Schätzung gemischter und verbundener Modelle mittels statistischer Boostingverfahren

Linear mixed models (LMM) are a widely used tool for clustered or longitudinal data to account for cluster-specific correlations. In addition, joint models (JM) are appropriate whenever a time-to-event outcome is measured alongside longitudinal data in order to quantify possible associations between these two processes. Performing variable selection or making these kinds of models feasible for high-dimensional data structures is subject to current research. Methods from statistical learning, so called boosting algorithms, are a promising approach towards this matter. While these methods are barely developed for joint models, existing approaches for mixed models tend to yield biased results. Gradient boosting and likelihood-based boosting techniques from the field of statistical learning are developed for mixed and joint models in order to perform variable selection and enable inference in high-dimensional data structures. Regarding mixed models, existing approaches are improved concerning more accurate estimates and decreased computational burden. In the case of joint models, a likelihood-based boosting algorithm enables regularized parameter estimation and a gradient boosting approach enables allocation of variables within the model. All algorithms are compared to each state-of-the-art approach of the respective boosting category based on simulation studies and real-world applications. For mixed models, especially the likelihood-based boosting approach yields outstanding results regarding variable. The estimation accuracy is in low- as well as in high-dimensional scenarios very good, similar to the algorithm focusing on gradient boosting. The boosting approaches for joint models lead to stronger shrinkage of effects, but offer good results regarding variable selection and allocation. Overall, the developed methods resemble a far more reliable and, due to the strong reduction of computation time, applicable regularization method for mixed models. Regarding joint models, they offer the first learning-based approach for effect estimation of time-varying covariates in survival analysis as well as allocating variables to the single predictors.Lineare gemischte Modelle (LMM) sind ein weitverbreitetes Analysewerkzeug für Daten aus Cluster-Stichproben oder Längsschnittstudien um gruppenspezifische Korrelationen zu berücksichtigen. Werden Ereigniszeiten parallel zu Längsschnittdaten erhoben, so können verbundene Modelle (Joint Models, JM) mögliche Beziehungen zwischen diesen beiden Prozessen abbilden. Wie diese Modelle für Variablenselektion oder hochdimensionale Daten genutzt werden können, ist Gegenstand aktueller Forschung. Statistische Lernverfahren, sogenannte Boosting-Algorithmen, sind ein vielversprechender Ansatz. Während diese Methoden für verbundene Modelle bislang kaum entwickelt wurden, liefern bereits existierende Verfahren für gemischte Modelle teilweise fehlerhafte Ergebnisse. Es werden diverse statistische Lernverfahren (Gradienten-Boosting und Likelihood-basiertes Boosting) entwickelt, um bei gemischten und verbundenen Modellen Variablen zu selektieren, sowie statistische Inferenz bei hochdimensionalen Datensätzen zu ermöglichen. Existierende Lernverfahren für gemischte Modelle werden im Hinblick auf die Genauigkeit der Schätzer und die Rechenzeit deutlich verbessert. Für verbundene Modelle wird ein Likelihood-basierter Boosting-Ansatz mit regularisierter Parameterschätzung entwickelt. Eine auf Gradientenboosting beruhende Methode hingegen ermöglicht die Zuordnung von Variablen innerhalb eines verbundenen Modells. Anhand von Simulationsstudien und realen Datenbeispielen werden die neuen Boosting-Algorithmen mit aktuellen Verfahren aus der jeweiligen Boosting-Kategorie verglichen. Im Falle gemischter Modelle liefert besonders der Likelihood-basierte Boosting-Algorithmus herausragende Ergebnisse bezüglich der Variablenselektion. Die Genauigkeit der Effektschätzer ist sowohl in niedrig- als auch in hochdimensionalen Szenarien sehr gut, ähnlich zum Gradienten-Boosting. Die Boosting-Methoden für verbundene Modelle führen zu einer stärkeren Effektschrumpfung, liefern dafür jedoch gute Ergebnisse bezüglich Selektion und Zuordnung der Variablen. Die entwickelten Verfahren stellen eine weitaus zuverlässigere und durch die deutlich reduzierte Rechenzeit auch zugänglichere Regularisierungsmethode für gemischte Modelle dar. Bei verbundenen Modellen ist es nun erstmals möglich, die Effekte zeitveränderlicher Kovariablen auf die Überlebenszeit mittels statistischer Lernverfahren zu schätzen sowie Variablen den einzelnen Prädiktoren zuzuordnen

Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques

Modeling longitudinal data (e.g., biomarkers) and the risk for events separately leads to a loss of information and bias, even though the underlying processes are related to each other. Hence, the popularity of joint models for longitudinal and time-to-event-data has grown rapidly in the last few decades. However, it is quite a practical challenge to specify which part of a joint model the single covariates should be assigned to as this decision usually has to be made based on background knowledge. In this work, we combined recent developments from the field of gradient boosting for distributional regression in order to construct an allocation routine allowing researchers to automatically assign covariates to the single sub-predictors of a joint model. The procedure provides several well-known advantages of model-based statistical learning tools, as well as a fast-performing allocation mechanism for joint models, which is illustrated via empirical results from a simulation study and a biomedical application

