Using survival prediction techniques to learn consumer-specific reservation price distributions.
Ontology highlight
ABSTRACT:
SUBMITTER: Jin P
PROVIDER: S-EPMC8084175 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo values enable statistically appropriate analyses of survival outcomes when used in seven alternative modeling techniques. METHODS:In this case study, we analyzed survival of 1282 Dutch patients with newly diagnosed Head and Neck Squamous Cell Carcinoma (HNSCC) with conventional Kaplan-Meier and Cox regression analysis. We subsequently calculated pseudo values to reflect the individual survival patterns. We used these pseudo values to compare recursive partitioning (RPART), neural nets (NNET), logistic regression (LR) general linear models (GLM) and three variants of support vector machines (SVM) with respect to dichotomous 60-month survival, and continuous pseudo values at 60 months or estimated survival time. We used the area under the ROC curve (AUC) and the root of the mean squared error (RMSE) to compare the performance of these models using bootstrap validation. RESULTS:Of a total of 1282 patients, 986 patients died during a median follow-up of 66 months (60-month survival: 52% [95% CI: 50%-55%]). The LR model had the highest optimism corrected AUC (0.791) to predict 60-month survival, followed by the SVM model with a linear kernel (AUC 0.787). The GLM model had the smallest optimism corrected RMSE when continuous pseudo values were considered for 60-month survival or the estimated survival time followed by SVM models with a linear kernel. The estimated importance of predictors varied substantially by the specific aspect of survival studied and modeling technique used. CONCLUSIONS:The use of pseudo values makes it readily possible to apply alternative modeling techniques to survival problems, to compare their performance and to search further for promising alternative modeling techniques to analyze survival time.
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Project description:The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This method is often used for dimensionality reduction and analysis of the data. In this paper, we develop a general method for stock price prediction using time-varying covariance information. To address the time-varying nature of financial time series, we assign exponential weights to the price data so that recent data points are weighted more heavily. Our proposed method involves a dimension-reduction operation constructed based on principle components. Projecting the noisy observation onto a principle subspace results in a well-conditioned problem. We illustrate our results based on historical daily price data for 150 companies from different market-capitalization categories. We compare the performance of our method to two other methods: Gauss-Bayes, which is numerically demanding, and moving average, a simple method often used by technical traders and researchers. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.
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Project description:ObjectiveTo determine how long it takes for neural impulses to travel along peripheral nerve fibers in living humans.MethodsA collision test was performed to measure the conduction velocity distribution of the ulnar nerve. Two stimuli at the distal and proximal sites were used to produce the collision. Compound muscle or nerve action potentials were recorded to perform the measurements on the motor or mixed nerve, respectively. Interstimulus interval was set at 1-5 ms. A quadri-pulse technique was used to measure the refractory period and calibrate the conduction time.ResultsCompound muscle action potential produced by the proximal stimulation started to emerge at the interstimulus interval of about 1.5 ms and increased with the increment in interstimulus interval. Two groups of motor nerve fibers with different conduction velocities were identified. The mixed nerve showed a wider conduction velocity distribution with identification of more subgroups of nerve fibers than the motor nerve.ConclusionsThe conduction velocity distributions in high resolution on a peripheral motor and mixed nerve are different and this can be measured with the collision test.SignificanceWe provided ground truth data to verify the neuroimaging pipelines for the measurements of latency connectome in the peripheral nervous system.
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Project description:Recently it was shown that gene expression signatures generated from DNA microarray analyses have promise as biomarkers of clinical outcome and that the molecular characteristics of tumors could be elucidated. Through this study, we have determined a high-risk signature for recurrence as a prognostic biomarker using formalin-fixed HNSCC tumors and tested the results to an independent data set obtained from fresh frozen tumors as a comparison. Also, we have shown the genes that are involved in epithelial to mesenchymal transition and nuclear factor-ĸB signaling deregulation are the most prominent molecular characteristics of the high-risk tumors. Forty samples including 34 formalin-fixed tissues and 6 matched frozen tissues from 29 HNSCC patients were analyzed for gene expression. The formalin-fixed tumors were classified based on their gene expression by intrinsic analysis and the intrinsic gene list was tested on the classification of previously published 60 frozen HNSCC tumors which highly correlated. Based on the molecular classification, a 75-gene list that is predictive of high-risk for recurrence was determined by training on the formalin-fixed tumor set and tested on the independent frozen tumor set. The difference in recurrence-free survival (RFS) between the high-risk vs. low-risk groups in the training and test sets were statistically significant (Log-rank test, p=0.002 and p=0.03, respectively). Also, the gene expression data was interrogated using Gene Set Enrichment Analysis to determine functional significance. The most significant sets of genes that are enriched in the high-risk tumors were genes involving epithelial to mesenchymal transition (EMT), NF-ĸB activation and cell adhesion. In conclusion, global gene expression analysis is feasible using formalin-fixed tissue, and the data from different sample preparations and array platforms can be reliably combined for analyses. The 75-gene list can be utilized as a prognostic biomarker of recurrence and the molecular characteristics of EMT and NF-ĸB activation can be targeted as the novel therapy in the identified high-risk patients. Keywords: FFPE, survival analysis
2006-11-02 | GSE2837 | GEO
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