scholarly journals Pathway-Based Personalized Analysis of Pan-Cancer Transcriptomic Data

Biomedicines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1502
Author(s):  
Cong Pian ◽  
Mengyuan He ◽  
Yuanyuan Chen
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Transcriptomic Data ◽  
Cancer Types ◽  
The Individual ◽  
Pathway Deregulation

The occurrence of cancer is closely related to the deregulation of certain pathways. Based on pathway deregulation scores (PDS) inferred by the Pathifier algorithm, we analyzed transcriptomic data of 13 different cancer types in The Cancer Genome Atlas database to identify cancer-specific deregulated pathways and prognostic pathways. The results showed that the individual-specific pathway deregulation scores can clearly distinguish different cancer types and their tumor-adjacent tissues. In addition, the cancer-specific deregulated pathways and prognostic pathways of different cancer types had high heterogeneity, and the identified cancer prognostic pathways have been reported to be closely related to the corresponding cancers. Furthermore, we also found that cancers with more deregulation pathways tend to be malignant and have worse prognoses. Finally, a Cox proportional Hazards model was constructed based on the prognostic pathways; this model successfully predicted survival and prognosis based on data from cancer samples. In addition, the performance of the breast cancer prognostic model was validated with an independent data set in the METABRIC database. Therefore, the prognostic pathways we identified have the potential to become targets for the treatment of cancer.

Age at Exposure to Parental Suicide and the Subsequent Risk of Suicide in Young People

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Kuan-Ying Lee ◽  
Chung-Yi Li ◽  
Kun-Chia Chang ◽  
Tsung-Hsueh Lu ◽  
Ying-Yeh Chen
Keyword(s):  
Young People ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Birth Registry ◽  
Data Set ◽  
Parental Suicide ◽  
The Impact ◽  
Age At Exposure

Abstract. Background: We investigated the age at exposure to parental suicide and the risk of subsequent suicide completion in young people. The impact of parental and offspring sex was also examined. Method: Using a cohort study design, we linked Taiwan's Birth Registry (1978–1997) with Taiwan's Death Registry (1985–2009) and identified 40,249 children who had experienced maternal suicide (n = 14,431), paternal suicide (n = 26,887), or the suicide of both parents (n = 281). Each exposed child was matched to 10 children of the same sex and birth year whose parents were still alive. This yielded a total of 398,081 children for our non-exposed cohort. A Cox proportional hazards model was used to compare the suicide risk of the exposed and non-exposed groups. Results: Compared with the non-exposed group, offspring who were exposed to parental suicide were 3.91 times (95% confidence interval [CI] = 3.10–4.92 more likely to die by suicide after adjusting for baseline characteristics. The risk of suicide seemed to be lower in older male offspring (HR = 3.94, 95% CI = 2.57–6.06), but higher in older female offspring (HR = 5.30, 95% CI = 3.05–9.22). Stratified analyses based on parental sex revealed similar patterns as the combined analysis. Limitations: As only register-­based data were used, we were not able to explore the impact of variables not contained in the data set, such as the role of mental illness. Conclusion: Our findings suggest a prominent elevation in the risk of suicide among offspring who lost their parents to suicide. The risk elevation differed according to the sex of the afflicted offspring as well as to their age at exposure.

scholarly journals Prognostic Value of Phosphotyrosine Phosphatases in Hepatocellular Carcinoma

2018 ◽  
Vol 46 (6) ◽  
pp. 2335-2346 ◽  
Author(s):  
Guangyan Zhangyuan ◽  
Yin Yin ◽  
Wenjie Zhang ◽  
WeiWei Yu ◽  
Kangpeng Jin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
The Cancer Genome Atlas ◽  
Expression Levels ◽  
Hazards Model ◽  
Kaplan Meier ◽  
Phosphotyrosine Phosphatases

