Data-Driven Simulations to Assess the Impact of Study Imperfections in Time-to-Event Analyses.

Quantitative bias analysis (QBA) permits assessment of the expected impact of various imperfections of the available data on the results and conclusions of a particular real-world study. This article extends QBA methodology to multivariable time-to-event analyses with right-censored endpoints, possibly including time-varying exposures or covariates. The proposed approach employs data-driven simulations, which preserve important features of the data at hand while offering flexibility in controlling the parameters and assumptions that may affect the results. First, the steps required to perform data-driven simulations are described, and then two examples of real-world time-to-event analyses illustrate their implementation and the insights they may offer. The first example focuses on the omission of an important time-invariant predictor of the outcome in a prognostic study of cancer mortality, and permits separating the expected impact of confounding bias from non-collapsibility. The second example assesses how imprecise timing of an interval-censored event - ascertained only at sparse times of clinic visits - affects its estimated association with a time-varying drug exposure. The simulation results also provide a basis for comparing the performance of two alternative strategies for imputing the unknown event times in this setting. The R scripts that permit the reproduction of our examples are provided.

Explaining the optimistic performance evaluation of newly proposed methods: A cross-design validation experiment.

The constant development of new data analysis methods in many fields of research is accompanied by an increasing awareness that these new methods often perform better in their introductory paper than in subsequent comparison studies conducted by other researchers. We attempt to explain this discrepancy by conducting a systematic experiment that we call "cross-design validation of methods". In the experiment, we select two methods designed for the same data analysis task, reproduce the results shown in each paper, and then reevaluate each method based on the study design (i.e., datasets, competing methods, and evaluation criteria) that was used to show the abilities of the other method. We conduct the experiment for two data analysis tasks, namely cancer subtyping using multiomic data and differential gene expression analysis. Three of the four methods included in the experiment indeed perform worse when they are evaluated on the new study design, which is mainly caused by the different datasets. Apart from illustrating the many degrees of freedom existing in the assessment of a method and their effect on its performance, our experiment suggests that the performance discrepancies between original and subsequent papers may not only be caused by the nonneutrality of the authors proposing the new method but also by differences regarding the level of expertise and field of application. Authors of new methods should thus focus not only on a transparent and extensive evaluation but also on comprehensive method documentation that enables the correct use of their methods in subsequent studies.

Stereotactic radiosurgery versus whole-brain radiotherapy in patients with 4-10 brain metastases: A nonrandomized controlled trial.

BACKGROUND AND PURPOSE: There is no randomized evidence comparing whole-brain radiotherapy (WBRT) and stereotactic radiosurgery (SRS) in the treatment of multiple brain metastases. This prospective nonrandomized controlled single arm trial attempts to reduce the gap until prospective randomized controlled trial results are available. MATERIAL AND METHODS: We included patients with 4-10 brain metastases and ECOG performance status ≤ 2 from all histologies except small-cell lung cancer, germ cell tumors, and lymphoma. The retrospective WBRT-cohort was selected 2:1 from consecutive patients treated within 2012-2017. Propensity-score matching was performed to adjust for confounding factors such as sex, age, primary tumor histology, dsGPA score, and systemic therapy. SRS was performed using a LINAC-based single-isocenter technique employing prescription doses from 15-20Gyx1 at the 80% isodose line. The historical control consisted of equivalent WBRT dose regimens of either 3Gyx10 or 2.5Gyx14. RESULTS: Patients were recruited from 2017-2020, end of follow-up was July 1st, 2021. 40 patients were recruited to the SRS-cohort and 70 patients were eligible as controls in the WBRT-cohort. Median OS, and iPFS were 10.4 months (95%-CI 9.3-NA) and 7.1 months (95%-CI 3.9-14.2) for the SRS-cohort, and 6.5 months (95%-CI 4.9-10.4), and 5.9 months (95%-CI 4.1-8.8) for the WBRT-cohort, respectively. Differences were non-significant for OS (HR: 0.65; 95%-CI 0.40-1.05; P =.074) and iPFS (P =.28). No grade III toxicities were observed in the SRS-cohort. CONCLUSION: This trial did not meet its primary endpoint as the OS-improvement of SRS compared to WBRT was non-significant and thus superiority could not be proven. Prospective randomized trials in the era of immunotherapy and targeted therapies are warranted.

