Minimum clinical important difference for resilience scale specific to cancer: a prospective analysis

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Abstract

Background

The minimum clinical important differences (MCIDs) of resilience instruments in patients with cancer have not been comprehensively described. This study was designed to evaluate MCIDs of 10-item and 25-item resilience scales specific to cancer (RS-SC-10 and RS-SC-25).

Methods

From June 2015 to December 2018, RS-SCs were longitudinally measured in 765 patients with different cancer diagnoses at baseline (T0) and 3 months later (T1). The EORTC QLQ-C30, Connor-Davidson Resilience Scale, Hospital Anxiety and Depression Scale, and Allostatic Load Index were measured concurrently as anchors. Anchor-based methods (linear regression, within-group), distribution-based methods(within-group), and receiver operating characteristic curves (ROCs, within-subject) were performed to evaluate the MCIDs.

Results

623 of 765 (84.1%) patients had paired RS-SCs scores. Moderate correlations were identified between the change in RS-SCs and change in anchors ( r = 0.38–0.44, all p < 0.001). Linear regression estimated + 8.9 and − 6.7 as the MCIDs of RS-SC-25, and + 3.4 and − 2.5 for RS-SC-10. Distribution-based methods estimated + 9.9 and − 9.9 as the MCIDs of RS-SC-25, and + 4.0 and − 4.0 for RS-SC-10. ROC estimated + 5.5 and − 4.5 as the MCIDs of RS-SC-25, and + 2.0 and − 1.5 for RS-SC-10.

Conclusions

The most reliable MCID is around 5 points for RS-SC-25 and 2 points for RS-SC-10. RS-SCs are more responsive to the worsening status of resilience in patients with cancer and these estimates could be useful in future resilience-based intervention trials.

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Most cited references 49

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Cancer statistics in China, 2015.

Wanqing Chen, Rongshou Zheng, Peter D Baade … (2016)

With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Because of China's massive population (1.37 billion), previous national incidence and mortality estimates have been limited to small samples of the population using data from the 1990s or based on a specific year. With high-quality data from an additional number of population-based registries now available through the National Central Cancer Registry of China, the authors analyzed data from 72 local, population-based cancer registries (2009-2011), representing 6.5% of the population, to estimate the number of new cases and cancer deaths for 2015. Data from 22 registries were used for trend analyses (2000-2011). The results indicated that an estimated 4292,000 new cancer cases and 2814,000 cancer deaths would occur in China in 2015, with lung cancer being the most common incident cancer and the leading cause of cancer death. Stomach, esophageal, and liver cancers were also commonly diagnosed and were identified as leading causes of cancer death. Residents of rural areas had significantly higher age-standardized (Segi population) incidence and mortality rates for all cancers combined than urban residents (213.6 per 100,000 vs 191.5 per 100,000 for incidence; 149.0 per 100,000 vs 109.5 per 100,000 for mortality, respectively). For all cancers combined, the incidence rates were stable during 2000 through 2011 for males (+0.2% per year; P = .1), whereas they increased significantly (+2.2% per year; P < .05) among females. In contrast, the mortality rates since 2006 have decreased significantly for both males (-1.4% per year; P < .05) and females (-1.1% per year; P < .05). Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.

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Interpretation of changes in health-related quality of life: the remarkable universality of half a standard deviation.

Geoffrey R Norman, Jeff A. Sloan, Kathleen Wyrwich (2003)

A number of studies have computed the minimally important difference (MID) for health-related quality of life instruments. To determine whether there is consistency in the magnitude of MID estimates from different instruments. We conducted a systematic review of the literature to identify studies that computed an MID and contained sufficient information to compute an effect size (ES). Thirty-eight studies fulfilled the criteria, resulting in 62 ESs. For all but 6 studies, the MID estimates were close to one half a SD (mean = 0.495, SD = 0.155). There was no consistent relationship with factors such as disease-specific or generic instrument or the number of response options. Negative changes were not associated with larger ESs. Population-based estimation procedures and brief follow-up were associated with smaller ESs, and acute conditions with larger ESs. An explanation for this consistency is that research in psychology has shown that the limit of people's ability to discriminate over a wide range of tasks is approximately 1 part in 7, which is very close to half a SD. In most circumstances, the threshold of discrimination for changes in health-related quality of life for chronic diseases appears to be approximately half a SD.

