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The version of record of this article, first published in AAPS journal, is available online at Publisher’s website: http://dx.doi.org/10.1208/s12248-024-00921-x, Comparative bioavailability studies often involve multiple groups of subjects for a variety of reasons, such as clinical capacity limitations. This raises questions about the validity of pooling data from these groups in the statistical analysis and whether a group-by-treatment interaction should be evaluated. We investigated the presence or absence of group-by-treatment interactions through both simulation techniques and a meta-study of well-controlled trials. Our findings reveal that the test falsely detects an interaction when no true group-by-treatment interaction exists. Conversely, when a true group-by-treatment interaction does exist, it often goes undetected. In our meta-study, the detected group-by-treatment interactions were observed at approximately the level of the test and, thus, can be considered false positives. Testing for a group-by-treatment interaction is both misleading and uninformative. It often falsely identifies an interaction when none exists and fails to detect a real one. This occurs because the test is performed between subjects in crossover designs, and studies are powered to compare treatments within subjects. This work demonstrates a lack of utility for including a group-by-treatment interaction in the model when assessing single-site comparative bioavailability studies, and the clinical trial study structure is divided into groups., Peer Reviewed, Postprint (published version)

Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal responses, using finite mixtures to cluster the rows and columns of the matrix. Those models can incorporate the main effects of individual rows and columns and the cluster effects to model the matrix of responses. However, many real-world applications also include available covariates. In this study, we have extended mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization (EM) algorithm and assess their performance. Finally, we also illustrate an application of the models to the well-known arthritis clinical trial data set, Peer Reviewed, Postprint (published version)

Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal responses, using finite mixtures to cluster the rows and columns of the matrix. Those models can incorporate the main effects of individual rows and columns and the cluster effects to model the matrix of responses. However, many real-world applications also include available covariates. In this study, we have extended mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization (EM) algorithm and assess their performance. Finally, we also illustrate an application of the models to the well-known arthritis clinical trial data set., Peer Reviewed, Postprint (published version)

Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of individual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the individuals’ similar patterns of responses and the effects of the covariates on the individuals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set, "This work has been supported by the Ministerio de Ciencia e Innovación (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033], and by Grant 2021 SGR 01421 (GRBIO) administrated by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain). Daniel Fernández is member of the Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM). Daniel Fernández is a Serra Húnter Fellow", Peer Reviewed, Postprint (published version)

Given a set of points, the microaggregation problem aims to find a clustering with a minimum sum of squared errors (SSE), where the cardinality of each cluster is greater than or equal to k. Points in the cluster are replaced by the cluster centroid, thus satisfying k-anonymity. Microaggregation is considered one of the most effective techniques for numerical microdata protection. Traditionally, non-optimal solutions to the microaggregation problem are obtained by heuristic approaches. Recently, the authors of this paper presented a mixed integer linear optimization (MILO) approach based on column generation for computing tight solutions and lower bounds to the microaggregation problem. However, MILO can be computationally expensive for large datasets. In this work we present a new heuristic that combines three blocks: (1) a decomposition of the dataset into subsets, (2) the MILO column generation algorithm applied to each dataset in order to obtain a valid microaggregation, and (3) a local search improvement algorithm to get the final clustering. Preliminary computational results show that this approach was able to provide (and even improve upon) some of the best solutions (i.e., of smallest SSE) reported in the literature for the Tarragona and Census datasets, and k¿{3,5,10} ., Peer Reviewed, Postprint (author's final draft)

Peer Reviewed, Postprint (published version)

