Sample size impact on the categorisation of continuous variables in clinical prediction

Recent advances in information technologies are generating a growth in the amount of available biomedical data. In this paper, we studied the impact sample size may have on the categorisation of a continuous predictor variable in a logistic regression setting. Two different approaches to categorise predictor variables were compared.MINECO: MTM2011-28285-C02-01, MTM2013-40941-P, MTM2014-55966-P. Basque Government: IT620-13. University of the Basque Country UPV/EHU: UFI11/52 Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273)

Robust combination of the Morris and Sobol methods in complex multidimensional models

Conducting global sensitivity analysis using variance decomposition methods in complex simulation models with many input factors is usually unaffordable. An alternative is to first apply a screening method to reduce the number of input factors and then apply a variance decomposition method to the reduced model. However, usually selection of input factors is not done robustly and convergence of the screening method is not ensured. We propose two new criteria, a criterion that mimics the visual selection of the input factors and a convergence criterion. In the application of the criteria to a complex model, the Morris screening method has needed 200 trajectories to converge and the visual criterion has outperformed other existing criteria. Our proposal ensures a robust combination of the Morris and the Sobol methods that provides an objective and automatic method to select the most important input factors with a feasible computing load to achieve convergence.Basque Government IT1294-19 MTM2016-74931-

Selecting the number of categories of the lymph node ratio in cancer research: A bootstrap-based hypothesis test

The high impact of the lymph node ratio as a prognostic factor is widely established in colorectal cancer, and is being used as a categorized predictor variable in several studies. However, the cut-off points as well as the number of categories considered differ considerably in the literature. Motivated by the need to obtain the best categorization of the lymph node ratio as a predictor of mortality in colorectal cancer patients, we propose a method to select the best number of categories for a continuous variable in a logistic regression framework. Thus, to this end, we propose a bootstrap-based hypothesis test, together with a new estimation algorithm for the optimal location of the cut-off points called BackAddFor, which is an updated version of the previously proposed AddFor algorithm. The performance of the hypothesis test was evaluated by means of a simulation study, under different scenarios, yielding type I errors close to the nominal errors and good power values whenever a meaningful difference in terms of prediction ability existed. Finally, the methodology proposed was applied to the CCR-CARESS study where the lymph node ratio was included as a predictor of five-year mortality, resulting in the selection of three categories.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19) from the Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco, the BERC 2018-2021 program and the SPRI Elkartek project 3KIA (KK-2020/00049); by the Spanish Government through the Ministerio de Ciencia, Innovación y Universidades: BCAM Severo Ochoa accreditation SEV-2017-0718 and by Ministerio de Economía y Competitividad and FEDER under research grants MTM2014-55966-P, MTM2016-74931-P and MTM2017-89422-P; and by Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019-2022) and the EU (ERDF), Ref. ED431G2019/06. Financial support for data collection was provided in part by grants from the Instituto de Salud Carlos III, (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397, and the thematic networks REDISSEC - Red de Investigación en Servicios de Salud en Enfermedades Crónicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF “Investing in your future”); and the Research Committee of the Hospital Galdakao

Estimation of cut-off points under complex-sampling design data

In the context of logistic regression models, a cut-off point is usually selected to dichotomize the estimated predicted probabilities based on the model. The techniques proposed to estimate optimal cut-off points in the literature, are commonly developed to be applied in simple random samples and their applicability to complex sampling designs could be limited. Therefore, in this work we propose a methodology to incorporate sampling weights in the estimation process of the optimal cut-off points, and we evaluate its performance using a real data-based simulation study. The results suggest the convenience of considering sampling weights for estimating optimal cut-off points.IT1294-19 BERC 2018-2021 KK-2020/00049 PIF18/21

Variable selection with LASSO regression for complex survey data

Variable selection is an important step to end up with good prediction models. LASSO regression models are one of the most commonly used methods for this purpose, for which cross-validation is the most widely applied validation technique to choose the tuning parameter (λ). Validation techniques in a complex survey framework are closely related to “replicate weights”. However, to our knowledge, they have never been used in a LASSO regression context. Applying LASSO regression models to complex survey data could be challenging. The goal of this paper is two-fold. On the one hand, we analyze the performance of replicate weights methods to select the tuning parameter for fitting LASSO regression models to complex survey data. On the other hand, we propose new replicate weights methods for the same purpose. In particular, we propose a new design-based cross-validation method as a combination of the traditional cross-validation and replicate weights. The performance of all these methods has been analyzed and compared by means of an extensive simulation study to the traditional cross-validation technique to select the tuning parameter for LASSO regression models. The results suggest a considerable improvement when the new proposal design-based cross-validation is used instead of the traditional crossvalidation.IT1456-22 PIF18/21

A new approach to categorize continuous variables in prediction models: Proposal and validation

When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points' location is known in theory or in practice. In addition, the proposed method is applied to a real data set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting.MINECO: MTM2010-14913, MTM2011-28285-C02-01 and MTM2013-40941-P. Basque Government: IT620-13, 2012111008. University of the Basque Country UPV/EHU: GIU10/21, UFI11/52. Agrupamento IN-BIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). Red IRYSS (Investigación en Resultados y Servicios Sanitarios)- of the Instituto de Salud Carlos III: G03/22

