Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis

Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analyse

Bootstrap-based procedures for inference in nonparametric ROC regression analysis

Before the use of a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is an essential step. The receiver operating characteristic (ROC) curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy needs to be also assessed. In this paper, attention is focused on an estimator for the covariate-specific ROC curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalized additive models for the ROC curve (ROC-GAM). The main aim of the paper is to offer new inferential procedures for testing the effect of co- variates over the conditional ROC curve within the ROC-GAM context. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the ROC curve; and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computed-aided diagnostic (CAD) system for the automatic detection of tumour masses in breast cancer is analysed

Time-dependent ROC methodology to evaluate the predictive accuracy of semiparametric multi-state models in the presence of competing risks: An application to peritoneal dialysis programme

The evaluation of peritoneal dialysis (PD) programmes requires the use of statistical methods that suit the complexity of such programmes. Multi-state regression models taking competing risks into account are a good example of suitable approaches. In this work, multi-state structured additive regression (STAR) models combined with penalized splines (P-splines) are proposed to evaluate peritoneal dialysis programmes. These models are very flexible since they may consider smooth estimates of baseline transition intensities and the inclusion of time-varying and smooth covariate effects at each transition. A key issue in survival analysis is the quantification of the time-dependent predictive accuracy of a given regression model, which is typically assessed using receiver operating characteristic (ROC)’based methodologies. The main objective of the present study is to adapt the concept of time-dependent ROC curve, and their corresponding area under the curve (AUC), to a multi-state competing risks framework. All statistical methodologies discussed in this work were applied to PD survival data. Using a multi-state competing risks framework, this study explored the effects of major clinical covariates on survival such as age, sex, diabetes and previous renal replacement therapy. Such multi-state model was composed of one transient state (peritonitis) and several absorbing states (death, transfer to haemodialysis and renal transplantation). The application of STAR models combined with time-dependent ROC curves revealed important conclusions not previously reported in the nephrology literature when using standard statistical methodologies. For practical application, all the statistical methods proposed in this article were implemented in R and we wrote and made available a script named as NestedCompRisks

Determinantes de la duración de la incapacidad temporal y la vuelta al trabajo en un área sanitaria de Galicia

ObjetivoDeterminar los factores asociados con la incidencia y la duración de la incapacidad temporal (IT) en un área sanitaria.DiseñoDescriptivo, retrospectivo.EmplazamientoÁrea Sanitaria Sur de la provincia de Lugo.ParticipantesUna muestra de 1.513 episodios de IT seleccionada aleatoriamente entre el total de éstos, durante un período de 3 años.Mediciones principalesSe analizaron las características sociodemográficas del paciente, el régimen de la seguridad social (SS), el diagnóstico que justifica la IT y la fecha de la prescripción; del médico prescriptor se analizaron la edad, el sexo, la formación especializada, la antigüedad en la plaza y los años de ejercicio. La comparación de medias se realizó mediante el análisis de la varianza y el test de Kruskal-Wallis. El efecto relativo de cada variable sobre la probabilidad de volver al trabajo se estimó mediante modelos de regresión de Cox.ResultadosLa duración media de los episodios de IT fue de 74 ± 103 días. Los diagnósticos más frecuentes fueron los del sistema osteomioarticular (SOMA), las lesiones y envenenamientos (LYE) y las enfermedades respiratorias (NML). Se reduce la probabilidad de volver al trabajo con el incremento de la edad, en los regímenes de seguridad social autónomos y agrarios por cuenta propia, en los diagnósticos de enfermedades mentales y del aparato circulatorio, y cuando el médico prescriptor es de mayor edad o menos antiguo en la plaza.ConclusionesLa duración media de los episodios de IT es superior a la de otros estudios españoles. Los factores que más influyen en la reincorporación al trabajo son la edad del paciente, el régimen de la seguridad social y la enfermedad diagnosticada.ObjectiveTo determine the factors associated with the incidence and duration of temporary work incapacity (TWI) in a health district.DesignDescriptive and retrospective study.SettingSouth health district of the province of Lugo, Spain.ParticipantsA random sample of 1513 cases was selected among the total of episodes of TWI, during 3 years period.Main measuresThe main factors analyzed are, on the one hand, the socio-demographic characteristics of the patient, his or her social security (SS) scheme, diagnosis that justifies the TWD, and the prescription date; and, on the other hand, the age, sex, specialised training, time in the post and years in practice of the physician who prescribes the TWI. The comparison of the means was carried out using variance analysis and the Kruskal-Wallis test. The relative effect of each variable on the probability of returning to the work was estimated through Cox regression models.ResultsThe mean duration of the episodes of TWI was of 74±103 days. The most frequent diagnoses were those of the bones-muscles and joints (BMAJ), injuries and poisonings (IAP), and respiratory diseases (RD). The probability of returning to work is reduced with the increase of the age, with agrarian and autonomous SS affiliates, with diagnoses of mental disease or diagnoses of the circulatory system, and in cases prescribed by older doctors or less time in the post.ConclusionsThe mean duration of the episodes of TWD is higher than that of other Spanish studies. The most influential factors in the return to work are the age of the patient, the SS scheme and the diagnosed illness

