Tree Based Method for Aggregate Survival Data Modeling.

The key test for confidence in any association discovered within the medical domain is replication testing. That is, the ability of the association to be detected in independent populations. At the same time, in order to increase the likelihood of discovering statistically significant associations there is a clear need to increase the statistical power of any given study. A key methodology for increasing statistical power is through the use of as many subjects as possible that match a study's inclusion criteria. Thus many have attempted to merge data from multiple independent sources/sites/studies that contain the same inclusion criteria for subjects as a way of creating a much larger study with significantly more statistical power. For these approaches to work though data from multiple sites need to be made available to a single analysis. This practice is significantly limited by the need to respect legal and ethical requirements that are often complicated, ambiguous and inconsistent across different countries. The common approach to achieve merging of data is by sharing aggregated data rather than subject's personal data. Aggregated data however may still in some cases be reverse engineered, therefore traditionally cells within the aggregated data with small values were suppressed, and some or all of the aggregated data were perturbed in order to add noise inhibiting any attempts at identifying personal information of a specific person or sub-group in the original data. In this paper we study the effects of cell-suppression and perturbation on the results of the data analysis. Each approach is looked at by itself as well as in combination using the typical settings documented in the literature. The tests are based on a real dataset that looks for associations between phenotypes and genetic markers. This work is part of the Linked2Safety project that aims to dynamically interconnect distributed patients' data to better enable medical research efforts, whilst respecting patients' anonymity, as well as European and national legislation.

A review of measurement tools for child wellbeing

Postoperative Atrial Fibrillation After Cardiac Surgery: A Meta-Analysis

In this paper, we propose a method of presenting an aerial image at any point in the same three-dimensional space as a user. In the existing method, presenting an image to an arbitrary point in 3D space is difficult because the presentation position on the device is fixed. Therefore, in this study, particles with scattering properties remain to an arbitrary point in space and used as a screen. Specifically, we focus on the phenomenon of vortex rings that can stably transport particles, and we generate an aerial screen by colliding vortex rings ejected from air cannons at multiple points in the air. In a prototyping experiment, we generated a screen at a specified point in space and confirmed that aerial image presentation was possible by projection. We also determined potential issues.

Meso-level Comparison of Mental Health Service Availability and Use in Chile and Spain

A Guide to Selecting Flexible Survival Models to Inform Economic Evaluations of Cancer Immunotherapies

MSR83 Predicting Pseudo Individual Patient-Level Data with Covariates

In longitudinal studies, investigators often measure multiple variables at multiple time points and are interested in investigating individual differences in patterns of change on those variables. Furthermore, in behavioral, social, psychological, and medical research, investigators often deal with latent variables that cannot be observed directly and should be measured by 2 or more manifest variables. Longitudinal latent variables occur when the corresponding manifest variables are measured at multiple time points. Our primary interests are in studying the dynamic change of longitudinal latent variables and exploring the possible interactive effect among the latent variables. Much of the existing research in longitudinal studies focuses on studying change in a single observed variable at different time points. In this article, we propose a novel latent curve model (LCM) for studying the dynamic change of multivariate manifest and latent variables and their linear and interaction relationships. The proposed LCM has the following useful features: First, it can handle multivariate variables for exploring the dynamic change of their relationships, whereas conventional LCMs usually consider change in a univariate variable. Second, it accommodates both first- and second-order latent variables and their interactions to explore how changes in latent attributes interact to produce a joint effect on the growth of an outcome variable. Third, it accommodates both continuous and ordered categorical data, and missing data.