Comparison of beta-binomial regression model approaches to analyze health related quality of life data

Health related quality of life (HRQoL) has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of HRQoL with patients' and disease's characteristics has become one of the primary aims of many HRQoL studies. HRQoL scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data, however, the beta-binomial regression has been performed by means of two di erent approaches in the literature: i) beta-binomial distribution with a logistic link; and ii) hierarchical generalized linear models (HGLMs). None of the existing literature in the analysis of HRQoL survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of HRQoL outcomes in patients with Chronic Obstructive Pulmonary Disease (COPD), where the use of both approaches yields to contradictory results in terms of covariate e ects signi cance and consequently the interpretation of the most relevant factors in HRQoL. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of HRQoL survey data for practitioners, providing an R package.IT-620-13, MTM2013-40941-P, MTM2014-52184-P, MTM2016-74931-P, RD12/0001/0001 - REDISSE

Acute toxicity, bioaccumulation and effects of dietary 1 transfer of silver from brine 2 shrimps exposed to PVP/PEI-coated silver nanoparticles to zebrafish

The extensive use and release to the aquatic environment of silver nanoparticles (NPs) could lead to their incorporation into the food web. Brine shrimp larvae of 24 h showed low sensitivity to the exposure to PVP/PEI-coated Ag NPs (5 nm), with EC50 values at 24 h of 19.63 mg Ag L-1, but they significantly accumulated silver after 24 h of exposure to 100 μg L-1 of Ag NPs. Thus, to assess bioaccumulation and effects of silver transferred by the diet in zebrafish, brine shrimp larvae were exposed to 100 ng L-1 of Ag NPs as an environmentally relevant concentration or to 100 μg L-1 as a potentially effective concentration and used to feed zebrafish for 21 days. Autometallography revealed a dose- and time-dependent metal accumulation in the intestine and in the liver of zebrafish. Three-day feeding with brine shrimps exposed to 100 ng L-1 of Ag NPs was enough to impair fish health as reflected by the significant reduction of lysosomal membrane stability and the presence of vacuolization and necrosis in the liver. However, dietary exposure to 100 μg L-1 of Ag NPs for 3 days did not significantly alter gene transcription levels, neither in the liver nor in the intestine. After 21 days, biological processes such as lipid transport and localization, cellular response to chemical stimulus and response to xenobiotic stimulus were significantly altered in the liver. Overall, these results indicate an effective dietary transfer of silver and point out to liver as the main target organ for Ag NP toxicity in zebrafish after dietary exposure.MINECO (NanoSilverOmicsproject- MAT2012-39372) Basque Government (consolidated research groups IT810-13 and IT620-13; Saiotek S-PE13UN142) University of the Basque Country (UFIs 11/37 and 11/52)

A Computer Application to Predict Adverse Events in the Short-Term Evolution of Patients With Exacerbation of Chronic Obstructive Pulmonary Disease

Background: Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Exacerbations of COPD (eCOPD) contribute to the worsening of the disease and the patient’s evolution. There are some clinical prediction rules that may help to stratify patients with eCOPD by their risk of poor evolution or adverse events. The translation of these clinical prediction rules into computer applications would allow their implementation in clinical practice. Objective: The goal of this study was to create a computer application to predict various outcomes related to adverse events of short-term evolution in eCOPD patients attending an emergency department (ED) based on valid and reliable clinical prediction rules. Methods: A computer application, Prediction of Evolution of patients with eCOPD (PrEveCOPD), was created to predict 2 outcomes related to adverse events: (1) mortality during hospital admission or within a week after an ED visit and (2) admission to an intensive care unit (ICU) or an intermediate respiratory care unit (IRCU) during the eCOPD episode. The algorithms included in the computer tool were based on clinical prediction rules previously developed and validated within the Investigación en Resultados y Servicios de Salud COPD study. The app was developed for Windows and Android systems, using Visual Studio 2008 and Eclipse, respectively. Results: The PrEveCOPD computer application implements the prediction models previously developed and validated for 2 relevant adverse events in the short-term evolution of patients with eCOPD. The application runs under Windows and Android systems and it can be used locally or remotely as a Web application. Full description of the clinical prediction rules as well as the original references is included on the screen. Input of the predictive variables is controlled for out-of-range and missing values. Language can be switched between English and Spanish. The application is available for downloading and installing on a computer, as a mobile app, or to be used remotely via internet. Conclusions: The PrEveCOPD app shows how clinical prediction rules can be summarized into simple and easy to use tools, which allow for the estimation of the risk of short-term mortality and ICU or IRCU admission for patients with eCOPD. The app can be used on any computer device, including mobile phones or tablets, and it can guide the clinicians to a valid stratification of patients attending the ED with eCOPD.Fondo de Investigación Sanitaria (PI 06\1010, PI06\1017, PI06\714, PI06\0326, PI06\0664) Departamento de Salud del Gobierno Vasco (2012111008) Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco (IT620-13) Ministerio de Economía y Competitividad del Gobierno Español and FEDER (MTM2013-40941-P and MTM2016-74931-P) the Research Committee of the Hospital Galdakao the thematic networks -REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas) - of the Instituto de Salud Carlos III