Assessing the relationship between markers of glycemic control through flexible copula regression models

Glycated haemoglobin (HbA1c) is a sensitive marker of blood glucose in patients with diabetes. However, levels can vary considerably, even amongst individuals with similar mean blood glucose concentrations. Other glycated proteins, such as fructosamine, can also act as blood sugar markers, but estimating HbA1c and fructosamine via independent models may lead to errors of interpretation regarding disease severity. From a clinical standpoint, it would be of great interest to know the factors that affect the mean concentration of both HbA1c and fructosamine, which influence the variability in the concentrations of these glycated markers and cause HbA1c/fructosamine discordance. Flexible models are required to illustrate the behaviour of these variables as well as the association between them. This work reviews existing models that might serve in this regard. Flexible copula regression models using splines were used to provide a better understanding of the behaviour of both glycated proteins and the relationship between them under the possible influence of different covariates. This work shows the usefulness of this type of models in practise and provides a basis for their clinical interpretation by means of an understandable case study. Ultimately, to better understand the effects of each continuous covariate, they are represented at the true scale of the response variables

Optimum sample size to estimate mean parasite abundance in fi sh parasite surveys

[EN] To reach ethically and scientifically valid mean abundance values in parasitological and epidemiological studies this paper considers analytic and simulation approaches for sample size determination. The sample size estimation was carried out by applying mathematical formula with predetermined precision level and parameter of the negative binomial distribution estimated from the empirical data. A simulation approach to optimum sample size determination aimed at the estimation of true value of the mean abundance and its confidence interval (CI) was based on the Bag of Little Bootstraps (BLB). The abundance of two species of monogenean parasites Ligophorus cephali and L. mediterraneus from Mugil cephalus across the Azov-Black Seas localities were subjected to the analysis. The dispersion pattern of both helminth species could be characterized as a highly aggregated distribution with the variance being substantially larger than the mean abundance. The holistic approach applied here offers a wide range of appropriate methods in searching for the optimum sample size and the understanding about the expected precision level of the mean. Given the superior performance of the BLB relative to formulae with its few assumptions, the bootstrap procedure is the preferred method. Two important assessments were performed in the present study: i) based on CIs width a reasonable precision level for the mean abundance in parasitological surveys of Ligophorus spp. could be chosen between 0.8 and 0.5 with 1.6 and 1x mean of the CIs width, and ii) the sample size equal 80 or more host individuals allows accurate and precise estimation of mean abundance. Meanwhile for the host sample size in range between 25 and 40 individuals, the median estimates showed minimal bias but the sampling distribution skewed to the low values; a sample size of 10 host individuals yielded to unreliable estimates.SS and VS were supported by MEDEA project fellowships, Erasmus Mundus Action 2. CC-S was funded by project #MTM2014-52975-C2-1-R:" Inference in Structured Additive Regression (STAR) Models with Extensions to Multivariate Responses. Applications in Biomedicine", cofinanced by the Ministry of Economy and Competitiveness (SPAIN) and by the European Regional Development Fund (FEDER). This study is partially supported by Ministry of Education and Science of Ukraine, project #1/17.Shvydka, S.; Sarabeev, V.; Estruch, VD.; Cadarso-Suarez, C. (2018). Optimum sample size to estimate mean parasite abundance in fi sh parasite surveys. Helminthologia. 