Background/Aims: During the occurrence and progression of hepatocellular carcinoma (HCC), phosphotyrosine phosphatases (PTPs) are usually described as tumor suppressors or proto-oncogenes, and to some degree are correlated with the prognosis of HCC. Methods: A total of 321 patients from the Cancer Genome Atlas (TCGA) database and 180 patients from our validated cohort with hepatocellular carcinoma were recruited in this study. Kaplan-Meier, univariate and multivariate Cox proportional hazards model were used to evaluate the risk factors for survival. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) were applied to detect the expression levels of PTP genes. Results: After screening the data of TCGA, we identified five PTPs as HCC overall survival related PTP genes, among which only three (PTPN12, PTPRN, PTPN18) exhibited differential expression levels in our 180 paired HCC and adjacent tissues (P< 0.001). Further analysis revealed that expression of PTPN18 was positively, but PTPRN was negatively associated with prognosis of HCC both in TCGA cohort and our own cohort. As to PTPN12, results turned out to be opposite according to HBV status. In detail, higher expression of PTPN12 was associated with better outcome in HBV group but worse prognosis in Non-HBV group. Conclusion: Our results suggested that PTPN12, PTPRN and PTPN18 were independent prognostic factors in HCC.

scholarly journals Combining Symbolic Regression with the Cox Proportional Hazards Model Improves Prediction of Heart Failure Deaths

2021 ◽  
Author(s):  
Casper Wilstrup ◽  
Chris Cave
Keyword(s):  
Heart Failure ◽  
Medical Records ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Symbolic Regression ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model

Abstract Background: Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. Methods: We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.Results: An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. Conclusion: Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

scholarly journals Estimating and Interpreting Effects from Nonlinear Exposure-Response Curves in Occupational Cohorts Using Truncated Power Basis Expansions and Penalized Splines

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Elizabeth J. Malloy ◽  
Jay M. Kapellusch ◽  
Arun Garg
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Simulated Data ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Penalized Splines ◽  
Response Curves ◽  
Data Set ◽  
Exposure Response ◽  
Power Basis

Truncated power basis expansions and penalized spline methods are demonstrated for estimating nonlinear exposure-response relationships in the Cox proportional hazards model. R code is provided for fitting models to get point and interval estimates. The method is illustrated using a simulated data set under a known exposure-response relationship and in a data application examining risk of carpal tunnel syndrome in an occupational cohort.

Methods for displaying and calibration of Cox proportional hazards models

2017 ◽  
Vol 232 (1) ◽  
pp. 105-115
Author(s):  
Chrianna I Bharat ◽  
Kevin Murray ◽  
Edward Cripps ◽  
Melinda R Hodkiewicz
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Explanatory Power ◽  
Cox Proportional Hazards ◽  
Remaining Useful Life ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Proportional Hazards Models ◽  
Hazards Model ◽  
Cox Proportional Hazards Models

Cox proportional hazards modelling is a widely used technique for determining relationships between observed data and the risk of asset failure when model performance is satisfactory. Cox proportional hazards models possess good explanatory power and are used by asset managers to gain insight into factors influencing asset life. However, validation of Cox proportional hazards models is not straightforward and is seldom considered in the maintenance literature. A comprehensive validation process is a necessary foundation to build trust in the failure models that underpin remaining useful life prediction. This article describes data splitting, model discrimination, misspecification and fit methods necessary to build trust in the ability of a Cox proportional hazards model to predict failures on out-of-sample assets. Specifically, we consider (1) Prognostic Index comparison for training and test sets, (2) Kaplan–Meier curves for different risk bands, (3) hazard ratios across different risk bands and (4) calibration of predictions using cross-validation. A Cox proportional hazards model on an industry data set of water pipe assets is used for illustrative purposes. Furthermore, because we are dealing with a non-statistical managerial audience, we demonstrate how graphical techniques, such as forest plots and nomograms, can be used to present prediction results in an easy to interpret way.

Money transfer between banks

2019 ◽  
Vol 38 (2) ◽  
pp. 283-295
Author(s):  
Andrea Lippi ◽  
Laura Barbieri ◽  
Federica Poli
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Financial Advisors ◽  
Data Set ◽  
Financial Intermediary ◽  
Content Type ◽  
Financial Advisor ◽  
Hazards Model

