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Technical advance
Estimating the number of usability problems affecting medical devices: modelling the discovery matrix
Alexandre Caron
Univ. Lille, CHU Lille, EA2694, F-59000 Lille, France
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9872-0633
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View the published version at BMC Medical Research Methodology.
Background Usability testing of medical devices are mandatory for market access. The testings’ goal is to identify usability problems that could cause harm to the user or limit the device’s effectiveness. In practice, human factor engineers study participants under actual conditions of use and list the problems encountered. This results in a binary discovery matrix in which each row corresponds to a participant, and each column corresponds to a usability problem. One of the main challenges in usability testing is estimating the total number of problems, in order to assess the completeness of the discovery process. Today’s margin-based methods fit the column sums to a binomial model of problem detection. However, the discovery matrix actually observed is truncated because of undiscovered problems, which corresponds to fitting the marginal sums without the zeros. Margin-based methods fail to overcome the bias related to truncation of the matrix. The objective of the present study was to develop and test a matrix-based method for estimating the total number of usability problems.
Methods The matrix-based model was based on the full discovery matrix (including unobserved columns) and not solely on a summary of the data (e.g. the margins). This model also circumvents a drawback of margin-based methods by simultaneously estimating the model’s parameters and the total number of problems. Furthermore, the matrix-based method takes account of a heterogeneous probability of detection, which reflects a real-life setting.
Results We assessed the matrix-based method’s performance in a range of settings reflecting real-life usability testing and with heterogeneous probabilities of problem detection. In our simulations, the matrix-based method improved the estimation of the number of problems (in terms of bias, consistency, and coverage probability) in a wide range of settings. We also applied our method to five real datasets from usability testing.
Conclusions Estimation models (and particularly matrix-based models) are of value in estimating and monitoring the detection process during usability testing. Matrix-based models have a solid mathematical grounding and, with a view to facilitating the decision-making process for both regulators and device manufacturers, should be incorporated into current standards.
Keywords
usability testing, medical device, missing data, Bayesian statistics, maximum likelihood
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On 18 Sep, 2020
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Editorial decision: Minor revision
On 09 Jul, 2020
Submission checks complete
On 09 Jul, 2020
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On 08 Jul, 2020
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On 24 Jun, 2020
Review #3 received
Received 23 Jun, 2020
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On 21 Jun, 2020
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Received 19 Jun, 2020
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On 18 May, 2020
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On 20 Apr, 2020
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Editorial decision: Major revision
On 02 Mar, 2020
Review #3 received
Received 17 Feb, 2020
Reviewer #3 agreed
On 16 Feb, 2020
Review #1 received
Received 28 Jan, 2020
Reviewer #2 agreed
On 24 Jan, 2020
Review #2 received
Received 24 Jan, 2020
Reviewer #1 agreed
On 22 Jan, 2020
Reviewers invited
Invitations sent on 21 Jan, 2020
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This preprint is under consideration at BMC Medical Research Methodology. Preprints are preliminary reports that have not undergone peer review. They should not be considered conclusive, used to inform clinical practice, or referenced by the media as validated information.
Learn more about In Review
Technical advance
Estimating the number of usability problems affecting medical devices: modelling the discovery matrix
Alexandre Caron
Univ. Lille, CHU Lille, EA2694, F-59000 Lille, France
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9872-0633
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 4
Posted 05 Aug, 2020
Published
On 18 Sep, 2020
No community comments so far
Editorial decision: Accept
On 28 Jul, 2020
Submission checks complete
On 28 Jul, 2020
No comments provided
Editorial decision: Minor revision
On 09 Jul, 2020
Submission checks complete
On 09 Jul, 2020
Editor assigned
On 08 Jul, 2020
Editor invited
On 08 Jul, 2020
No comments provided
Editorial decision: Minor revision
On 24 Jun, 2020
Review #3 received
Received 23 Jun, 2020
Reviewer #3 agreed
On 21 Jun, 2020
Review #2 received
Received 19 Jun, 2020
Reviewer #2 agreed
On 18 May, 2020
Editor assigned
On 20 Apr, 2020
Reviewers invited
Invitations sent on 20 Apr, 2020
Reviewer #1 agreed
On 20 Apr, 2020
Review #1 received
Received 20 Apr, 2020
Editor invited
On 20 Apr, 2020
Submission checks complete
On 16 Apr, 2020
No comments provided
Editorial decision: Major revision
On 02 Mar, 2020
Review #3 received
Received 17 Feb, 2020
Reviewer #3 agreed
On 16 Feb, 2020
Review #1 received
Received 28 Jan, 2020
Reviewer #2 agreed
On 24 Jan, 2020
Review #2 received
Received 24 Jan, 2020
Reviewer #1 agreed
On 22 Jan, 2020
Reviewers invited
Invitations sent on 21 Jan, 2020
Submission checks complete
On 02 Nov, 2019
Editor assigned
On 29 Oct, 2019
Editor invited
On 29 Oct, 2019
First submitted
On 28 Oct, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
View the published version at BMC Medical Research Methodology.
Background Usability testing of medical devices are mandatory for market access. The testings’ goal is to identify usability problems that could cause harm to the user or limit the device’s effectiveness. In practice, human factor engineers study participants under actual conditions of use and list the problems encountered. This results in a binary discovery matrix in which each row corresponds to a participant, and each column corresponds to a usability problem. One of the main challenges in usability testing is estimating the total number of problems, in order to assess the completeness of the discovery process. Today’s margin-based methods fit the column sums to a binomial model of problem detection. However, the discovery matrix actually observed is truncated because of undiscovered problems, which corresponds to fitting the marginal sums without the zeros. Margin-based methods fail to overcome the bias related to truncation of the matrix. The objective of the present study was to develop and test a matrix-based method for estimating the total number of usability problems.
Methods The matrix-based model was based on the full discovery matrix (including unobserved columns) and not solely on a summary of the data (e.g. the margins). This model also circumvents a drawback of margin-based methods by simultaneously estimating the model’s parameters and the total number of problems. Furthermore, the matrix-based method takes account of a heterogeneous probability of detection, which reflects a real-life setting.
Results We assessed the matrix-based method’s performance in a range of settings reflecting real-life usability testing and with heterogeneous probabilities of problem detection. In our simulations, the matrix-based method improved the estimation of the number of problems (in terms of bias, consistency, and coverage probability) in a wide range of settings. We also applied our method to five real datasets from usability testing.
Conclusions Estimation models (and particularly matrix-based models) are of value in estimating and monitoring the detection process during usability testing. Matrix-based models have a solid mathematical grounding and, with a view to facilitating the decision-making process for both regulators and device manufacturers, should be incorporated into current standards.
Figure 1
Figure 2
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.