Search

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perf

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.

The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not.

BACKGROUND: Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies. METHODS: We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant. RESULTS: Even if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection. CONCLUSIONS: The source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.

In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field

Despite advancements in lowering blood pressure, the best approach to lower it remains controversial because of the lack of information on the molecular basis of hypertension. We, therefore, performed plasma proteomics of plasma from patients with hypertension to identify molecular determinants detectable in these subjects but not in controls and vice versa. Plasma samples from hypertensive subjects (cases; n=118) and controls (n=85) from the InGenious HyperCare cohort were used for this study and performed mass spectrometric analysis. Using biostatistical methods, plasma peptides specific for hypertension were identified, and a model was developed using least absolute shrinkage and selection operator logistic regression. The underlying peptides were identified and sequenced off-line using matrix-assisted laser desorption ionization orbitrap mass spectrometry. By comparison of the molecular composition of the plasma samples, 27 molecular determinants were identified differently expressed in cases from controls. Seventy percent of the molecular determinants selected were found to occur less likely in hypertensive patients. In cross-validation, the overall R 2 was 0.434, and the area under the curve was 0.891 with 95% confidence interval 0.8482 to 0.9349, P<0.0001. The mean values of the cross-validated proteomic score of normotensive and hypertensive patients were found to be -2.007±0.3568 and 3.383±0.2643, respectively, P<0.0001. The molecular determinants were successfully identified, and the proteomic model developed shows an excellent discriminatory ability between hypertensives and normotensives. The identified molecular determinants may be the starting point for further studies to clarify the molecular causes of hypertension

Purpose Shared Electronic Health Record (EHR) systems, which provide a health information exchange (HIE) within a community of care, were found to be a key enabler of informational continuity of diabetes mellitus (DM) care. Quantitative analyses of the actual contribution of Shared EHR systems to informational continuity of care are rare. The goal of this study was to quantitatively analyze (i) the degree of fragmentation of DM care in Austria as an indicator for the need for HIE, and (ii) the quantity of information (i.e. number of documents) from Austrian DM patients that would be made available by a nationwide Shared EHR system for HIE. Methods Our analyses are based on social security claims data of 7.9 million Austrians from 2006 and 2007. DM patients were identified through medication data and inpatient diagnoses. The degree of fragmentation was determined by the number of different healthcare providers per patient. The amount of information that would be made available by a nationwide Shared EHR system was estimated by the number of documents that would have been available to a healthcare provider if he had access to information on the patient’s visits to any of the other healthcare providers. As a reference value we determined the number of locally available documents that would have originated from the patient’s visits to the healthcare provider himself. We performed our analysis for two types of systems: (i) a “comprehensive” Shared EHR system (SEHRS), where each visit of a patient results in a single document (progress note), and (ii) the Austrian ELGA system, which allows four specific document types to be shared. Results 391,630 DM patients were identified, corresponding to 4.7% of the Austrian population. More than 90% of the patients received health services from more than one healthcare provider in one year. Both, the SEHRS as well as ELGA would have multiplied the available information during a patient visit in comparison to an isolated local EHR sy

Purpose Shared Electronic Health Record (EHR) systems, which provide a health information exchange (HIE) within a community of care, were found to be a key enabler of informational continuity of diabetes mellitus (DM) care. Quantitative analyses of the actual contribution of Shared EHR systems to informational continuity of care are rare. The goal of this study was to quantitatively analyze (i) the degree of fragmentation of DM care in Austria as an indicator for the need for HIE, and (ii) the quantity of information (i.e. number of documents) from Austrian DM patients that would be made available by a nationwide Shared EHR system for HIE. Methods Our analyses are based on social security claims data of 7.9 million Austrians from 2006 and 2007. DM patients were identified through medication data and inpatient diagnoses. The degree of fragmentation was determined by the number of different healthcare providers per patient. The amount of information that would be made available by a nationwide Shared EHR system was estimated by the number of documents that would have been available to a healthcare provider if he had access to information on the patient’s visits to any of the other healthcare providers. As a reference value we determined the number of locally available documents that would have originated from the patient’s visits to the healthcare provider himself. We performed our analysis for two types of systems: (i) a “comprehensive” Shared EHR system (SEHRS), where each visit of a patient results in a single document (progress note), and (ii) the Austrian ELGA system, which allows four specific document types to be shared. Results 391,630 DM patients were identified, corresponding to 4.7% of the Austrian population. More than 90% of the patients received health services from more than one healthcare provider in one year. Both, the SEHRS as well as ELGA would have multiplied the available information during a patient visit in comparison to an isolated local EHR sy

