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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Blinding01:11

Blinding

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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Related Experiment Video

Updated: Jun 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Detecting irregularities in randomized controlled trials using machine learning.

Walter Nelson1,2, Jeremy Petch1,3,4,5, Jonathan Ranisau1

  • 1Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.

Clinical Trials (London, England)
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms can detect data irregularities in clinical trials as early as humans, improving efficiency. This automated approach aids central statistical monitoring in large multi-center trials.

Keywords:
Central statistical monitoringartificial intelligencedata qualitymachine learningoutlier detectionquality assurancerandomized controlled trials

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Area of Science:

  • Clinical trial data management
  • Biostatistics
  • Machine learning in healthcare

Background:

  • Clinical trials generate data that may contain irregularities, necessitating manual, resource-intensive central statistical monitoring.
  • Machine learning (ML) shows potential for automated detection of center-level irregularities in multi-center trials.

Purpose of the Study:

  • To characterize form-level data irregularities in historical clinical trials.
  • To evaluate an ML-based outlier detection algorithm's ability to identify these irregularities.

Main Methods:

  • Compared preliminary trial snapshots to final databases to identify human-ascertained irregularities.
  • Assessed ML algorithm performance using concordance, positive predictive value, and sensitivity.

Main Results:

  • Analyzed seven trials with 77,001 participants, identifying 24,850 form-wide irregularities.
  • The proposed ML algorithm achieved a median concordance of 0.74, outperforming a previous ML approach (median AUC 0.73).

Conclusions:

  • ML algorithms can detect form-level data irregularities as early or earlier than humans, without intervention.
  • This ML approach can complement existing central statistical monitoring, potentially enhancing efficiency in data verification for large trials.