HPGe detectors are commonly used in the laboratories to detect gamma-rays due to its high resolution and efficiency which is a good characteristic to distinguish low energy gamma radiation. In the present study a HPGe detector called Ge5 of LARA Laboratori d’anàlisi de radiactivitat ambiental (INTE-UPC) must be studied because it is intended to be used again to get the activities of several samples. The first step is to get the efficiency calibration for environmental samples which are small and large filters. Efficiency calibration can be obtained experimentally but the efficiency curves would not be reliable for a continuum energy range. Moreover, HPGe detector are limited because some of the radionuclides such as 60Co releases more than one gamma-ray at the same time and distorts the actual efficiency. Therefore, it is needed to get correction factors. Monte Carlo methods are used to get efficiency calibration so that an accurate efficiency curve can be obtained for a wide energy range. Moreover, the correction factors can be computed. PENELOPE/PenEasy is the program used to get the efficiency calibration for small and large filters and correct the efficiency of 60Co due to the true coincidence summing effect. This program will also allow to define the geometry of the Ge5 detector since the layout is unknown. In order to determine the geometry of the detector Ge5 a previous simulation is done with old environmental samples. After characterizing the geometry of the detector, a current experiment with a large filter is simulated to get a new efficiency calibration which is the main goal of the project.

Objective: To evaluate the association between prematurity (by the gestational week [gw]) and ADHD during childhood. Method: Observational, matched cohort study using data from children born in a tertiary-level hospital (Hospital Universitari Vall d'Hebron, Catalonia, Spain) during 1995-2007 and data from the Information System for the Development of Research in Primary Health Care (SIDIAP database, Catalonia, Spain). Results: Prevalence of ADHD increases as gestational age decreases, 12.7% for those born =28 gw, compared to 3.2% for those born after the 37 gw. The risk of developing ADHD in the non-premature children tends to increase as the gw decreases (35-36 gw, hazard ratio [HR] = 1.70, 95% confidence interval [CI] [1.19, 2.44]; 33-34 gw, HR = 3.38, 95% CI [2.08, 5.50]; 29-32 gw, HR = 2.37, 95% CI [1.54, 3.63]; and =28 gw, HR = 5.57, 95% CI [2.49, 12.46]) Conclusion: Being born preterm is associated with a risk of developing ADHD, also in late preterm children (35-36 gw). Attention when taking care of these infants regarding their mental health must be made., Peer Reviewed, Postprint (author's final draft)

El Observatorio del Estudiante de la Universitat de Barcelona acoge el proyecto “Identidad y Vida Universitaria” que propone una herramienta con la que suplir la necesidad de conocer las características, expectativas y demandas del alumnado. En este trabajo se recopila el proceso de creación del cuestionario con el que poner a prueba dicha herramienta para probar su eficacia. Recogiendo y analizando una muestra de estudiantes pertenecientes a la Facultad de Economía y Empresa de la Universitat de Barcelona, se indaga en la situación real de los individuos en cuanto su percepción respecto que misión desempeña la institución: formar a los alumnos para ser capaces de adaptarse a las demandas profesionales de la sociedad o formarlos con una orientación ética.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948–2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms., Peer Reviewed, Postprint (published version)

Archetypoid analysis (ADA) is an exploratory approach that explains a set of continuous observations as mixtures of pure (extreme) patterns. Those patterns (archetypoids) are actual observations of the sample which makes the results of this technique easily interpretable, even for non-experts. Note that the observations are approximated as a convex combination of the archetypoids. Archetypoid analysis, in its current form, cannot be applied directly to ordinal data. We propose and describe a two-step method for applying ADA to ordinal responses based on the ordered stereotype model. One of the main advantages of this model is that it allows us to convert the ordinal data to numerical values, using a new data-driven spacing that better reflects the ordinal patterns of the data, and this numerical conversion then enables us to apply ADA straightforwardly. The results of the novel method are presented for two behavioural science applications. Finally, the proposed method is also compared with other unsupervised statistical learning methods., Daniel Fernández was supported by the grant 2017 SGR 622 (GRBIO) administrated by the Departament d’Economia i Coneixement de la Generalitat de Catalunya (Spain). Daniel Fernández and Louise Fastier McMillan were supported by the Marsden Grant E2987-3648 administrated by the Royal Society of New Zealand., Peer Reviewed, Postprint (published version)