55(1):52-59. https://doi.org/10.1515/helm-2017-0054S5259551Rohde, K., Hayward, C., & Heap, M. (1995). Aspects of the ecology of metazoan ectoparasites of marine fishes. International Journal for Parasitology, 25(8), 945-970. doi:10.1016/0020-7519(95)00015-tAnderson, R. M., & Gordon, D. M. (1982). Processes influencing the distribution of parasite numbers within host populations with special emphasis on parasite-induced host mortalities. Parasitology, 85(2), 373-398. doi:10.1017/s0031182000055347Poiani, A. (1992). Ectoparasitism as a possible cost of social life: a comparative analysis using Australian passerines (Passeriformes). Oecologia, 92(3), 429-441. doi:10.1007/bf00317470Kleiner, A., Talwalkar, A., Sarkar, P., & Jordan, M. I. (2014). A scalable bootstrap for massive data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(4), 795-816. doi:10.1111/rssb.12050Jovani, R., & Tella, J. L. (2006). Parasite prevalence and sample size: misconceptions and solutions. Trends in Parasitology, 22(5), 214-218. doi:10.1016/j.pt.2006.02.011BAGGE, A. M., SASAL, P., VALTONEN, E. T., & KARVONEN, A. (2005). Infracommunity level aggregation in the monogenean communities of crucian carp (Carassius carassius). Parasitology, 131(3), 367-372. doi:10.1017/s0031182005007626Belghyti, D., Berrada-rkhami, O., Boy, V., Aguesse, P., & Gabrion, C. (1994). Population biology of two helminth parasites of flatfishes from the Atlantic coast of Morocco. Journal of Fish Biology, 44(6), 1005-1021. doi:10.1111/j.1095-8649.1994.tb01272.xTAYLOR, L. R. (1961). Aggregation, Variance and the Mean. Nature, 189(4766), 732-735. doi:10.1038/189732a

Assessing the relationship between markers of glycemic control through flexible copula regression models

Glycated haemoglobin (HbA1c) is a sensitive marker of blood glucose in patientswith diabetes. However, levels can vary considerably, even among individualswith similar mean blood glucose concentrations. Other glycated proteins, such asfructosamine, can also act as blood sugar markers, but estimating HbA1c and fruc-tosamine via independent models may lead to errors of interpretation regardingdisease severity. From a clinical standpoint, it would be of great interest to knowthe factors that affect the mean concentration of both HbA1c and fructosamine,that influence the variability in the concentrations of these glycated markers, andthat cause HbA1c/fructosamine discordance. Flexible models are required that illus-trate the behaviour of these variables as well as the association between them. Thepresent work reviews existing models that might serve in this regard. Flexible cop-ula regression models using P-splines, were used to provide a better understandingof the behaviour of both glycated proteins, and the relationship between them underthe possible influence of different covariates. This work shows the usefulness ofthis type of models in practice, and provides a basis for its clinical interpretation bymeans of an understandable case study. Ultimately, to better understand the effectsof each continuous covariate, they were represented at the true scale of the response variables

Impact of the Covid-19 pandemic on perinatal mental health (Riseup-PPD-COVID-19): protocol for an international prospective cohort study

