Outlier-response pattern checks to improve measurement with the Positive and Negative Syndrome Scale (PANSS)

Jonathan Rabinowitz; Alon A. Rabinowitz

doi:10.1016/j.psychres.2021.114114

Outlier-response pattern checks to improve measurement with the Positive and Negative Syndrome Scale (PANSS)

Jonathan Rabinowitz, Alon A. Rabinowitz

School of Social Work

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

We derived outlier-response pattern checks to flag possible careless PANSS (Positive and Negative Syndrome Scale) administrations based on analysis of 122,000 administrations from 29 registration trials of antipsychotics from NEWMEDS data repository. Flags identify outlier administrations based on frequency of endorsing a given response value, use of even or odd values, consecutive use of same value, variability of values, responses per specific item, and values on multiple items. Outlier flags were compared to published expert derived scoring inconsistency flags and tested in Monte Carlo simulated data, with known inconsistency, and appear to be useful at identifying administrations that require review.

Original language	English
Article number	114114
Journal	Psychiatry Research
Volume	303
DOIs	https://doi.org/10.1016/j.psychres.2021.114114
State	Published - Sep 2021

Bibliographical note

Funding Information:
Jonathan Rabinowitz has received research grant(s) support and/or travel support and/or speaker fees and/or consultant fees from Janssen (J&J), Eli Lilly, Pfizer, Lundbeck, Roche, Abraham Pharmaceuticals, Pierre Fabre, Intra-cellular Therapies, Minerva, Takeda and Amgen.

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

Data surveillance
PANNS flags
Reliability
Risk-based monitoring

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.psychres.2021.114114

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abstract = "We derived outlier-response pattern checks to flag possible careless PANSS (Positive and Negative Syndrome Scale) administrations based on analysis of 122,000 administrations from 29 registration trials of antipsychotics from NEWMEDS data repository. Flags identify outlier administrations based on frequency of endorsing a given response value, use of even or odd values, consecutive use of same value, variability of values, responses per specific item, and values on multiple items. Outlier flags were compared to published expert derived scoring inconsistency flags and tested in Monte Carlo simulated data, with known inconsistency, and appear to be useful at identifying administrations that require review.",

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Outlier-response pattern checks to improve measurement with the Positive and Negative Syndrome Scale (PANSS)

Abstract

Bibliographical note

Keywords

UN SDGs

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