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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Statistical Software for Data Analysis and Clinical Trials01:12

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Clinical Trials01:16

Clinical Trials

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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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Related Experiment Video

Updated: Apr 5, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Data Science Solution to Event Prediction in Outsourced Clinical Trial Models.

Daniel Dalevi1, Susan Lovick2, Helen Mann2

  • 1Advanced Analytics Centre, Biometrics and Information Sciences, AstraZeneca, Sweden/UK.

Studies in Health Technology and Informatics
|August 12, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a standardized event prediction tool for clinical trials, enhancing trust and communication between sponsors and Contract Research Organizations (CROs). The dynamic web application improves reliability and transparency in statistical analysis.

Related Experiment Videos

Last Updated: Apr 5, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

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Area of Science:

  • Clinical research
  • Statistical modeling
  • Data science in healthcare

Background:

  • Late-phase clinical trials are frequently outsourced to Contract Research Organizations (CROs), with sponsors retaining ultimate risk and accountability.
  • Statistical tasks performed by CROs often require revalidation by in-house sponsor teams, creating potential inefficiencies and trust gaps.

Purpose of the Study:

  • To present a technological approach for standardized event prediction in clinical trials.
  • To introduce a dynamic web application built around an R-package designed to foster transparency and trust between CROs and in-house statisticians.
  • To demonstrate the benefits of standardization in improving communication and reliability for time-to-event prediction, particularly in oncology.

Main Methods:

  • Development of a dynamic web application integrated with an R-package for standardized event prediction.
  • Focus on key features including a short learning curve, interactivity, reproducibility, and data diagnostics.
  • Application motivated by time-to-event prediction in oncology clinical trials.

Main Results:

  • The developed tool offers reliable event predictions, simplifying communication and increasing trust through transparency.
  • Demonstrated clear benefits of standardization for both CROs and sponsor companies.
  • The application supports exploration, communication, sensitivity analysis, and standard report generation.

Conclusions:

  • Standardized event prediction tools can significantly enhance collaboration and efficiency in outsourced clinical trial statistics.
  • The presented web application provides a transparent and reproducible method for time-to-event predictions, particularly beneficial in oncology.
  • Adoption of such tools can lead to improved data integrity and streamlined workflows between research organizations.