Does missing data matter in the revised Patients' Attitudes Towards Deprescribing questionnaire? A systematic review and two case analyses
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved..
BACKGROUND: The revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire was developed to capture beliefs and perceptions of patients about deprescribing. In general, handling of missing data is underreported in survey studies. Underlying mechanisms related to missing data may impact the findings from survey studies.
OBJECTIVES: The aim of this study was to assess the missing data in studies using the rPATD questionnaire through a systematic review and datasets from two studies.
METHODS: First, this review updated a systematic review on the rPATD (and other versions). We searched Medline via OVID, EMBASE, Scopus, Web of Science until 31st January 2023. Missing data reporting and methods to handle them were collected. Second, data from two deprescribing studies were analyzed using three methods of missing data handling: complete case analysis, personal mean substitution, and multiple imputation. We compared the scores from each domain and the associations of the domains with two questions from the rPATD to highlight how using different methods can influence the interpretation of study findings.
RESULTS: We identified 49 studies: 31 (63 %) from this study and 18 (37 %) from the original systematic review. The question or domain with the most missing data could be identified in 9 studies (18.4 %). Missing data management was reported in 19 studies (38.8 %). In one case analysis, the "Burden" domain was significantly associated with the question "I would like to try stopping one of my medicines to see how I feel without it" using complete case analysis (p = 0.044) or multiple imputation (p = 0.038), but not when using personal mean substitution (p = 0.057).
CONCLUSIONS: Missing data and methods used to handle missing data were underreported in studies using the rPATD questionnaire. The methods should be chosen carefully as our analyses from two distinct studies suggest that they may impact the interpretation of the findings from the questionnaire.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:20 |
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| Enthalten in: |
Research in social & administrative pharmacy : RSAP - 20(2024), 3 vom: 01. Feb., Seite 296-307 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nguyen-Soenen, Jérôme [VerfasserIn] |
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| Links: |
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| Themen: |
Deprescribing |
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| Anmerkungen: |
Date Completed 19.02.2024 Date Revised 19.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.sapharm.2023.12.010 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: The revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire was developed to capture beliefs and perceptions of patients about deprescribing. In general, handling of missing data is underreported in survey studies. Underlying mechanisms related to missing data may impact the findings from survey studies | ||
| 520 | |a OBJECTIVES: The aim of this study was to assess the missing data in studies using the rPATD questionnaire through a systematic review and datasets from two studies | ||
| 520 | |a METHODS: First, this review updated a systematic review on the rPATD (and other versions). We searched Medline via OVID, EMBASE, Scopus, Web of Science until 31st January 2023. Missing data reporting and methods to handle them were collected. Second, data from two deprescribing studies were analyzed using three methods of missing data handling: complete case analysis, personal mean substitution, and multiple imputation. We compared the scores from each domain and the associations of the domains with two questions from the rPATD to highlight how using different methods can influence the interpretation of study findings | ||
| 520 | |a RESULTS: We identified 49 studies: 31 (63 %) from this study and 18 (37 %) from the original systematic review. The question or domain with the most missing data could be identified in 9 studies (18.4 %). Missing data management was reported in 19 studies (38.8 %). In one case analysis, the "Burden" domain was significantly associated with the question "I would like to try stopping one of my medicines to see how I feel without it" using complete case analysis (p = 0.044) or multiple imputation (p = 0.038), but not when using personal mean substitution (p = 0.057) | ||
| 520 | |a CONCLUSIONS: Missing data and methods used to handle missing data were underreported in studies using the rPATD questionnaire. The methods should be chosen carefully as our analyses from two distinct studies suggest that they may impact the interpretation of the findings from the questionnaire | ||
| 650 | 4 | |a Systematic Review | |
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| 700 | 1 | |a Fournier, Jean-Pascal |e verfasserin |4 aut | |
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