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Related Concept Videos

Controls in Experiments01:13

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
Group Design02:01

Group Design

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 the two are due to...
Randomized Experiments01:13

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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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Experimental Designs01:16

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

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Related Experiment Video

Updated: May 19, 2026

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
06:45

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Published on: April 18, 2017

Are We in Control? How Best to Include a Control Group in Interrupted Time Series Designs: A Simulation Study.

Francesco Manca1, Daniel Mackay1, Jim Lewsey1

  • 1School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland.

Journal of Evaluation in Clinical Practice
|May 17, 2026
PubMed
Summary

This study compares statistical models for incorporating control groups in public health policy evaluations. Using time splines within an interrupted time series (ITS) of the difference between groups proved most robust, even when assumptions were violated.

Keywords:
autocorrelationcontrolled interrupted time seriesinterrupted time seriesparallel trendsimulation studiesspline

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Controlled interrupted time series (CITS) are vital for evaluating public health policies.
  • Limited research exists on statistically modeling control groups within CITS and segmented regression.

Purpose of the Study:

  • To compare the statistical performance of different segmented regression models for including control groups.
  • To assess model robustness under various assumption violations.

Main Methods:

  • Simulated and compared four segmented regression models using a real-world dataset.
  • Evaluated models under scenarios violating assumptions like non-parallel trends and autocorrelation.
  • Included models with restricted cubic splines for time to mitigate assumption violations.

Main Results:

  • Standard Difference-in-Difference (DiD) and CITS models showed low bias when assumptions were met.
  • Interrupted time series (ITS) of the difference between groups, with time splines, demonstrated the lowest bias and highest coverage, even with violated assumptions.
  • This approach is valuable for causal inference across diverse trend patterns.

Conclusions:

  • Modeling CITS as an ITS of the difference between series is a robust method for incorporating control groups.
  • Using time splines within this ITS framework reduces bias from assumption violations without compromising performance when assumptions hold.