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Technical advance
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 article 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 10 Jul, 2020
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Received 24 Jun, 2020
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Received 20 Jun, 2020
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On 19 May, 2020
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On 21 Apr, 2020
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On 21 Apr, 2020
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Received 21 Apr, 2020
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On 20 Apr, 2020
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On 16 Apr, 2020
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Editorial decision: Major revision
On 03 Mar, 2020
Review #3 received
Received 18 Feb, 2020
Reviewer #3 agreed
On 17 Feb, 2020
Review #1 received
Received 29 Jan, 2020
Reviewer #2 agreed
On 25 Jan, 2020
Review #2 received
Received 25 Jan, 2020
Reviewer #1 agreed
On 23 Jan, 2020
Reviewers invited
Invitations sent on 22 Jan, 2020
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On 30 Oct, 2019
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This preprint is under consideration at BMC Medical Research Methodology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Technical advance
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 29 Jul, 2020
Submission checks complete
On 28 Jul, 2020
No comments provided
Editorial decision: Minor revision
On 10 Jul, 2020
Submission checks complete
On 09 Jul, 2020
Editor assigned
On 09 Jul, 2020
Editor invited
On 08 Jul, 2020
No comments provided
Editorial decision: Minor revision
On 25 Jun, 2020
Review #3 received
Received 24 Jun, 2020
Reviewer #3 agreed
On 22 Jun, 2020
Review #2 received
Received 20 Jun, 2020
Reviewer #2 agreed
On 19 May, 2020
Editor assigned
On 21 Apr, 2020
Reviewers invited
Invitations sent on 21 Apr, 2020
Reviewer #1 agreed
On 21 Apr, 2020
Review #1 received
Received 21 Apr, 2020
Editor invited
On 20 Apr, 2020
Submission checks complete
On 16 Apr, 2020
No comments provided
Editorial decision: Major revision
On 03 Mar, 2020
Review #3 received
Received 18 Feb, 2020
Reviewer #3 agreed
On 17 Feb, 2020
Review #1 received
Received 29 Jan, 2020
Reviewer #2 agreed
On 25 Jan, 2020
Review #2 received
Received 25 Jan, 2020
Reviewer #1 agreed
On 23 Jan, 2020
Reviewers invited
Invitations sent on 22 Jan, 2020
Submission checks complete
On 03 Nov, 2019
Editor assigned
On 30 Oct, 2019
First submitted
On 29 Oct, 2019
Editor invited
On 29 Oct, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article 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
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This preprint is available for download as a PDF.
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