The connection of clinical care with clinical research is the main purpose of the Learning Health System (LHS) integrating scientific information, informatics, and patient care. The LHS generates new medical knowledge as a by-product of the care process. For this purpose, the aggregation of data from Electronic Health Records (EHR), Case Report Forms (CRF), web questionnaires with other data sources like primary care databases and genetic data repositories is necessary for research purposes. This joining of healthcare and research processes results in challenges for the privacy protection framework of the LHS. Based on an exploration of EU legal requirements for data protection and privacy, different data access policies of data provider organizations as well as existing privacy frameworks of research projects, basic privacy principles and privacy requirements were extracted. Based on privacy principles and legal requirements a graphical model to display privacy protection requirements was created. This graphical model is based on concepts of requirements engineering and can be used like a model kit to create new privacy frameworks and to ease knowledge exchange with stakeholders of the LHS. Our model is built upon the concept of three privacy zones (Care Zone, Non-Care Zone and Research Zone) representing areas where similar legal requirements and rules apply. These zones contain databases, data transformation operators, such as data linkers and privacy filters and graphs to indicate the data flow necessary for research processes. The aim of the model is to help arrange its components in a way that creates a risk gradient for the data flow from a zone of high risk for patient identification to a zone of low risk. The model is applied to the analysis of several general clinical research usage scenarios and two research use cases from the TRANSFoRm project (finding patients for clinical research and linkage of databases). Both use cases represent different data collection aspects of the LHS. The model was used during discussions with data managers from the NIVEL Primary Care Database in the Netherlands and validated by representing an approved research case of using primary care data employing NIVEL services. Experiences with the graphic privacy model used to improve the privacy framework of TRANSFoRm and with the presentation of the model to LHS stakeholders and the research community are discussed.

Participant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of randomized clinical trials. No gold standard has been identified for detecting noncompliance, but in some trials participants' biomarkers can provide objective information that suggests exposure to non-study treatments. However, existing methods are limited to retrospectively detecting noncompliance at a single time point based on a single biomarker measurement. We propose a novel method that can leverage participants' full biomarker history to detect noncompliance across multiple time points. Conditional on longitudinal biomarker data, our method can estimate the probability of compliance at (1) a single time point of the trial, (2) all time points, and (3) a future time point. Across time points, we model the biomarker as a mixture density with (latent) components corresponding to longitudinal patterns of compliance. To estimate the mixture density, we fit mixed effects models for both compliance and the biomarker. We use the mixture density to derive compliance probabilities that condition on the longitudinal biomarker data. We evaluate our compliance probabilities by simulation and apply them to a trial in which current smokers were asked to only smoke low nicotine study cigarettes (Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2). In the simulation, we investigated three different effects of compliance on the biomarker, as well as the effect of misspecification of the covariance structures. We compared probability estimators (1) and (2) to those that ignore the longitudinal correlation in the data according to area under the receiver operating characteristic curve. We evaluated estimator (3) by plotting its calibration lines. For Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, we compared estimators (1) and (3) to a probability estimator of compliance at the last time point that ignores the longitudinal correlation. In the simulation, for both compliance at the last time point and at all time points, conditioning on the longitudinal biomarker data uniformly raised area under the receiver operating characteristic curve across all three compliance effect scenarios. The gains in area under the receiver operating characteristic curve were smaller under misspecification. The calibration lines for the prediction of compliance closely followed 45°, though with additional variability under misspecification. For compliance at the last time point of Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, conditioning on participants' full biomarker history boosted area under the receiver operating characteristic curve by three percentage points. The prediction probabilities somewhat accurately approximated the non-longitudinal compliance probabilities. Compared to existing methods that only use a single biomarker measurement, our method can account for the longitudinal correlation in the biomarker and compliance to more accurately identify noncompliant participants. Our method can also use participants' biomarker history to predict compliance at a future time point.

e14528 Background: Treatment monitoring in patients with High-Grade Glioma (HGG) and identification of disease progression, remains a major challenge in clinical neurooncology. We aimed to develop a support vector machine (SVM) classifier utilising combined longitudinal conventional and Dynamic Susceptibility Contrast (DSC) perfusion MRI to classify between Stable Disease (SD), Pseudoprogression (PsP) and Progressive Disease (PD) in glioma patients under surveillance. Methods: Conventional (269) and perfusion (62) MRI studies of HGG patients acquired between 2012 and 2018 were prospectively analysed. Study participants were separated into two groups: Group I with a single DSC time point (64 participants) and Group II with multiple DSC time points (19 participants). The SVM classifier was trained using all available MRI for each group. Classification accuracy was assessed for the use of features extracted from different feature dataset and time point combinations and compared to the experienced radiologists’ predictions. Results: The study included 64 participants (mean age: 48.5 ± 12.8 yrs [standard deviation], 24 female). SVM classification based on combined perfusion and structural features outperformed standalone datasets across all groups. For the clinically relevant classification step (SD/PSP vs PD), both feature combination as well as the addition of multiple DSC time points, improved classification performance (lowest median error rate: 0.016). The SVM algorithm outperformed radiologists in predicting lesion destiny in both groups. Optimal performance was observed in Group II, in which SVM sensitivity/specificity/accuracy was 100/91.67/94.7% for analysis based on the first time point and 85.71/100/ 94.7% based on multiple time points compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In Group I, the SVM also exceeded radiologist predictions, albeit by a smaller margin and resulted in sensitivity/specificity of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists). Conclusions: Our results indicate that the addition of multiple longitudinal perfusion time points as well as the combination of structural and perfusion features significantly enhance classification outcome in treatment monitoring of HGGs and machine-learning-assisted diagnosis has potentially superior accuracy than the radiologist's visual evaluation and expertise.