Relationship between age at initiation of cysteamine treatment, adherence with therapy, and glomerular kidney function in infantile nephropathic cystinosis.

Infantile nephropathic cystinosis, due to impaired transport of cystine out of lysosomes, occurs with an incidence of 1 in 100-200,000 live births. It is characterized by renal Fanconi syndrome in the first year of life and glomerular dysfunction progression to end-stage kidney disease by approximately 10 years of age. Treatment with oral cysteamine therapy helps preserve glomerular function, but affected individuals eventually require kidney replacement therapy. This is because glomerular damage had already occurred by the time a child is diagnosed with cystinosis, typically in the second year of life. We performed a retrospective multicenter study to investigate the impact of initiating cysteamine treatment within the first 2 months of life in some infants and comparing two different levels of adherence in patients diagnosed at the typical age. We collected 3983 data points from 55 patients born between 1997 and 2020; 52 patients with 1592 data points could be further evaluated. These data were first analyzed by dividing the patient cohort into three groups: (i) standard treatment start with good adherence, (ii) standard treatment start with less good adherence, and (iii) early treatment start. At every age, mean estimated glomerular filtration rate (eGFR) was higher in early-treated patients than in later-treated patients. Second, a generalized additive mixed model (GAMM) was applied showing that patients with initiation of treatment before 2 months of age are expected to have a 34 ml/min/1.73 m2 higher eGFR than patients with later treatment start while controlling for adherence and patients' age. These data strongly suggest that oral cysteamine treatment initiated within 2 months of birth preserves kidney function in infantile nephropathic cystinosis and provide evidence of the utility of newborn screening for this disease.

COMPANION: development of a patient-centred complexity and casemix classification for adult palliative care patients based on needs and resource use - a protocol for a cross-sectional multi-centre study.

BACKGROUND: A casemix classification based on patients' needs can serve to better describe the patient group in palliative care and thus help to develop adequate future care structures and enable national benchmarking and quality control. However, in Germany, there is no such an evidence-based system to differentiate the complexity of patients' needs in palliative care. Therefore, the study aims to develop a patient-oriented, nationally applicable complexity and casemix classification for adult palliative care patients in Germany. METHODS: COMPANION is a mixed-methods study with data derived from three subprojects. Subproject 1: Prospective, cross-sectional multi-centre study collecting data on patients' needs which reflect the complexity of the respective patient situation, as well as data on resources that are required to meet these needs in specialist palliative care units, palliative care advisory teams, and specialist palliative home care. Subproject 2: Qualitative study including the development of a literature-based preliminary list of characteristics, expert interviews, and a focus group to develop a taxonomy for specialist palliative care models. Subproject 3: Multi-centre costing study based on resource data from subproject 1 and data of study centres. Data and results from the three subprojects will inform each other and form the basis for the development of the casemix classification. Ultimately, the casemix classification will be developed by applying Classification and Regression Tree (CART) analyses using patient and complexity data from subproject 1 and patient-related cost data from subproject 3. DISCUSSION: This is the first multi-centre costing study that integrates the structure and process characteristics of different palliative care settings in Germany with individual patient care. The mixed methods design and variety of included data allow for the development of a casemix classification that reflect on the complexity of the research subject. The consecutive inclusion of all patients cared for in participating study centres within the time of data collection allows for a comprehensive description of palliative care patients and their needs. A limiting factor is that data will be collected at least partly during the COVID-19 pandemic and potential impact of the pandemic on health care and the research topic cannot be excluded. TRIAL REGISTRATION: German Register for Clinical Studies trial registration number: DRKS00020517 .

Large-scale benchmark study of survival prediction methods using multi-omics data.