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Author and article information

Contributors

Zeng Jie Ye:

ORCID: http://orcid.org/0000-0002-4437-3947

zengjieye@qq.com

Journal

Journal ID (nlm-ta): Health Qual Life Outcomes

Journal ID (iso-abbrev): Health Qual Life Outcomes

Title: Health and Quality of Life Outcomes

Publisher: BioMed Central (London )

ISSN (Electronic): 1477-7525

Publication date (Electronic): 9 December 2020

Publication date PMC-release: 9 December 2020

Publication date Collection: 2020

Volume: 18

Electronic Location Identifier: 381

Affiliations

[1 ]GRID grid.411866.c, ISNI 0000 0000 8848 7685, Guangzhou University of Chinese Medicine, ; Guangzhou, 510006 Guangdong Province China

[2 ]Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060 Guangdong Province China

[3 ]GRID grid.411866.c, ISNI 0000 0000 8848 7685, Institute of Tumor, , Guangzhou University of Chinese Medicine, ; Guangzhou, 510006 Guangdong Province China

[4 ]GRID grid.411866.c, ISNI 0000 0000 8848 7685, Guangdong Provincial Key Laboratory of New Drug Development and Research of Chinese Medicine, Mathematical Engineering Academy of Chinese Medicine, , Guangzhou University of Chinese Medicine, ; Guangzhou, 510006 Guangdong Province China

[5 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, The Seventh Affiliated Hospital, , Sun Yat-sen University, ; Shenzhen, 510275 Guangdong Province China

[6 ]Army Medical University, Chongqing Municipality, 400038 China

[7 ]GRID grid.411866.c, ISNI 0000 0000 8848 7685, The First Affiliated Hospital, , Guangzhou University of Chinese Medicine, ; Guangzhou, 510405 Guangdong Province China

[8 ]Guangdong Academy of Population Development, Guangzhou, 510600 Guangdong Province China

[9 ]GRID grid.79703.3a, ISNI 0000 0004 1764 3838, South China University of Technology, ; Guangzhou, 510641 Guangdong Province China

Author information

Zeng Jie Ye http://orcid.org/0000-0002-4437-3947

Article

Publisher ID: 1631

DOI: 10.1186/s12955-020-01631-6

PMC ID: 7724855

PubMed ID: 33298059

SO-VID: 6be32b05-aefb-4260-bff6-ae47252de4de

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 29 June 2020

Date accepted : 23 November 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 71904033

Funded by: Humanity and Social Science Youth Foundation of Ministry of Education of China

Award ID: 19YJCZH227

Funded by: Humanity and Social Science Foundation of Department of Education of Guangdong Province

Award ID: 2020WTSCX009

Funded by: Humanity and Social Science Foundation of Guangzhou University of Chinese Medicine

Award ID: 2020SKXK01

Funded by: Medical Scientific Research Foundation of Guangdong Province

Award ID: A2019484

Funded by: Research Fund for Talented Scholars of Guangzhou University of Chinese Medicine

Award ID: A1-AFD018

Funded by: Innovative Project of Guangzhou University of Chinese Medicine

Award ID: 2016KYTD08

Custom metadata

ScienceOpen disciplines: Health & Social care

Keywords: resilience,nursing,cancer,rs-sc-25,rs-sc-10,minimum clinical important difference,anchor-based,distribution-based,roc

Data availability:

ScienceOpen disciplines: Health & Social care

Keywords: resilience, nursing, cancer, rs-sc-25, rs-sc-10, minimum clinical important difference, anchor-based, distribution-based, roc

Minimum clinical important difference for resilience scale specific to cancer: a prospective analysis

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Most cited references 49

Cancer statistics in China, 2015.

Index for rating diagnostic tests

Interpretation of changes in health-related quality of life: the remarkable universality of half a standard deviation.

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