard deviation. You can use just the standardised Cohen's d value in this calculator, or alternatively you can enter the mean difference.You can estimate your expected effect size from the literature, such as past clinical trials.
Fast Data Science are pleased to announce that the Clinical Trial Risk Tool, has been accepted as a supplier on the UK Government’s G-Cloud 15 framework. The G-Cloud 15 framework allows public sector bodies to buy cloud-based computing services such as AI, hosting, software and support directly without lengthy, costly traditional tender processes. What does the Clinical Trial Risk Tool do? The Clinical Trial Risk Tool helps users to analyse clinical trial protocols and documents.
Estimating the total cost of a clinical trial before it runs is challenging. Public data on past trial costs can be hard to come by, as many companies guard this information carefully. Trials in high income countries and low and middle income countries have very different costs. Upload your clinical trial protocol and create a cost benchmark with AI Protocol to cost benchmark The Clinical Trial Risk Tool uses AI and Natural Language Processing (NLP) to estimate the cost of a trial using the information contained in the clinical trial protocol.
You can download a white paper about clinical trial cost benchmarking here Estimating the total cost of a clinical trial before it runs is challenging. Public data on past trial costs can be hard to come by, as many companies guard this information carefully. Trials in high income countries and low and middle income countries have very different costs. Clinical trial costs are not normally distributed.[1] I took a dataset of just over 10,000 US-funded trials.
