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1.  Assessing the performance of prediction models: a framework for some traditional and novel measures 
Epidemiology (Cambridge, Mass.)  2010;21(1):128-138.
The performance of prediction models can be assessed using a variety of different methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic (ROC) curve), and goodness-of-fit statistics for calibration.
Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision–analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.
We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n=544 for model development, n=273 for external validation).
We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for making clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
doi:10.1097/EDE.0b013e3181c30fb2
PMCID: PMC3575184  PMID: 20010215
2.  Fc Gamma Receptor IIIB (FcγRIIIB) Polymorphisms Are Associated with Clinical Malaria in Ghanaian Children 
PLoS ONE  2012;7(9):e46197.
Plasmodium falciparum malaria kills nearly a million people annually. Over 90% of these deaths occur in children under five years of age in sub-Saharan Africa. A neutrophil mediated mechanism, the antibody dependent respiratory burst (ADRB), was recently shown to correlate with protection from clinical malaria. Human neutrophils constitutively express Fc gamma receptor-FcγRIIA and FcγRIIIB by which they interact with immunoglobulin (Ig) G (IgG)-subclass antibodies. Polymorphisms in exon 4 of FCGR2A and exon 3 of FCGR3B genes encoding FcγRIIA and FcγRIIIB respectively have been described to alter the affinities of both receptors for IgG. Here, associations between specific polymorphisms, encoding FcγRIIA p.H166R and FcγRIIIB-NA1/NA2/SH variants with clinical malaria were investigated in a longitudinal malaria cohort study. FcγRIIA-p.166H/R was genotyped by gene specific polymerase chain reaction followed by allele specific restriction enzyme digestion. FCGR3B-exon 3 was sequenced in 585 children, aged 1 to 12 years living in a malaria endemic region of Ghana. Multivariate logistic regression analysis found no association between FcγRIIA-166H/R polymorphism and clinical malaria. The A-allele of FCGR3B-c.233C>A (rs5030738) was significantly associated with protection from clinical malaria under two out of three genetic models (additive: p = 0.0061; recessive: p = 0.097; dominant: p = 0.0076) of inheritance. The FcγRIIIB-SH allotype (CTGAAA) containing the 233A-allele (in bold) was associated with protection from malaria (p = 0.049). The FcγRIIIB-NA2*03 allotype (CTGCGA), a variant of the classical FcγRIIIB-NA2 (CTGCAA) was associated with susceptibility to clinical malaria (p = 0.0092). The present study is the first to report an association between a variant of FcγRIIIB-NA2 and susceptibility to clinical malaria and provides justification for further functional characterization of variants of the classical FcγRIIIB allotypes. This would be crucial to the improvement of neutrophil mediated functional assays such as the ADRB assay aimed at assessing the functionality of antibodies induced by candidate malaria vaccines.
doi:10.1371/journal.pone.0046197
PMCID: PMC3458101  PMID: 23049979
3.  Robust Prediction of t-year Survival with Data from Multiple Studies 
Biometrics  2010;67(2):436-444.
Summary
Recently meta analysis has been widely utilized to combine information across multiple studies to evaluate a common effect. Integrating data from similar studies is particularly useful in genomic studies where the individual study sample sizes are not large relative to the number of parameters of interest. In this paper, we are interested in developing robust prognostic rules for the prediction of t-year survival based on multiple studies. We propose to construct a composite score for prediction by fitting a stratified semiparametric transformation model that allows the studies to have related but not identical outcomes. To evaluate the accuracy of the resulting score, we provide point and interval estimators for the commonly used accuracy measures including the time-specific ROC curves, and positive and negative predictive values. We apply the proposed procedures to develop prognostic rules for the 5-year survival of breast cancer patients based on five breast cancer genomic studies.
doi:10.1111/j.1541-0420.2010.01462.x
PMCID: PMC2987565  PMID: 20670303
Biomarker; Classification; Conditional Kaplan-Meier; Meta Analysis; Nonparametric Maximum Likelihood; Predictive Values; Prognosis; ROC; Survival Analysis
4.  pcaGoPromoter - An R Package for Biological and Regulatory Interpretation of Principal Components in Genome-Wide Gene Expression Data 
PLoS ONE  2012;7(2):e32394.
Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome-wide expression data and overcomes the aforementioned problems. In the first step, principal component analysis (PCA) is applied to survey any differences between experiments and possible groupings. The next step is the interpretation of the principal components with respect to both biological function and regulation by predicted transcription factor binding sites. The robustness of the results is evaluated using cross-validation, and illustrative plots of PCA scores and gene ontology terms are available. pcaGoPromoter works with any platform that uses gene symbols or Entrez IDs as probe identifiers. In addition, support for several popular Affymetrix GeneChip platforms is provided. To illustrate the features of the pcaGoPromoter package a serum stimulation experiment was performed and the genome-wide gene expression in the resulting samples was profiled using the Affymetrix Human Genome U133 Plus 2.0 chip. Array data were analyzed using pcaGoPromoter package tools, resulting in a clear separation of the experiments into three groups: controls, serum only and serum with inhibitor. Functional annotation of the axes in the PCA score plot showed the expected serum-promoted biological processes, e.g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-κB activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. These tools give an overview of the input data via PCA, functional interpretation by gene ontology terms (biological processes), and an indication of the involvement of possible transcription factors.
doi:10.1371/journal.pone.0032394
PMCID: PMC3288097  PMID: 22384239
5.  A genome-wide association study of men with symptoms of testicular dysgenesis syndrome and its network biology interpretation 
Journal of Medical Genetics  2011;49(1):58-65.