La millora i el manteniment continu de la qualitat de l’atenció als malalts amb ictus agut requereix una avaluació periòdica de la pràctica clínica. Els Audits de l’Ictus són l’instrument avaluatiu del PDMVC. La millora dels seus resultats pretén garantir la millora dels resultats dels pacients., S’han auditat 4.008 casos ingressats per ictus agut entre 2018 i 2019. El període d’estudi s’ha ampliat a 6 mesos, en períodes de 1 mes i mig al llarg de 12 mesos, similars per a tots els centres. L’obtenció prospectiva de dades s’ha realitzat majoritàriament per infermeres de cada hospital. La mediana del temps entre l’inici dels símptomes i l’arribada a urgències va ser de 2,1 hores. El 72% dels casos van arribar a l’hospital dins les primeres quatre hores i mitja. Respecte al 4t Audit: * Augmenten els ingressos en Unitat d’ictus agut (44,2% a 61,3%), les activacions del Codi Ictus (42,9 a 61,4; realitzades pel SEM de 43,4 a 67,8%) i els tractaments de reperfusió (16% a 30% dels ictus isquèmics) * Augmenta el nombre de pacients en els que es diagnostica durant l’ingrés una fibril·lació auricular no coneguda prèviament (7% a 18,8%). * Hi ha un lleuger augment de les pneumònies(6% a 8%)ibaixa la mor talitat intrahospitalària (12% a 9%). * Sis indicadors de qualitat milloren significativament, tres 3 indicadors es mantenen i 3 indicadors empitjoren. Destaca una important millora en alguns indicadors de qualitat rellevants com són la realització del test de disfàgia, l’avaluació del perfil lipídic, l’educació sanitària als pacients i familiars, el registre de l’etiologia de l’ictus i la utilització d’escales neurològiques. Es necessiten accions de milloradels indicadors següents: pauta d’antitrombòtics abans de 48 hores, mobilització precoç i avaluació de l’estat d’ànim. L’estat d’ànim s’avalua en un baix percentatge i es fa servir una gran variabilitat d’eines de mesura., Preprint

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Objective: We aimed to describe sociodemographic, comorbidities, co-medication and risk of thromboembolic events and bleeding in patients with NVAF initiating oral anticoagulants (OAC) for stroke prevention, and to estimate adherence and persistence to OAC. Setting: Primary Health Care (PHC) in the Catalan Health Institute (ICS), Catalunya, Spain. Participants: All NVAF adult patients initiating OAC for stroke prevention in August 2013-December 2015. Methods: Population-based cohort study. Persistence was measured in patients initiating OAC in August 2013-December 2014. Data source: SIDIAP, which captures electronic health records from PHC in the (ICS), covering approximately 5.8 million people. Results: 51,690 NVAF patients initiated OAC; 47,197 (91.3%) were naive to OAC and 32,404 (62.7%) initiated acenocoumarol. Mean age was 72.8 years (SD 12.3) and 49.4% were women. Platelet-aggregation inhibitors were taken by 9,105 (17.6%) of the patients. Persistence and adherence were estimated up to the end of follow-up. For 22,075 patients, persistence was higher among the non-naive patients [n=258 (61.7%)] than among the naive [n=11,502 (53.1%)]. Adherence was estimated for patients initiating DOAC and it was similar in naive and non-naive patients. Among the naive to DOAC treatment, those starting rivaroxaban showed a highest proportion [(n=360 (80.1%)] of good adherence at implementation (MPR>80%) while patients starting dabigatran were less adherent [n= 203 [47.8%)]. Conclusions: Acenocoumarol was the most frequently prescribed OAC as first therapy in NVAF patients. Non-naive to DOAC showed better persistence than naive. Rivaroxaban showed higher proportion of adherent patients during the implementation phase than apixaban and dabigatran the lowest., Peer Reviewed, Postprint (author's final draft)

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Peer Reviewed, Postprint (published version)