Multi-omics data, that is, datasets containing different types of high-dimensional molecular variables, are increasingly often generated for the investigation of various diseases. Nevertheless, questions remain regarding the usefulness of multi-omics data for the prediction of disease outcomes such as survival time. It is also unclear which methods are most appropriate to derive such prediction models. We aim to give some answers to these questions through a large-scale benchmark study using real data. Different prediction methods from machine learning and statistics were applied on 18 multi-omics cancer datasets (35 to 1000 observations, up to 100 000 variables) from the database 'The Cancer Genome Atlas' (TCGA). The considered outcome was the (censored) survival time. Eleven methods based on boosting, penalized regression and random forest were compared, comprising both methods that do and that do not take the group structure of the omics variables into account. The Kaplan-Meier estimate and a Cox model using only clinical variables were used as reference methods. The methods were compared using several repetitions of 5-fold cross-validation. Uno's C-index and the integrated Brier score served as performance metrics. The results indicate that methods taking into account the multi-omics structure have a slightly better prediction performance. Taking this structure into account can protect the predictive information in low-dimensional groups-especially clinical variables-from not being exploited during prediction. Moreover, only the block forest method outperformed the Cox model on average, and only slightly. This indicates, as a by-product of our study, that in the considered TCGA studies the utility of multi-omics data for prediction purposes was limited. Contact:moritz.herrmann@stat.uni-muenchen.de, +49 89 2180 3198 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online. All analyses are reproducible using R code freely available on Github.

Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study.

BACKGROUND: Prognostic models based on high-dimensional omics data generated from clinical patient samples, such as tumor tissues or biopsies, are increasingly used for prognosis of radio-therapeutic success. The model development process requires two independent discovery and validation data sets. Each of them may contain samples collected in a single center or a collection of samples from multiple centers. Multi-center data tend to be more heterogeneous than single-center data but are less affected by potential site-specific biases. Optimal use of limited data resources for discovery and validation with respect to the expected success of a study requires dispassionate, objective decision-making. In this work, we addressed the impact of the choice of single-center and multi-center data as discovery and validation data sets, and assessed how this impact depends on the three data characteristics signal strength, number of informative features and sample size. METHODS: We set up a simulation study to quantify the predictive performance of a model trained and validated on different combinations of in silico single-center and multi-center data. The standard bioinformatical analysis workflow of batch correction, feature selection and parameter estimation was emulated. For the determination of model quality, four measures were used: false discovery rate, prediction error, chance of successful validation (significant correlation of predicted and true validation data outcome) and model calibration. RESULTS: In agreement with literature about generalizability of signatures, prognostic models fitted to multi-center data consistently outperformed their single-center counterparts when the prediction error was the quality criterion of interest. However, for low signal strengths and small sample sizes, single-center discovery sets showed superior performance with respect to false discovery rate and chance of successful validation. CONCLUSIONS: With regard to decision making, this simulation study underlines the importance of study aims being defined precisely a priori. Minimization of the prediction error requires multi-center discovery data, whereas single-center data are preferable with respect to false discovery rate and chance of successful validation when the expected signal or sample size is low. In contrast, the choice of validation data solely affects the quality of the estimator of the prediction error, which was more precise on multi-center validation data.

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection.

Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question. We denote this latter type of uncertainty as method uncertainty. It results from all the choices to be made for an analysis, for example, decisions related to data preparation, method choice, or model selection. In medical sciences, a large part of omics research is focused on the identification of molecular biomarkers, which can either be performed through ranking or by selection from among a large number of candidates. In this paper, we introduce a general resampling-based framework to quantify and compare sampling and method uncertainty. For illustration, we apply this framework to different scenarios related to the selection and ranking of omics biomarkers in the context of acute myeloid leukemia: variable selection in multivariable regression using different types of omics markers, the ranking of biomarkers according to their predictive performance, and the identification of differentially expressed genes from RNA-seq data. For all three scenarios, our findings suggest highly unstable results when the same analysis strategy is applied to two independent samples, indicating high sampling uncertainty and a comparatively smaller, but non-negligible method uncertainty, which strongly depends on the methods being compared.

A plea for taking all available clinical information into account when assessing the predictive value of omics data.

