Related Experiment Video
Updated: Sep 4, 2025
20:24
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
16.6K
Methods to Summarize Discrete-Choice Experiments in a Systematic Review: A Scoping Review
Daksh Choudhary1, Megan Thomas2, Kevin Pacheco-Barrios3,4
1Department of Medicine, University of Calgary, Calgary, AB, Canada.
The Patient
|July 13, 2022
Summary
Systematic reviews increasingly use discrete-choice experiments (DCEs). This review identified methods for summarizing DCE results, finding narrative approaches common but semi-quantitative and quantitative methods also exist for synthesizing patient preferences.
Area of Science:
- Health economics
- Health services research
- Patient-reported outcomes
Background:
- Systematic reviews of discrete-choice experiments (DCEs) are growing in frequency.
- Methodologies for synthesizing results across multiple DCEs are not well-established.
Purpose of the Study:
- To identify and describe methods used to summarize findings across DCEs.
- To compare different approaches for synthesizing DCE results.
Main Methods:
- Scoping review of systematic reviews of patient preferences using DCEs.
- Searched MEDLINE and EMBASE databases up to March 18, 2021.
- Classified synthesis methods into narrative, semi-quantitative, and quantitative (meta-analytic) approaches.
Main Results:
- Identified 54 eligible systematic reviews from 2010-2021 across diverse health conditions.
- Most reviews (83%) used narrative synthesis; few (6%) systematically addressed preference heterogeneity.
- Semi-quantitative methods included attribute importance frequency; one review used meta-analysis.
Conclusions:
- Semi-quantitative and quantitative methods for synthesizing DCE results are available but not widely used.
- Further research is needed to evaluate the validity and utility of these synthesis approaches.
Related Concept Videos
01:16Experimental Designs
11.9K
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...
11.9K
01:13Randomized Experiments
7.1K
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
Simple...
Simple randomization
Simple...
7.1K
01:16Crossover Experiments
2.9K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
2.9K
02:01Group Design
9.5K
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...
9.5K
02:01Factorial Design
13.3K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.3K
01:17Cochran's Q Test
530
Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square...
530