El presente documento parte de la realización de un análisis del rendimiento de técnicas de minería de datos aplicadas en diferentes métodos de predicción, la cual se centra en la predicción de aprobados o suspensos de los estudiantes de la ETSEIB, en concreto de las asignaturas que se han cursado en el Q3. Dicha minería de datos es un proceso que se ha desarrollado durante los últimos años y se caracteriza por la extracción de información significativa que se encuentra dentro de un conjunto de datos que permite la identificación de patrones. De este modo, se hace posible la predicción de futuras situaciones. Para poderla llevar a cabo, se han utilizado diferentes técnicas de predicción, como la regresión logística y el árbol de decisión, tomando la primera como modelo predictivo base. La metodología que se ha seguido durante este proceso de análisis es la denominada CRISP-DM modelo referente en la minería de datos y compuesta por diferentes etapas, las cuales se han aplicado en este proyecto. Para la realización de dicho proyecto se ha utilizado el lenguaje de programación Python y, en concreto, las librerías Pandas y SciKit-Learn incluidas en Python que han permitido la creación de modelos predictivos. Además, a través de la plataforma Anaconda y mediante Spyder, se ha conseguido dinamizar la programación en Python con el objetivo de poder observar los resultados de una forma más rápida.

Public services, such as higher education, medical services, libraries or public administration offices, provide services to their customers. To obtain opinion-satisfaction indices of customers, it would be necessary to survey all the customers of the service (census), which is impossible. What is possible is to estimate the indices by surveying a random customer sample. The efficiency obtained with the classic data envelopment analysis models, considering the opinion indices of the customers of the public service as output data estimated with a user sample, will be an estimation of the obtained efficiency if the census is available. This paper proposes a bootstrap methodology to build a confidence region to simultaneously estimate the population data envelopment analysis efficiency score vector of a set of public service-producing units, with a fixed confidence level and using deterministic input data and estimated customer opinion indices as output data. The usefulness of the result is illustrated by describing a case study comparing the efficiency of libraries., Peer Reviewed

©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset., Peer Reviewed, Postprint (author's final draft)

Noise maps, whether they are based on simulation or in measurements, share the difficulty of data acquisition, since several input information is required to properly represent the temporal and spatial evolution of noise in the place under assessment. Therefore, mobile sampling could be a proper solution to enhance data acquisition, as it would increase considerably the spatiotemporal resolution of noise samples without compromising the accuracy and representativeness. In order to perform noise measurements in movement, the present research proposes a scheme of mobile sampling based on a low-cost georeferenced noise sensor mounted on a bicycle. To validate the application of the mobile sampling approach, several mobile and static measurements are taken, then an analysis between mobile and static sampling is performed, Postprint (published version)

Longer life expectancy has led to an increase in the prevalence of age-related cognitive decline and dementia worldwide. Due to the current lack of effective treatment for these conditions, preventive strategies represent a research priority. A large body of evidence suggests that nutrition is involved in the pathogenesis of age-related cognitive decline, but also that it may play a critical role in slowing down its progression. At a population level, healthy dietary patterns interventions, such as the Mediterranean and the MIND diets, have been associated with improved cognitive performance and a decreased risk of neurodegenerative disease development. In the era of evidence-based medicine and patient-centered healthcare, personalized nutritional recommendations would offer a considerable opportunity in preventing cognitive decline progression. N-of-1 clinical trials have emerged as a fundamental design in evidence-based medicine. They consider each individual as the only unit of observation and intervention. The aggregation of series of N-of-1 clinical trials also enables population-level conclusions. This review provides a general view of the current scientific evidence regarding nutrition and cognitive decline, and critically states its limitations when translating results into the clinical practice. Furthermore, we suggest methodological strategies to develop N-of-1 clinical trials focused on nutrition and cognition in an older population. Finally, we evaluate the potential challenges that researchers may face when performing studies in precision nutrition and cognition., Peer Reviewed, Postprint (published version)