BACKGROUND: Omics data can be very informative in survival analysis and may improve the prognostic ability of classical models based on clinical risk factors for various diseases, for example breast cancer. Recent research has focused on integrating omics and clinical data, yet has often ignored the need for appropriate model building for clinical variables. Medical literature on classical prognostic scores, as well as biostatistical literature on appropriate model selection strategies for low dimensional (clinical) data, are often ignored in the context of omics research. The goal of this paper is to fill this methodological gap by investigating the added predictive value of gene expression data for models using varying amounts of clinical information. METHODS: We analyze two data sets from the field of survival prognosis of breast cancer patients. First, we construct several proportional hazards prediction models using varying amounts of clinical information based on established medical knowledge. These models are then used as a starting point (i.e. included as a clinical offset) for identifying informative gene expression variables using resampling procedures and penalized regression approaches (model based boosting and the LASSO). In order to assess the added predictive value of the gene signatures, measures of prediction accuracy and separation are examined on a validation data set for the clinical models and the models that combine the two sources of information. RESULTS: For one data set, we do not find any substantial added predictive value of the omics data when compared to clinical models. On the second data set, we identify a noticeable added predictive value, however only for scenarios where little or no clinical information is included in the modeling process. We find that including more clinical information can lead to a smaller number of selected omics predictors. CONCLUSIONS: New research using omics data should include all available established medical knowledge in order to allow an adequate evaluation of the added predictive value of omics data. Including all relevant clinical information in the analysis might also lead to more parsimonious models. The developed procedure to assess the predictive value of the omics data can be readily applied to other scenarios.

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.

BACKGROUND: The inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind. RESULTS: In this paper we present priority-Lasso, an intuitive and practical analysis strategy for building prediction models based on Lasso that takes such block structures into account. It requires the definition of a priority order of blocks of data. Lasso models are calculated successively for every block and the fitted values of every step are included as an offset in the fit of the next step. We apply priority-Lasso in different settings on an acute myeloid leukemia (AML) dataset consisting of clinical variables, cytogenetics, gene mutations and expression variables, and compare its performance on an independent validation dataset to the performance of standard Lasso models. CONCLUSION: The results show that priority-Lasso is able to keep pace with Lasso in terms of prediction accuracy. Variables of blocks with higher priorities are favored over variables of blocks with lower priority, which results in easily usable and transportable models for clinical practice.

Mediation analysis reveals common mechanisms of RUNX1 point mutations and RUNX1/RUNX1T1 fusions influencing survival of patients with acute myeloid leukemia.

Alterations of RUNX1 in acute myeloid leukemia (AML) are associated with either a more favorable outcome in the case of the RUNX1/RUNX1T1 fusion or unfavorable prognosis in the case of point mutations. In this project we aimed to identify genes responsible for the observed differences in outcome that are common to both RUNX1 alterations. Analyzing four AML gene expression data sets (n = 1514), a total of 80 patients with RUNX1/RUNX1T1 and 156 patients with point mutations in RUNX1 were compared. Using the statistical tool of mediation analysis we identified the genes CD109, HOPX, and KIAA0125 as candidates for mediator genes. In an analysis of an independent validation cohort, KIAA0125 again showed a significant influence with respect to the impact of the RUNX1/RUNX1T1 fusion. While there were no significant results for the other two genes in this smaller validation cohort, the observed relations linked with mediation effects (i.e., those between alterations, gene expression and survival) were almost without exception as strong as in the main analysis. Our analysis demonstrates that mediation analysis is a powerful tool in the identification of regulative networks in AML subgroups and could be further used to characterize the influence of genetic alterations.

Guest Editorial-Special Collection Topic: Statistical Systems Theory in Cancer Modeling, Diagnosis, and Therapy.

Cancer is a systems disease involving mutations and altered regulation. This supplement treats cancer research as it pertains to 3 systems issues of an inherently statistical nature: regulatory modeling and information processing, diagnostic classification, and therapeutic intervention and control. Topics of interest include (but are not limited to) multiscale modeling, gene/protein transcriptional regulation, dynamical systems, pharmacokinetic/pharmacodynamic modeling, compensatory regulation, feedback, apoptotic and proliferative control, copy number-expression interaction, integration of different feature types, error estimation, and reproducibility. We are especially interested in how the above issues relate to the extremely high-dimensional data sets and small- to moderate-sized data sets typically involved in cancer research, for instance, their effect on statistical power, inference accuracy, and multiple comparisons.

Independent validation of a new reirradiation risk score (RRRS) for glioma patients predicting post-recurrence survival: A multicenter DKTK/ROG analysis.