We study clustering over multiple graphs each encoding a distinct set of similarity relationships (edges) over the same set of objects (nodes) where the aim is to identify clusters that are supported across the collection of graphs. This problem of simultaneous clustering is readily motivated by the recent deluge of datasets in several domains (including the biological sciences, social sciences, and marketing), where the same objects are repeatedly measured in different conditions, populations or time points. Whilst there has been a vast amount of heuristic work on practical simultaneous clustering problems, little is known on the theoretical side – we present theoretical results that help explain why such heuristics typically come without quantitative guarantees. We give algorithmic and complexity results for simultaneous clustering using two standard measures on clustering quality: density and connectivity. Specifically, we focus on the basic problem of finding a single cluster (rather than an entire clustering) that is simultaneously of high quality in every graph. When the quality of a cluster is its minimum density over all graphs, we show the problem is not approximable within a factor of 2 1−e , unless NP ⊆ DTIME(n). Furthermore, this problem appears very difficult even when there are just two graphs; the resulting problem is approximately as hard as the problem of finding a dense subgraph on at most k vertices. When cluster quality is a fixed connectivity requirement between terminals within the cluster, there are two natural optimization problems: a maximization version (find a good quality cluster with as many terminals as possible) and a minimization version (find a good quality cluster that is as small as possible). We show that the maximization problem is tractable in polynomial time for any fixed connectivity requirement k. On the other hand the minimization problem is hard to approximate within a factor of 2 1−e , unless NP ⊆ DTIME(n). The number of graphs in our reduction depends on n. If instead the number of graphs is fixed, we show there is an e > 0 for which the minimization problem is not approximable within g1/2−e for any fixed number g of graphs unless NP = ZPP . These hardness results for the minimization problem hold even in the simple cases where the connectivity requirement is one and there are either just two terminal nodes or every node is a terminal node. We remark that our results extend to case where more robust variants of the quality measure are used.

Behavioural, molecular, and functional analyses revealed that fever contributes to induction and resolution phases of acute inflammation, through enhancement of antimicrobial defences, increased efficiency in inflammation control, and improved wound repair.

When data from several independent Markov chains are aggregated over each time point, least square estimation of transition probabilities faces the problem of multi-collinearity. We propose here an estimation procedure which involves use of ridge regression for the ordinary least square estimators. Performance of this estimator is then compared with that of the ordinary least squares.

Guided by Kraemer et al.'s (Psychological Methods, 3:257-271, 1999) framework for measuring the potency of risk factors, we sought to improve on the classification accuracy reported in Petras et al. (Journal of the American Academy of Child and Adolescent Psychiatry 43:88-96, 2004a) and Petras et al. (Journal of the American Academy of Child and Adolescent Psychiatry 44:790-797, 2005) by using multiple as opposed to single point in time assessments of early aggressive and disruptive behavior in the classification of youth who would likely benefit from targeted preventive interventions. Different from Petras et al. (2004a, 2005), the outcome used in this study included serious antisocial behavior in young adulthood as well as in adolescence. Among males, the use of multiple time points did not yield greater classification accuracy than the highest single time points, that is, third and fifth grades. For females, although fifth grade represented the best single time point in terms of classification accuracy, no significant association was found between earlier time points and the later outcome, rendering a test of the multiple time points hypothesis moot. The findings presented in this study have strong implications for the design of targeted intervention for violence prevention, indicating that the screening quality based on aggression ratings during the elementary years is rather modest, particularly for females.