This is the peer reviewed version of the following article: Bofill, M.; Gómez Melis, G. A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components. "Statistics in medicine", 20 Maig 2019, vol. 38, núm. 11, p. 1935-1956, which has been published in final form at https://doi.org/10.1002/sim.8092. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving., Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. In practice, the marginal parameters of the components can be obtained from previous studies or pilot trials; however, the correlation is often not previously reported and thus usually unknown. We first show that the sample size for composite binary endpoints is strongly dependent on the correlation and, second, that slight deviations in the prior information on the marginal parameters may result in underpowered trials for achieving the study objectives at a pre-specified significance level. We propose a general strategy for calculating the required sample size when the correlation is not specified and accounting for uncertainty in the marginal parameter values. We present the web platform CompARE to characterize composite endpoints and to calculate the sample size just as we propose in this paper. We evaluate the performance of the proposal with a simulation study and illustrate it by means of a real case study using CompARE., Peer Reviewed, Postprint (author's final draft)

Introduction 24-hour ambulatory blood pressure monitoring (ABPM) is the gold standard diagnostic method for hypertension, but has some shortcomings in clinical practice while clinical settings often lack sufficient devices to accommodate all patients with suspected hypertension. Home blood pressure monitoring (HBPM) and office blood pressure monitoring (OBPM) also have shortcomings, such as the white coat effect or a lack of accuracy. This study aims to study the validity of a new method of diagnosing hypertension consisting of monitoring blood pressure (BP) for 1 hour and comparing it with OBPM and HBPM and examining the sensitivity and specificity of this method compared with 24-hour ABPM. The patient experience will be examined in each method. Methods and analysis A minimum sample of 214 patients requiring a diagnostic test for hypertension from three urban primary healthcare centres will be included. Participants will undergo 24-hour ABPM, 1-hour BP measurement (1-BPM), OBPM for three consecutive weeks and HBPM. Patients will follow a random sequence to first receive 24-hour ABPM or 1-hour ABPM. Daytime 24-hour ABPM records will be compared with the other monitoring methods using the correlation coefficient and Bland Altman plots. The kappa concordance index and the sensitivity and specificity of the methods will be calculated. The patient’s experience will be studied, with selected indicators of efficiency and satisfaction calculated using parametric tests. Ethics and dissemination The protocol has been authorised by the research ethics committee of the Hospital Clinic of Barcelona (Ref. HCB/2014/0615): protocol details and amendments will be recorded and reported to ClinicalTrials.com. The results will be disseminated in peer-reviewed literature, and to policy makers and healthcare partners., Peer Reviewed, Postprint (published version)

© 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Patient recruitment in clinical trials is challenging with failure to recruit to time and target sample size common. This may be caused by unanticipated problems or by overestimation of the recruitment rate. This study is a systematic review of statistical models to predict recruitment at the design stage of clinical trials, The authors would like to thank Ms Van Nguyen Thu, PhD candidate, on the MiRoR project, and Professor Isabelle Boutron, Paris Descartes University, for their help in reviewing the manuscript before submission. This project is part of the MiRoR (Methods in Research on Research) Joint Doctoral Training Programme. The authors would like to acknowledge the MiRoR network for their support and guidance. The European Union's Horizon 2020 Marie Sklodowska Curie Innovative Training Networks–European Joint Doctorate (ITN-EJD) under grant agreement No. 676207, fund MiRoR., Peer Reviewed, Postprint (published version)

Prevention and nutrition are key for healthy lifestyles and are crucial in the new paradigm of patient-centered medicine, as healthy diet protects against chronic diseases and helps to delay diseases in general population. Making healhty diets is becoming essential and there are no well-formalized support mechanisms for suporting nutritionists when designing diets of specific patients. The project Diet4You proposes an intelligent decision support system oriented to the adaptive and dynamic preparation of personalized diets for specific individuals from general population, with or without comorbidities. Diet4You is a hybrid system interacting several AI and data science components in a complex way to compose diets by taking into account all the information available on the person, including their characteristics, health conditions, personal and cultural habits and eventual drugs intake and their genomic information. Authors are not aware of other menus recommenders with such a holistic view. The system is highly configurable. It relies on an information system that can formalize all contextual knowledge to improve optimization, and the menus are composed over a food database containing prepared dishes with known nutritional decomposition., This work has been partially supported by project Diet4You (TIN2014-60557-R), and the Consolidated Research Group Grant from AGAUR (Generalitat de Catalunya, cataalan government) IDEAI-UPC (AGAUR SGR2017-574)., Peer Reviewed, Postprint (author's final draft)

