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Development and evaluation of multi-marker risk scores for clinical prognosis.

ABSTRACT: Heart failure research suggests that multiple biomarkers could be combined with relevant clinical information to more accurately quantify individual risk and guide patient-specific treatment strategies. Therefore, statistical methodology is required to determine multi-marker risk scores that yield improved prognostic performance. Development of a prognostic score that combines biomarkers with clinical variables requires specification of an appropriate statistical model and is most frequently achieved using standard regression methods such as Cox regression. We demonstrate that care is needed in model specification and that maximal use of marker information requires consideration of potential non-linear effects and interactions. The derived multi-marker score can be evaluated using time-dependent receiver operating characteristic methods, or risk reclassification methods adapted for survival outcomes. We compare the performance of alternative model accuracy methods using simulations, both to evaluate power and to quantify the potential loss in accuracy associated with use of a sub-optimal regression model to develop the multi-marker score. We illustrate development and evaluation strategies using data from the Penn Heart Failure Study. Based on our results, we recommend that analysts carefully examine the functional form for component markers and consider plausible forms for effect modification to maximize the prognostic potential of a model-derived multi-marker score.

SUBMITTER: French B

PROVIDER: S-EPMC3467353 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Development and evaluation of multi-marker risk scores for clinical prognosis.

French Benjamin B Saha-Chaudhuri Paramita P Ky Bonnie B Cappola Thomas P TP Heagerty Patrick J PJ

Statistical methods in medical research 20120705 1

Heart failure research suggests that multiple biomarkers could be combined with relevant clinical information to more accurately quantify individual risk and guide patient-specific treatment strategies. Therefore, statistical methodology is required to determine multi-marker risk scores that yield improved prognostic performance. Development of a prognostic score that combines biomarkers with clinical variables requires specification of an appropriate statistical model and is most frequently ach ...[more]

PMID: 22767865

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Project description:We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application to survival prediction in High-Risk (HR) Neuroblastoma from transcriptomics data, a task that studies from the MAQC consortium have shown to remain the hardest among multiple diagnostic and prognostic endpoints predictable from the same dataset. To obtain a more accurate risk stratification needed for appropriate treatment strategies, CDRP combines a first component (CDRP-A) synthesizing a diagnostic task and a second component (CDRP-N) dedicated to one or more prognostic tasks. The approach leverages the advent of semi-supervised deep learning structures that can flexibly integrate multimodal data or internally create multiple processing paths. CDRP-A is an autoencoder trained on gene expression on the HR/non-HR risk stratification by the Children's Oncology Group, obtaining a 64-node representation in the bottleneck layer. CDRP-N is a multi-task classifier for two prognostic endpoints, i.e., Event-Free Survival (EFS) and Overall Survival (OS). CDRP-A provides the HR embedding input to the CDRP-N shared layer, from which two branches depart to model EFS and OS, respectively. To control for selection bias, CDRP is trained and evaluated using a Data Analysis Protocol (DAP) developed within the MAQC initiative. CDRP was applied on Illumina RNA-Seq of 498 Neuroblastoma patients (HR: 176) from the SEQC study (12,464 Entrez genes) and on Affymetrix Human Exon Array expression profiles (17,450 genes) of 247 primary diagnostic Neuroblastoma of the TARGET NBL cohort. On the SEQC HR patients, CDRP achieves Matthews Correlation Coefficient (MCC) 0.38 for EFS and MCC = 0.19 for OS in external validation, improving over published SEQC models. We show that a CDRP-N embedding is indeed parametrically associated to increasing severity and the embedding can be used to better stratify patients' survival.

Dataset Information

Development and evaluation of multi-marker risk scores for clinical prognosis.

Publications

Development and evaluation of multi-marker risk scores for clinical prognosis.

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