Development of a new sternal dehiscence prediction scale for decision making in sternal closure techniques after cardiac surgery

Abstract Background After sternotomy, the spectrum for sternal osteosynthesis comprises standard wiring and more complex techniques, like titanium plating. The aim of this study is to develop a predictive risk score that evaluates the risk of sternum instability individually. The surgeon may then choose an appropriate sternal osteosynthesis technique that is risk- adjusted as well as cost-effective. Methods Data from 7.173 patients operated via sternotomy for all cardiovascular indications from 2008 until 2017 were retrospectively analyzed. Sternal dehiscence occurred in 2.5% of patients (n = 176). A multivariable analysis model examined pre- and intraoperative factors. A multivariable logistic regression model and a backward elimination based on the Akaike Information Criterion (AIC) a logistic model were selected. Results The model showed good sensitivity and specificity (area under the receiver-operating characteristic curve, AUC: 0.76) and several predictors of sternal instability could be evaluated. Multivariable logistic regression showed the highest Odds Ratios (OR) for reexploration (OR 6.6, confidence interval, CI [4.5–9.5], p  35 kg/m2) (OR 4.23, [CI 2.4–7.3], p < 0.001), insulin-dependent diabetes mellitus (IDDM) (OR 2.2, CI [1.5–3.2], p = 0.01), smoking (OR 2.03, [CI 1.3–3.08], p = 0.001). After weighting the probability of sternum dehiscence with each factor, a risk score model was proposed scaling from − 1 to 5 points. This resulted in a risk score ranging up to 18 points, with an estimated risk for sternum complication up to 74%. Conclusions A weighted scoring system based on individual risk factors was specifically created to predict sternal dehiscence. High-scoring patients should receive additive closure techniques

Safety of medical compression stockings in patients with diabetes mellitus or peripheral arterial disease

Introduction Compression therapy is highly effective in the treatment of many venous diseases, including leg edema. However, its relevance in patients with peripheral arterial disease (PAD) or diabetes mellitus is critically discussed. The aim of the present study was to assess the influence of compression therapy on microperfusion and its safety in patients with PAD or diabetes mellitus.Research design and methods A prospective analysis of 94 consecutive patients (44 patients with diabetes, 45 patients with PAD and 5 healthy controls) undergoing medical compression therapy was performed. Microperfusion was assessed by a combined method of white light tissue spectrometry and laser Doppler flowmetry under medical compression therapy (classes I and II), in different body positions (supine, sitting, standing and elevated position of the leg) and at different locations (great toe, lateral ankle and calf).Results During the entire study, no compression-related adverse events occurred. Evaluation of microcirculation parameters (oxygen saturation of hemoglobin and flow) at the different locations and in sitting and standing positions (patients with diabetes and PAD) under compression therapy classes I and II revealed no tendency for reduced microperfusion in both groups. In contrast, in the elevated leg position, all mean perfusion values decreased in the PAD and diabetes groups. However, the same effect was seen in the healthy subgroup.Conclusions In consideration of the present inclusion criteria, use of medical compression stockings is safe and feasible in patients with diabetes or PAD. This study did not find relevant impairment of microperfusion parameters under compression therapy in these patient subgroups in physiologic body positions.Trial registration number NCT03384758

Passive Exercise of the Hind Limbs after Complete Thoracic Transection of the Spinal Cord Promotes Cortical Reorganization

Physical exercise promotes neural plasticity in the brain of healthy subjects and modulates pathophysiological neural plasticity after sensorimotor loss, but the mechanisms of this action are not fully understood. After spinal cord injury, cortical reorganization can be maximized by exercising the non-affected body or the residual functions of the affected body. However, exercise per se also produces systemic changes – such as increased cardiovascular fitness, improved circulation and neuroendocrine changes – that have a great impact on brain function and plasticity. It is therefore possible that passive exercise therapies typically applied below the level of the lesion in patients with spinal cord injury could put the brain in a more plastic state and promote cortical reorganization. To directly test this hypothesis, we applied passive hindlimb bike exercise after complete thoracic transection of the spinal cord in adult rats. Using western blot analysis, we found that the level of proteins associated with plasticity – specifically ADCY1 and BDNF – increased in the somatosensory cortex of transected animals that received passive bike exercise compared to transected animals that received sham exercise. Using electrophysiological techniques, we then verified that neurons in the deafferented hindlimb cortex increased their responsiveness to tactile stimuli delivered to the forelimb in transected animals that received passive bike exercise compared to transected animals that received sham exercise. Passive exercise below the level of the lesion, therefore, promotes cortical reorganization after spinal cord injury, uncovering a brain-body interaction that does not rely on intact sensorimotor pathways connecting the exercised body parts and the brain