Background: Corona Virus Disease 19 (COVID-19) is a new pandemic, declared a public health emergency by the World Health Organization, which could have negative consequences for pregnant and postpartum women. The scarce evidence published to date suggests that perinatal mental health has deteriorated since the COVID-19 outbreak. However, the few studies published so far have some limitations, such as a cross-sectional design and the omission of important factors for the understanding of perinatal mental health, including governmental restriction measures and healthcare practices implemented at the maternity hospitals. Within the Riseup-PPD COST Action, a study is underway to assess the impact of COVID-19 in perinatal mental health. The primary objectives are to (1) evaluate changes in perinatal mental health outcomes; and (2) determine the risk and protective factors for perinatal mental health during the COVID-19 pandemic. Additionally, we will compare the results between the countries participating in the study. Methods: This is an international prospective cohort study, with a baseline and three follow-up assessments over a six-month period. It is being carried out in 11 European countries (Albania, Bulgaria, Cyprus, France, Greece, Israel, Malta, Portugal, Spain, Turkey, and the United Kingdom), Argentina, Brazil and Chile. The sample consists of adult pregnant and postpartum women (with infants up to 6 months of age). The assessment includes measures on COVID-19 epidemiology and public health measures (Oxford COVID-19 Government Response Tracker dataset), Coronavirus Perinatal Experiences (COPE questionnaires), psychological distress (BSI-18), depression (EPDS), anxiety (GAD-7) and post-traumatic stress symptoms (PTSD checklist for DSM-V). Discussion: This study will provide important information for understanding the impact of the COVID-19 pandemic on perinatal mental health and well-being, including the identification of potential risk and protective factors by implementing predictive models using machine learning techniques. The findings will help policymakers develop suitable guidelines and prevention strategies for perinatal mental health and contribute to designing tailored mental health interventions. Trial registration: ClinicalTrials.gov Identifier: NCT04595123.The project is part of the COST Action Riseup-PPD CA 18138 and was supported by COST under COST Action Riseup-PPD CA18138; also, by the Spanish Ministry of Health, the Institute of Health Carlos III, and the European Regional Development Fund «Una manera de hacer Europa» by the Prevention and Health Promotion Research Network ‘redIAPP’ (RD16/0007). Raquel Costa is supported by the FSE and FCT under an individual Post-Doctoral Grant SFRH/BPD/117597/2016. Rena Bina and Drorit Levy received funding from the Bar-Ilan Dangoor Centre for Personalized Medicine, Israel. Ana Mesquita is supported from the Portuguese Foundation for Science and Technology (FCT) and from EU through the European Social Fund and from the Human Potential Operational Program - IF/00750/2015. Ana Osório received financial support from CAPES/Proex no. 0653/2018 and CAPES/PrInt grant no. 88887.310343/2018-00.The funders of the study had no role in the study design or the writing the protocol. The corresponding author had final responsibility for the decision to submit for publication

Applying Binary Structured Additive Regression (STAR) for predicting wildfire in Galicia, Spain

Studies on causes and dynamics of wildfires make an important contribution to environmental. In the north of Spain, Galicia is one of the areas in which wildfires are the main cause of forest destruction. The main aim of this work is to model geographical and environmental effects on the risk of wildfires in Galicia using flexible regression techniques based on Structured Additive Regression (STAR) models. This methodology represents a new contribution to the classical logistic Generalized Linear Models (GLM) and Generalized Additive Models (GAM), commonly used in this environmental context. Their advantage lies on the flexibility of including spatial and temporal covariates, jointly with the other continuous covariates information. Moreover, these models generate maps of both structured and the unstructured effects, and they plotted separately. Working at spatial scales with a voxel resolution level of 1Km x 1Km per day, with the possibility of mapping the predictions in a color range, the binary STAR model represents an important tool for planning and management for the prevention of wildfires. Also, this statistical tool can accelerate the progress of fire behavior models that can be very useful for developing plans of prevention and firefightingPeer Reviewe