Predictive accuracy of novel risk factors and markers: A simulation study of the sensitivity of different performance measures for the Cox proportional hazards regression model

Predicting outcomes that occur over time is important in clinical, population health, and health services research. We compared changes in different measures of performance when a novel risk factor or marker was added to an existing Cox proportional hazards regression model. We performed Monte Carlo simulations for common measures of performance: concordance indices (c, including various extensions to survival outcomes), Royston's D index, R2-type measures, and Chambless' adaptation of the integrated discrimination improvement to survival outcomes. We found that the increase in performance due to the inclusion of a risk factor tended to decrease as the performance of the reference model increased. Moreover, the increase in performance increased as the hazard ratio or the prevalence of a binary risk factor increased. Finally, for the concordance indices and R2-type measures, the absolute increase in predictive accuracy due to the inclusion of a risk factor was greater when the observed event rate was higher (low censoring). Amongst the different concordance indices, Chambless and Diao's c-statistic exhibited the greatest increase in predictive accuracy when a novel risk factor was added to an existing model. Amongst the different R2-type measures, O'Quigley et al.'s modification of Nagelkerke's R2 index and Kent and O'Quigley's Ï w, a 2 displayed the greatest sensitivity to the addition of a novel risk factor or marker. These methods were then applied to a cohort of 8635 patients hospitalized with heart failure to examine the added benefit of a point-based scoring system for predicting mortality after initial adjustment with patient age alone

Clinical Significance of Organic Anion Transporting Polypeptide Gene Expression in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is considered the most deadly and frequently occurring type of ovarian cancer and is associated with various molecular compositions and growth patterns. Evaluating the mRNA expression pattern of the organic anion transporters (OATPs) encoded by SLCO genes may allow for improved stratification of HGSOC patients for targeted invention. The expression of SLCO mRNA and genes coding for putative functionally related ABC-efflux pumps, enzymes, pregnane-X-receptor, ESR1 and ESR2 (coding for estrogen receptors ERα and ERß) and HER-2 were assessed using RT-qPCR. The expression levels were assessed in a cohort of 135 HGSOC patients to elucidate the independent impact of the expression pattern on the overall survival (OS). For identification of putative regulatory networks, Graphical Gaussian Models were constructed from the expression data with a tuning parameter K varying between meaningful borders (Pils et al., 2012; Auer et al., 2015, 2017; Kurman and Shih Ie, 2016; Karam et al., 2017; Labidi-Galy et al., 2017; Salomon-Perzynski et al., 2017; Sukhbaatar et al., 2017). The final value used (K = 4) was determined by maximizing the proportion of explained variation of the corresponding LASSO Cox regression model for OS. The following two networks of directly correlated genes were identified: (i) SLCO2B1 with ABCC3 implicated in estrogen homeostasis; and (ii) two ABC-efflux pumps in the immune regulation (ABCB2/ABCB3) with ABCC3 and HER-2. Combining LASSO Cox regression and univariate Cox regression analyses, SLCO5A1 coding for OATP5A1, an estrogen metabolite transporter located in the cytoplasm and plasma membranes of ovarian cancer cells, was identified as significant and independent prognostic factor for OS (HR = 0.68, CI 0.49–0.93; p = 0.031). Furthermore, results indicated the benefits of patients with high expression by adding 5.1% to the 12.8% of the proportion of explained variation (PEV) for clinicopathological parameters known for prognostic significance (FIGO stage, age and residual tumor after debulking). Additionally, overlap with previously described signatures that indicated a more favorable prognosis for ovarian cancer patients was shown for SLCO5A1, the network ABCB2/ABCB3/ABCC4/HER2 as well as ESR1. Furthermore, expression of SLCO2A1 and PGDH, which are important for PGE2 degradation, was associated with the non-miliary peritoneal tumor spreading. In conclusion, the present findings suggested that SLCOs and the related molecules identified as potential biomarkers in HGSOC may be useful for the development of novel therapeutic strategies

Modelo de prognóstico do tempo necessário ao controlo da dor oncológica

Tese de mestrado em Bioestatística, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012A dor e dos sintomas mais angustiantes e prevalentes do cancro, ocorrendo em mais de 60% dos doentes com cancro metastático ou avançado. Entender as características associadas a complexidade do tratamento da dor oncológica tem sido nas últimas duas décadas, e continua a ser, um objectivo por cumprir. Neste trabalho são desenvolvidos seis modelos de tempo de vida acelerado para o tempo até controlar a dor oncológica. São consideradas três distribuições para o tempo, nomeadamente as distribuições Weibull, log-logística e log-normal, e adoptadas duas regras de decisão alternativas no processo de seleção dos preditores. Os seis modelos são avaliados relativamente a sua capacidade preditiva, através de medidas que quantificam a capacidade preditiva global, a discriminação e a calibração. Estas medidas são calculadas para a amostra original mas também para amostras bootstrap, permitindo assim a obtenção de valores corrigidos para o optimismo (validação interna). A capacidade preditiva e utilizada como critério de seleção de um dos modelos, que constitui um índice de prognóstico e serve de base a criação de três grupos de prognóstico da dificuldade em atingir a analgesia em doentes oncológicos.In cancer patients, pain is one of the most feared and prevalent symptoms, occurring in more than 60% of the patients with metastatic or advanced stage disease. Understanding the characteristics related to the complexity of cancer pain treatment has been in the past two decades, and still is, an unfulfilled goal. Six accelerated failure time models of the time to achieve pain control are developed. Weibull, log-logistic and log-normal distributions are considered as well as two alternative stopping rules in the predictors selection process. These six models are evaluated with respect to their predictive accuracy, through measures assessing discrimination, calibration and overall accuracy. Such measures are calculated from the original data set as well as from bootstrap samples, allowing for the optimism correction (internal validation). Predictive accuracy is set as a criterion to select one of the models to constitute a prognostic index and to create three prognostic groups including patients with different levels of difficulty to attain stabilized cancer pain