This is the peer reviewed version of the following article: Forns, J. [et al.]. Validity of ICD-9 and ICD-10 codes used to identify acute liver injury: a study in three European data sources. "Pharmacoepidemiology and drug safety", 6 Juny 2019, vol. 28, núm. 7, p. 965-975, which has been published in final form at 10.1002/pds.4803. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.", Purpose Validating cases of acute liver injury (ALI) in health care data sources is challenging. Previous validation studies reported low positive predictive values (PPVs). Methods Case validation was undertaken in a study conducted from 2009 to 2014 assessing the risk of ALI in antidepressants users in databases in Spain (EpiChron and SIDIAP) and the Danish National Health Registers. Three ALI definitions were evaluated: primary (specific hospital discharge codes), secondary (specific and nonspecific hospital discharge codes), and tertiary (specific and nonspecific hospital and outpatient codes). The validation included review of patient profiles (EpiChron and SIDIAP) and of clinical data from medical records (EpiChron and Denmark). ALI cases were confirmed when liver enzyme values met a definition by an international working group. Results Overall PPVs (95% CIs) for the study ALI definitions were, for the primary ALI definition, 84% (60%-97%) (EpiChron), 60% (26%-88%) (SIDIAP), and 74% (60%-85%) (Denmark); for the secondary ALI definition, 65% (45%-81%) (EpiChron), 40% (19%-64%) (SIDIAP), and 70% (64%-77%) (Denmark); and for the tertiary ALI definition, 25% (18%-34%) (EpiChron), 8% (7%-9%) (SIDIAP), and 47% (42%-52%) (Denmark). The overall PPVs were higher for specific than for nonspecific codes and for hospital discharge than for outpatient codes. The nonspecific code “unspecified jaundice” had high PPVs in Denmark. Conclusions PPVs obtained apply to patients using antidepressants without preexisting liver disease or ALI risk factors. To maximize validity, studies on ALI should prioritize hospital specific discharge codes and should include hospital codes for unspecified jaundice. Case validation is required when ALI outpatient cases are considered., Peer Reviewed, Postprint (author's final draft)

© 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Objectives The objective of the study was to identify factors affecting the use of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement, specifically authors’ attitudes toward and experiences with it. Study Design and Setting An online survey was distributed to authors of observational studies recruited via social media, personal network snowballing, and mass mailings using targeted search strategies. Data on demographics, awareness, motivators, and usage were collected in conjunction with a modified Unified Theory of Acceptance and Use of Technology (UTAUT) scale on which confirmatory factor analysis (CFA) was performed. Results One thousand fifteen participants completed the survey. Of these, 185 (18.2%) indicated they had never heard of STROBE nor used it previously, 195 (19.2%) had heard of it but never used it, and 635 (62.6%) had used it. Journals promoting STROBE were both key motivators and awareness mechanisms; peers and educational workshops were also important influencing factors to a lesser degree. The internal consistency of the modified UTAUT scale was strong (Cronbach's alpha = 0.94). CFA supported a four-factor model with 23 questions. Conclusion The endorsement of STROBE by journals is key to authors’ awareness and use of the guideline. We tested and validated our scale which can guide future research on reporting guidelines., The authors would like to acknowledge the Methods in Research on Research (MiRoR) network for their support and guidance. The authors would also like to thank Dr. Michail Kovanis for assistance gathering author emails and Ms. Alice Biggane for her assistance with the recruitment strategy., Peer Reviewed, Postprint (published version)