Background
Testicular dysgenesis syndrome (TDS) is a common disease that links testicular germ cell cancer, cryptorchidism and some cases of hypospadias and male infertility with impaired development of the testis. The incidence of these disorders has increased over the last few decades, and testicular cancer now affects 1% of the Danish and Norwegian male population.
Methods
To identify genetic variants that span the four TDS phenotypes, the authors performed a genome-wide association study (GWAS) using Affymetrix Human SNP Array 6.0 to screen 488 patients with symptoms of TDS and 439 selected controls with excellent reproductive health. Furthermore, they developed a novel integrative method that combines GWAS data with other TDS-relevant data types and identified additional TDS markers. The most significant findings were replicated in an independent cohort of 671 Nordic men.
Results
Markers located in the region of TGFBR3 and BMP7 showed association with all TDS phenotypes in both the discovery and replication cohorts. An immunohistochemistry investigation confirmed the presence of transforming growth factor β receptor type III (TGFBR3) in peritubular and Leydig cells, in both fetal and adult testis. Single-nucleotide polymorphisms in the KITLG gene showed significant associations, but only with testicular cancer.
Conclusions
The association of single-nucleotide polymorphisms in the TGFBR3 and BMP7 genes, which belong to the transforming growth factor β signalling pathway, suggests a role for this pathway in the pathogenesis of TDS. Integrating data from multiple layers can highlight findings in GWAS that are biologically relevant despite having border significance at currently accepted statistical levels.
doi:10.1136/jmedgenet-2011-100174
PMCID: PMC3284313  PMID: 22140272
TDS; systems biology; GWAS; infertility; testis cancer; reproductive medicine; genome-wide; genetics; epidemiology; diabetes; endocrinology; genetic epidemiology; cancer: urological; chromosomal; oncology; developmental
6.  Concepts in cancer survival analysis: research questions, data, and models 
Surgical oncology  2010;19(2):52-54.
doi:10.1016/j.suronc.2010.03.003
PMCID: PMC2878891  PMID: 20381340
7.  The Validation and Assessment of Machine Learning: A Game of Prediction from High-Dimensional Data 
PLoS ONE  2009;4(8):e6287.
In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often implies that multiple methods are tested and compared on the same set of data. This is particularly difficult in situations that are prone to over-fitting where the number of subjects is low compared to the number of potential predictors. The article presents a game which provides some grounds for conducting a fair model comparison. Each player selects a modeling strategy for predicting individual response from potential predictors. A strictly proper scoring rule, bootstrap cross-validation, and a set of rules are used to make the results obtained with different strategies comparable. To illustrate the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively.
doi:10.1371/journal.pone.0006287
PMCID: PMC2716515  PMID: 19652722
8.  Positron emission tomography/computed tomography for optimized colon cancer staging and follow up 
Objectives
Optimal management of colon cancer (CC) requires detailed assessment of extent of disease. This study prospectively investigates the diagnostic accuracy of 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (PET/CT) for staging and detection of recurrence in primary CC.
Material and methods
PET/CT for preoperative staging was performed in 66 prospectively included patients with primary CC. Diagnostic accuracy for PET/CT and CT was analyzed. In addition to routine follow up, 42 stages I–III CC patients had postoperative PET/CT examinations every 6 months for 2 years. Serological levels of tissue inhibitor of metalloproteinase-1 (TIMP-1), carcinoembryonic antigen, and liberated domain I of urokinase plasminogen activator receptor were analyzed.
Results
Accuracy for tumor, nodal, and metastases staging by PET/CT were 82% (95% confidence interval [CI]: 70; 91), 66% (CI: 51; 78), and 89% (CI: 79; 96); for CT the accuracy was 77% (CI: 64; 87), 60% (CI: 46; 73), and 69% (CI: 57; 80). Cumulative relapse incidences for stages I–III CC at 6, 12, 18, and 24 months were 7.1% (CI: 0; 15); 14.3% (CI: 4; 25); 19% (CI: 7; 31), and 21.4% (CI: 9; 34). PET/CT diagnosed all relapses detected during the first 2 years. High preoperative TIMP-1 levels were associated with significant hazards toward risk of recurrence and shorter overall survival.
Conclusions
This study indicates PET/CT as a valuable tool for staging and follow up in CC. TIMP-1 provided prognostic information potentially useful in selection of patients for intensive follow up.
doi:10.3109/00365521.2013.863967
PMCID: PMC3956490  PMID: 24286594
carcinoembryonic antigen; colonic neoplasms; colorectal neoplasms; neoplasm staging; positron emission tomography; prognosis; receptors; tissue inhibitor of metalloproteinase-1; urokinase plasminogen activator; X-ray computed tomography

Results 1-8 (8)