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Comparative Study
. 2016 Jun 8;7(2):477-88.
doi: 10.4338/ACI-2015-12-RA-0178. eCollection 2016.

Visual assessment of the similarity between a patient and trial population: Is This Clinical Trial Applicable to My Patient?

Amos Cahan  1 , James J Cimino  2
Affiliations
Comparative Study

Visual assessment of the similarity between a patient and trial population: Is This Clinical Trial Applicable to My Patient?

Amos Cahan et al. Appl Clin Inform. .

Abstract

Background: A critical consideration when applying the results of a clinical trial to a particular patient is the degree of similarity of the patient to the trial population. However, similarity assessment rarely is practical in the clinical setting. Here, we explore means to support similarity assessment by clinicians.

Methods: A scale chart was developed to represent the distribution of reported clinical and demographic characteristics of clinical trial participant populations. Constructed for an individual patient, the scale chart shows the patient's similarity to the study populations in a graphical manner. A pilot test case was conducted using case vignettes assessed by clinicians. Two pairs of clinical trials were used, each addressing a similar clinical question. Scale charts were manually constructed for each simulated patient. Clinicians were asked to estimate the degree of similarity of each patient to the populations of a pair of trials. Assessors relied on either the scale chart, a summary table (aligning characteristics of 2 trial populations), or original trial reports. Assessment time and between-assessor agreement were compared. Population characteristics considered important by assessors were recorded.

Results: Six assessors evaluated 6 cases each. Using a visual scale chart, agreement between physicians was higher and the time required for similarity assessment was comparable.

Conclusion: We suggest that further research is warranted to explore visual tools facilitating the choice of the most applicable clinical trial to a specific patient. Automating patient and trial population characteristics extraction is key to support this effort.

Keywords: Data representation; clinical decision support systems; patient similarity; visual scale.

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Conflict of interest statement

Conflict of Interest

The authors declare that they have no conflict of interest in the research.

Figures

Fig. 1
Fig. 1
An example of a Scale Chart comparing a patient’s characteristics (red vertical line, grey boxes) to the distribution of reported population characteristics of two clinical trials (blue and orange horizontal bars). Each bar represents the mean or median and the standard deviation or interquartile range (IQR), as indicated, for continuous variables, and relative frequency for categorical variables. Horizontal bars are aligned to correspond to the patient’s characteristics. Different tones are used for different categories.
Fig. 2:
Fig. 2:
Numerical similarity-degree assessment-difference between each two clinicians assessing the same case vignette using the same assessment tool. Error bars represent ± 1 standard error of mean (upper panel); Overall degree of between-assessor agreement on the more similar trial of each pair of trials presented using the same assessment tool (lower panel).
Fig. 3
Fig. 3
Between-assessor agreement on trial population characteristics important for similarity assessment (dark and light bars represent the first and second pairs of trials, respectively). The majority of characteristics were not viewed as important by most assessing physicians.
See this image and copyright information in PMC

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