Natural language processing is becoming ever more important in research. The explosion in interest in LLMs is sparking many opportunities to analyse text data in healthcare, social sciences, and other areas.
We’re proud to announce that the Clinical Trial Risk Tool has been selected as a winner of the Plotly Dash Example Apps Challenge (2023), out of 25 amazing apps submitted by the community.
Funded by the Bill and Melinda Gates Foundation, this app uses natural language processing, Plotly Dash, and the spaCy and Scikit-Learn libraries to calculate the risk of a clinical trial failing to deliver informative results. It reads the trial protocol and identifies key features from the text which are fed into a risk model.
Join Plotly’s webinar to see the tool in action! https://tinyurl.com/4zd3emkr
You can try the new version of the app at: https://clinicaltrialrisk.org/tool
You can try version 1.0 of the app (using Plotly Dash) at: https://clinicaltrialrisk.org/tool/login?guest=true/.
Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Res 2023, 7:56 (https://doi.org/10.12688/gatesopenres.14416.1).
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.
