This post originally appeared on Fast Data Science’s blog on LinkedIn.
Clinical trials are the backbone of medical advancements, playing a crucial role in developing new treatments, improving patient outcomes, and ensuring that medical practices are based on solid evidence. As an expert in the Healthcare, Pharmaceutical, and Medical Industries, I’ve witnessed first-hand how clinical trials drive innovation and elevate the standard of care.
Development of New Treatments: Clinical trials are essential for the safe and effective development of new drugs and therapies. Without these trials, many life-saving treatments would not be available today.
Evidence-Based Practice: Research ensures that medical practices are grounded in robust evidence, leading to better patient outcomes. Healthcare providers rely on clinical trial data to make informed decisions about treatments.
Patient Safety and Efficacy: Clinical trials rigorously test new treatments, ensuring they are safe and effective before reaching the market. This process helps protect patients and fosters public trust in medical advancements.
Economic Impact: Investing in clinical research stimulates the economy by creating jobs and fostering innovation. Successful treatments also reduce long-term disease management costs.
Access to Cutting-Edge Treatments: Participants in clinical trials often gain access to new treatments before they are widely available, offering hope to those with conditions that do not respond to current therapies.
For a deeper dive into the importance of clinical trials, read our detailed article here.
Why Diversity in Clinical Trials Matter?
Diverse participation in clinical trials ensures that the findings apply to all population segments, leading to more effective and equitable healthcare solutions. By including diverse populations, we can understand how treatments affect different groups, reduce health disparities, enhance scientific validity, and adhere to ethical research practices. Tools like the Clinical Trial Risk Tool can help manage these trials effectively, ensuring inclusivity and representation.
You can learn more about the significance of diversity in clinical trials here.
Innovations like Fast Clinical AI are transforming clinical research. By leveraging Natural Language Processing (NLP) and predictive modelling, tools like Fast Clinical AI streamline data extraction, enhance risk management, and improve cost and time efficiency in clinical trials. These advancements help researchers overcome traditional challenges and accelerate the development of new treatments.
Explore how Fast Clinical AI can revolutionise your clinical trials here.
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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.
