This post originally appeared on Fast Data Science’s blog on LinkedIn.
Clinical trials, the backbone of medical science advancement, often grapple with high costs, complexity, and lengthy timelines. Fast Data Science presents Fast Clinical AI, a game-changing solution that harnesses the power of Natural Language Processing (NLP) and predictive modelling to tackle these challenges head-on.
Fast Clinical AI automates the extraction of critical information from trial protocols, significantly reducing manual efforts. This tool identifies risk factors and predicts costs and enrolment criteria, ensuring efficient trial planning and execution.
By identifying potential risks early in the trial process, Fast Clinical AI allows researchers to take proactive measures, minimising the chances of trial delays or failures. This proactive approach ensures that trials are faster and more reliable.
Fast Clinical AI helps predict and manage the costs associated with clinical trials, ensuring better resource allocation. Reducing manual efforts and streamlining processes significantly reduces the time required to bring new treatments to market.
Explore how Fast Clinical AI can revolutionise your clinical trials. Visit here for more details. Learn more about clinical trial cost modelling with NLP and AI here. Connect with us today to see how we can support your clinical trial efforts. Let’s innovate together!
#ClinicalTrials #AI #NLP #HealthcareInnovation #FastClinicalAI
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.