BACKGROUND AND PURPOSE: Reirradiation (reRT) is a valid option with considerable efficacy in patients with recurrent high-grade glioma, but it is still not known which patients might be optimal candidates for a second course of irradiation. This study validated a newly developed prognostic score independently in an external patient cohort. MATERIAL AND METHODS: The reRT risk score (RRRS) is based on a linear combination of initial histology, clinical performance status, and age derived from a multivariable model of 353 patients. This score can predict post-recurrence survival (PRS) after reRT. The validation dataset consisted of 212 patients. RESULTS: The RRRS differentiates three prognostic groups. Discrimination and calibration were maintained in the validation group. Median PRS times in the development cohort for the good/intermediate/poor risk categories were 14.2, 9.1, and 5.3 months, respectively. The respective groups within the validation cohort displayed median PRS times of 13.8, 8.8, and 3.8 months, respectively. Uno's C for development data was 0.64 (CI: 0.60-0.69) and for validation data 0.63 (CI: 0.58-0.68). CONCLUSIONS: The RRRS has been successfully validated in an independent patient cohort. This linear combination of three easily determined clinicopathological factors allows for a reliable classification of patients and may be used as stratification factor for future trials.

IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data.

As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed "omics" data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.

Interleukin-22 is elevated in lavage from patients with lung cancer and other pulmonary diseases.

BACKGROUND: Interleukin-22 (IL-22) is involved in lung diseases such as pneumonia, asthma and lung cancer. Lavage mirrors the local environment, and may provide insights into the presence and role of IL-22 in patients. METHODS: Bronchoscopic lavage (BL) samples (n = 195, including bronchoalveolar lavage and bronchial washings) were analysed for IL-22 using an enzyme-linked immunosorbent assay. Clinical characteristics and parameters from lavage and serum were correlated with lavage IL-22 concentrations. RESULTS: IL-22 was higher in lavage from patients with lung disease than in controls (38.0 vs 15.3 pg/ml, p < 0.001). Patients with pneumonia and lung cancer had the highest concentrations (48.9 and 33.0 pg/ml, p = 0.009 and p < 0.001, respectively). IL-22 concentration did not correlate with systemic inflammation. IL-22 concentrations did not relate to any of the analysed cell types in BL indicating a potential mixed contribution of different cell populations to IL-22 production. CONCLUSIONS: Lavage IL-22 concentrations are high in patients with lung cancer but do not correlate with systemic inflammation, thus suggesting that lavage IL-22 may be related to the underlying malignancy. Our results suggest that lavage may represent a distinct compartment where the role of IL-22 in thoracic malignancies can be studied.

Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysis.

BACKGROUND: Identification of microorganisms in positive blood cultures still relies on standard techniques such as Gram staining followed by culturing with definite microorganism identification. Alternatively, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or the analysis of headspace volatile compound (VC) composition produced by cultures can help to differentiate between microorganisms under experimental conditions. This study assessed the efficacy of volatile compound based microorganism differentiation into Gram-negatives and -positives in unselected positive blood culture samples from patients. METHODS: Headspace gas samples of positive blood culture samples were transferred to sterilized, sealed, and evacuated 20 ml glass vials and stored at -30 °C until batch analysis. Headspace gas VC content analysis was carried out via an auto sampler connected to an ion-molecule reaction mass spectrometer (IMR-MS). Measurements covered a mass range from 16 to 135 u including CO2, H2, N2, and O2. Prediction rules for microorganism identification based on VC composition were derived using a training data set and evaluated using a validation data set within a random split validation procedure. RESULTS: One-hundred-fifty-two aerobic samples growing 27 Gram-negatives, 106 Gram-positives, and 19 fungi and 130 anaerobic samples growing 37 Gram-negatives, 91 Gram-positives, and two fungi were analysed. In anaerobic samples, ten discriminators were identified by the random forest method allowing for bacteria differentiation into Gram-negative and -positive (error rate: 16.7 % in validation data set). For aerobic samples the error rate was not better than random. CONCLUSIONS: In anaerobic blood culture samples of patients IMR-MS based headspace VC composition analysis facilitates bacteria differentiation into Gram-negative and -positive.

Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives.

