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      Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains

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          Abstract

          Background

          Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains.

          Methods

          In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping.

          Results

          Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83–0.93).

          Conclusions

          The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice.

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          Most cited references29

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

            The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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              The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research.

              To develop the Self-Administered Comorbidity Questionnaire (SCQ) and assess its psychometric properties, including the predictive validity of the instrument, as reflected by its association with health status and health care utilization after 1 year. A cross-sectional comparison of the SCQ with a standard, chart abstraction-based measure (Charlson Index) was conducted on 170 inpatients from medical and surgical care units. The association of the SCQ with the chart-based comorbidity instrument and health status (short form 36) was evaluated cross sectionally. The association between these measures and health status and resource utilization was assessed after 1 year. The Spearman correlation coefficient for the association between the SCQ and the Charlson Index was 0.32. After restricting each measure to include only comparable items, the correlation between measures was stronger (Spearman r = 0.55). The SCQ had modest associations with measures of resource utilization during the index admission, and with health status and resource utilization after 1 year. The SCQ has modest correlations with a widely used medical record-based comorbidity instrument, and with subsequent health status and utilization. This new measure represents an efficient method to assess comorbid conditions in clinical and health services research. It will be particularly useful in settings where medical records are unavailable.
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                Author and article information

                Contributors
                Dora.Revesz@maastrichtuniversity.nl , D.Revesz@uvt.nl
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                12 March 2020
                12 March 2020
                2020
                : 20
                : 54
                Affiliations
                [1 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Department of Epidemiology, GROW – School for Oncology and Developmental Biology, , Maastricht University, ; P. Debyeplein 1, 6200 MD Maastricht, the Netherlands
                [2 ]ISNI 0000 0001 0943 3265, GRID grid.12295.3d, Department of Medical and Clinical Psychology, CoRPS – Center of Research on Psychology in Somatic diseases, , Tilburg University, ; Warandelaan 2, 5037 AB Tilburg, the Netherlands
                [3 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Clinical Epidemiology and Medical Technology Assessment, , Maastricht University Medical Centre+, ; P. Debyelaan 25, PO Box 5800, Maastricht, 6202 AZ the Netherlands
                [4 ]ISNI 0000 0004 0501 9982, GRID grid.470266.1, Netherlands Comprehensive Cancer Organisation (IKNL), ; Godebaldkwartier 419, 3511 DT Utrecht, the Netherlands
                [5 ]ISNI 0000 0001 0791 5666, GRID grid.4818.5, Division of Human Nutrition, , Wageningen University & Research, ; Stippeneng 4, 6708 WE Wageningen, the Netherlands
                [6 ]ISNI 0000 0001 2097 4281, GRID grid.29857.31, Department of Public Health Sciences, , Penn State Cancer Institute, ; 500 University, Hershey, PA 17033 USA
                [7 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, , Maastricht University, ; P. Debyeplein 1, 6200 MD Maastricht, the Netherlands
                [8 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Department of Surgery, , Maastricht University Medical Centre, ; P. Debyelaan 25, 6229 HX Maastricht, the Netherlands
                [9 ]GRID grid.430814.a, Department of Psychosocial Oncology and Epidemiology, , Netherlands Cancer Institute, ; Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
                Author information
                http://orcid.org/0000-0003-0435-6808
                Article
                1064
                10.1186/s12911-020-1064-9
                7068880
                32164641
                4969a504-8762-46c5-be4a-61016586e8b5
                © The Author(s). 2020

                Open Access This 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
                : 30 September 2019
                : 23 February 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004622, KWF Kankerbestrijding;
                Award ID: UM-2012-5653
                Award Recipient :
                Funded by: Health foundation Limburg
                Award ID: 00005739
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Bioinformatics & Computational biology
                colorectal cancer,cancer survivors,quality of life,prediction models,model development,internal validation

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