Results of the simulation study.

<p>Comparison of the discriminatory power resulting from boosting the -index and competing approaches. Numbers refer to the median value and interquartile range (in parentheses) of the final on 100 simulation runs. The <i>true</i>-index refers to the discriminatory power resulting from the true combination of predictors with known coefficients. The amount of pre-selected genes is denoted as , is the size of the training samples and <i>cens.</i> refers to the censoring rate.</p

Comparing the discriminatory power of biomarker combinations to predict the time to distant metastases resulting from the proposed -index boosting approach with competing estimation schemes.

<p>The plot on the left refers to the Desmedt et al. data, whereas the plot on the right presents results from the van de Vijver et al. data. All biomarker combinations were optimized via the corresponding algorithms based on the same 100 learning samples. Boxplots represent the empirical distribution of the resulting on corresponding test samples. The dotted line corresponds to the median -index resulting from the new approach.</p

Coefficient estimates for pre-selected markers obtained from 100 simulation runs.

<p>The marker combinations were optimized via gradient boosting based on training samples of size (left) and (right). Boxplots represent the empirical distribution of the resulting coefficients. Only markers to had an actual effect on the survival time.</p

Simulation results for the discriminatory power obtained via the proposed -index boosting approach and competing Cox-based estimation schemes.

<p>The marker combinations were optimized via the different approaches based on training samples of size (left) and (right). Boxplots represent the empirical distribution of the resulting on corresponding test samples. The dotted line corresponds to the discriminatory power resulting from the <i>true</i> combination of predictors with known coefficients.</p

Additional file 1 of Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection

Supporting Information. The document provides a more detailed description of the presented approach and its implementation. Furthermore, it includes a worked-out example on how C-index boosting with stability selection can be applied in practice. (PDF 216 kb

Additional file 2 of Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection

R Code. This R-file provides the underlying functions to reproduce the results of the simulation and the breast cancer analysis. (R 21 kb

Effect of a Multi-Dimensional and Inter-Sectoral Intervention on the Adherence of Psychiatric Patients

<div><p>Background</p><p>In psychiatry, hospital stays and transitions to the ambulatory sector are susceptible to major changes in drug therapy that lead to complex medication regimens and common non-adherence among psychiatric patients. A multi-dimensional and inter-sectoral intervention is hypothesized to improve the adherence of psychiatric patients to their pharmacotherapy.</p><p>Methods</p><p>269 patients from a German university hospital were included in a prospective, open, clinical trial with consecutive control and intervention groups. Control patients (09/2012-03/2013) received usual care, whereas intervention patients (05/2013-12/2013) underwent a program to enhance adherence during their stay and up to three months after discharge. The program consisted of therapy simplification and individualized patient education (multi-dimensional component) during the stay and at discharge, as well as subsequent phone calls after discharge (inter-sectoral component). Adherence was measured by the “Medication Adherence Report Scale” (MARS) and the “Drug Attitude Inventory” (DAI).</p><p>Results</p><p>The improvement in the MARS score between admission and three months after discharge was 1.33 points (95% CI: 0.73–1.93) higher in the intervention group compared to controls. In addition, the DAI score improved 1.93 points (95% CI: 1.15–2.72) more for intervention patients.</p><p>Conclusion</p><p>These two findings indicate significantly higher medication adherence following the investigated multi-dimensional and inter-sectoral program.</p><p>Trial Registration</p><p>German Clinical Trials Register <a href="https://drks-neu.uniklinik-freiburg.de/DRKS00006358" target="_blank">DRKS00006358</a></p></div

Trial profile.

<p>ITT, Intention To Treat. Flow chart of control and intervention patients from allocation to group to analysis of date.</p

Probability density functions for beta distributions.

<p>Probability density functions for beta distributions with (left) and (right).</p

MARS scores.

<p>Development of MARS Scores with median and interquartile ranges for control and intervention group from baseline to follow-up three months after discharge. MARS, Medication Adherence Report Scale.</p