Reliable risk assessment of frequent, but treatable diseases and disorders has considerable clinical and socio-economic relevance. However, as these conditions usually originate from a complex interplay between genetic and environmental factors, precise prediction remains a considerable challenge. The current progress in genotyping technology has resulted in a substantial increase of knowledge regarding the genetic basis of such diseases and disorders. Consequently, common genetic risk variants are increasingly being included in epidemiological models to improve risk prediction. This work reviews recent high-quality publications targeting the prediction of common complex diseases. To be included in this review, articles had to report both, numerical measures of prediction performance based on traditional (non-genetic) risk factors, as well as measures of prediction performance when adding common genetic variants to the model. Systematic PubMed-based search finally identified 55 eligible studies. These studies were compared with respect to the chosen approach and methodology as well as results and clinical impact. Phenotypes analysed included tumours, diabetes mellitus, and cardiovascular diseases. All studies applied one or more statistical measures reporting on calibration, discrimination, or reclassification to quantify the benefit of including SNPs, but differed substantially regarding the methodological details that were reported. Several examples for improved risk assessments by considering disease-related SNPs were identified. Although the add-on benefit of including SNP genotyping data was mostly moderate, the strategy can be of clinical relevance and may, when being paralleled by an even deeper understanding of disease-related genetics, further explain the development of enhanced predictive and diagnostic strategies for complex diseases.

Publication Bias in Methodological Computational Research.

The problem of publication bias has long been discussed in research fields such as medicine. There is a consensus that publication bias is a reality and that solutions should be found to reduce it. In methodological computational research, including cancer informatics, publication bias may also be at work. The publication of negative research findings is certainly also a relevant issue, but has attracted very little attention to date. The present paper aims at providing a new formal framework to describe the notion of publication bias in the context of methodological computational research, facilitate and stimulate discussions on this topic, and increase awareness in the scientific community. We report an exemplary pilot study that aims at gaining experiences with the collection and analysis of information on unpublished research efforts with respect to publication bias, and we outline the encountered problems. Based on these experiences, we try to formalize the notion of publication bias.

Gender mismatch in allograft aortic valve surgery.

OBJECTIVES: Factors influencing allograft valve failure in adult patients are still under discussion. There is evidence in heart transplantation that gender mismatching may influence patient outcome. In case of aortic valve replacement with a homograft valve, gender matching is not performed routinely. The aim of this study was to investigate the impact of gender mismatch of human aortic homografts. METHODS: Between June 1992 and August 2009, 363 adult patients received aortic or pulmonary homografts in the aortic position. For 350 patients, the following donor-recipient patterns could be investigated: male recipient and male donor (n = 193), male recipient and female donor (n = 64), female recipient and male donor (n = 47), female recipient and female donor (n = 46). RESULTS: The overall mortality rate was 18.5%. In total, 95 patients (27.1%) needed reoperation during follow-up (mean overall follow-up time was 8.1 years). Event-free survival (i.e. survival without reoperation) of recipients of gender-incompatible homografts was not significantly different compared with recipients who received gender-compatible homografts. Echocardiographic performance of the homograft valve over time was not significantly worse in case of gender incompatibility than in case of gender compatibility. CONCLUSIONS: There was no significant difference between gender-mismatched and gender-matched allografts regarding death, need for reoperation and echocardiographic allograft function during follow-up. Limitations of this study are its retrospective design and the lack of immunohistochemical data to determine the presence of viable cells in explanted valves.

Added predictive value of omics data: specific issues related to validation illustrated by two case studies.

BACKGROUND: In the last years, the importance of independent validation of the prediction ability of a new gene signature has been largely recognized. Recently, with the development of gene signatures which integrate rather than replace the clinical predictors in the prediction rule, the focus has been moved to the validation of the added predictive value of a gene signature, i.e. to the verification that the inclusion of the new gene signature in a prediction model is able to improve its prediction ability. METHODS: The high-dimensional nature of the data from which a new signature is derived raises challenging issues and necessitates the modification of classical methods to adapt them to this framework. Here we show how to validate the added predictive value of a signature derived from high-dimensional data and critically discuss the impact of the choice of methods on the results. RESULTS: The analysis of the added predictive value of two gene signatures developed in two recent studies on the survival of leukemia patients allows us to illustrate and empirically compare different validation techniques in the high-dimensional framework. CONCLUSIONS: The issues related to the high-dimensional nature of the omics predictors space affect the validation process. An analysis procedure based on repeated cross-validation is suggested.