Objectives. This 2013 update of the practice guidelines for the biological treatment of unipolar depressive disorders was developed by an international Task Force of the World Federation of Societies of Biological Psychiatry (WFSBP). The goal has been to systematically review all available evidence pertaining to the treatment of unipolar depressive disorders, and to produce a series of practice recommendations that are clinically and scientifically meaningful based on the available evidence. The guidelines are intended for use by all physicians seeing and treating patients with these conditions. Methods. The 2013 update was conducted by a systematic update literature search and appraisal. All recommendations were approved by the Guidelines Task Force. Results. This first part of the guidelines (Part 1) covers disease definition, classification, epidemiology, and course of unipolar depressive disorders, as well as the management of the acute and continuation phase treatment. It is primarily concerned with the biological treatment (including antidepressants, other psychopharmacological medications, electroconvulsive therapy, light therapy, adjunctive and novel therapeutic strategies) of adults. Conclusions. To date, there is a variety of evidence-based antidepressant treatment options available. Nevertheless there is still a substantial proportion of patients not achieving full remission. In addition, somatic and psychiatric comorbidities and other special circumstances need to be more thoroughly investigated. Therefore, further high-quality informative randomized controlled trials are urgently needed. © 2013 Informa Healthcare.

The purpose of this study is to examine programming techniques incorporating evolutionary strategies. It has been proposed that evolutionary strategies may provide alternative programming paradigms for finding solutions to problems of polynomial or NP-complete complexity. Disciplines such as Emergent Behaviour, Cognitive Science, Artificial Life, Complexity, and Bio-Informatics are working out the mechanics of how these systems operate. An attempt is made to demonstrate some of these evolutionary strategies through the creation of a program incorporating a genetic algorithm. The goal of the program is to generate aesthetically pleasing entity relationship diagrams. The crux of any genetic algorithm is to develop an appropriate fitness function. This task proves to be surprisingly difficult. Ironically, the task of finding the appropriate balance in the weighting of different fitness factors is in itself a problem that may be best addressed by an evolutionary algorithm. As a practical tool for the generation of aesthetically pleasing entity relationship diagrams, the ERD Viewer application was not overly successful. However, the implementation did provide a great deal of insight into the difficulties of specifying the rules when attempting to guide evolution. There are many suggestions for further development including a potential method to implement learning. Evolutionary strategies hold promise for exploring problem spaces where the number of potential solutions grows exponentially. The proof that these de-centralized, non-global, emergent processes do actually work surrounds us in the natural world. The prime examples are the two stochastic processes: evolution and learning. We are still in the early stages of determining how these techniques can be practically utilized in a programming environment., 2004-05

The purpose of this study is to examine programming techniques incorporating evolutionary strategies. It has been proposed that evolutionary strategies may provide alternative programming paradigms for finding solutions to problems of polynomial or NP-complete complexity. Disciplines such as Emergent Behaviour, Cognitive Science, Artificial Life, Complexity, and Bio-Informatics are working out the mechanics of how these systems operate. An attempt is made to demonstrate some of these evolutionary strategies through the creation of a program incorporating a genetic algorithm. The goal of the program is to generate aesthetically pleasing entity relationship diagrams. The crux of any genetic algorithm is to develop an appropriate fitness function. This task proves to be surprisingly difficult. Ironically, the task of finding the appropriate balance in the weighting of different fitness factors is in itself a problem that may be best addressed by an evolutionary algorithm. As a practical tool for the generation of aesthetically pleasing entity relationship diagrams, the ERD Viewer application was not overly successful. However, the implementation did provide a great deal of insight into the difficulties of specifying the rules when attempting to guide evolution. There are many suggestions for further development including a potential method to implement learning. Evolutionary strategies hold promise for exploring problem spaces where the number of potential solutions grows exponentially. The proof that these de-centralized, non-global, emergent processes do actually work surrounds us in the natural world. The prime examples are the two stochastic processes: evolution and learning. We are still in the early stages of determining how these techniques can be practically utilized in a programming environment., 2004-05

To document the migration pattern of the black-headed gull in Bavaria 706 recoveries were used from the EURING-databank. The material supports previous results, that gulls borne in the southern part of Bavaria (south of the river Danube) use south-western areas for wintering (> 85% of all recoveries), whereas gulls from northern Bavaria use areas in north-western (about 40%) and south-western direction (60%). In general no pronounced influence of the population increase was found during the sixties on the spatial migration pattern. Weak evidence points to some temporal changes in the migration pattern: gulls migrate a bit earlier to the breeding areas. Since 1965 an increased colony fidelity has occured.

In contrast to conventional drums the rotary drum to be described here has an ascending angle toward the outlet. This produces a torsional thrust which is responsible for even particle sizes. Further advantages are the higher mass throughput rates and the lower specific power consumption., In contrast to conventional drums the rotary drum to be described here has an ascending angle toward the outlet. This produces a torsional thrust which is responsible for even particle sizes. Further advantages are the higher mass throughput rates and the lower specific power consumption.