Purpose The purpose of this paper is to examine which individual traits of financial advisors influence portfolio transfer speed when a financial advisor recommends investors to migrate to a new financial intermediary. Design/methodology/approach With reference to the years 2014–2016, one of the three leading Italian tied-agent banks provided the authors with an exclusive and unique data set containing information regarding the financial advisors who had become tied agents, transferring their existing portfolios from their previous banks (traditional or tied-agent banks). The authors observed the ability of the migrant financial advisor in successfully transferring the entire portfolio declared within 12 months of observation. To investigate empirically which personal traits of financial advisors determine their success in the rapid transfer of clients’ portfolios to a new financial intermediary, the authors applied a Cox proportional hazards model. Findings The authors find that factors such as age, type of bank of origin and size of the managed financial portfolio positively affect the speed transfer. Practical implications The obtained results may be interesting for guiding recruiting policies of financial intermediaries. Social implications Regulators should closely examine the phenomenon analyzed in this paper to avoid conflict of interests. Originality/value The literature on this topic is scarce, mainly due to the lack of available data. This paper represents an original contribution to open a new field of research.

scholarly journals Association of pre-existing comorbidities with outcome of allogeneic hematopoietic cell transplantation. A retrospective analysis from the EBMT

2021 ◽  
Author(s):  
Olaf Penack ◽  
Christophe Peczynski ◽  
Mohamad Mohty ◽  
Ibrahim Yakoub-Agha ◽  
Rafael de la Camara ◽  
...  
Keyword(s):  
Cell Transplantation ◽  
Hematopoietic Cell Transplantation ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hematopoietic Cell ◽  
Allogeneic Hematopoietic Cell Transplantation ◽  
Full Data ◽  
Data Set

AbstractRisk assessment of allogeneic hematopoietic cell transplantation (allo-HCT) is hindered by the lack of current data on comorbidities and outcome. The EBMT identified 38,760 allo-HCT recipients with hematologic malignancies transplanted between 2010 and 2018 from matched sibling and unrelated donors with a full data set of pre-existing comorbidities. Multivariate analyses using the Cox proportional-hazards model including known risk factors for non-relapse mortality (NRM) were performed. We found that pre-existing renal comorbidity had the strongest association with NRM (hazard ratio [HR] 1.85 [95% CI 1.55–2.19]). In addition, the association of multiple pre-existing comorbidities with NRM was significant, including diabetes, infections, cardiac comorbidity, and pulmonary comorbidity. However, the HR of the association of these comorbidities with NRM was relatively low and did not exceed 1.24. Consequently, the risk of NRM was only moderately increased in patients with a high hematopoietic cell transplantation comorbidity index (HCT-CI) ≥ 3 (HR 1.34 [1.26–1.42]). In the current EBMT population, pre-existing non-renal comorbidities determined NRM after allo-HCT to a much lesser extent as compared with the underlying HCT-CI data. Improvements in management and supportive care as well as higher awareness based on the use of HCT-CI may have contributed to this favorable development.

scholarly journals Systematical identifications of prognostic meaningful lung adenocarcinoma subtypes and the underlying mutational and expressional characters

2019 ◽  
Author(s):  
Ting Lei ◽  
Zhenyang Lv
Keyword(s):  
Lung Adenocarcinoma ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Comprehensive Description ◽  
Common Cancer ◽  
Master Genes ◽  
Hazards Model ◽  
Cancer Types

Abstract Lung adenocarcinoma (LUAD) is one of the most common cancer types. However, the high heterogeneity and complexity of LUAD hinder effective treatments. This study aimed to identify the key prognosis impacting genes and the corresponding subtypes for LUAD. Specifically, the cox proportional hazards model was combined with a causal regulatory network to help reveal which genes play master roles among numerous prognosis impacting genes, and sub-types were identified based on expressional profiles of the master genes. As results, a collection of 75 genes were recognized as the master prognosis impacting genes, where some were enriched in mTOR signaling and lysosome pathways. Based on their expressions, the LUAD patients were separated into two sub-types displaying remarkable differences in expressional profiles, prognostic outcomes and genomic mutations. Meanwhile, the two subtypes were re-discovered from two additional LUAD cohorts based on only the top-10 important master genes. This study provides a comprehensive description on the key prognosis-relevant genes and an alternative way to classify LUAD subtypes which can promote LUAD precision treatment.

Impact of digital multi-sided platforms on firm survival in retail

2021 ◽  
pp. 93-122
Author(s):  
E. S. Andronova ◽  
A. I. Rey ◽  
G. R. Akzhigitova
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Positive Impact ◽  
Survival Function ◽  
Firm Survival ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model ◽  
Kaplan Meier

This paper explores firm survival in Russian retail industry in cases of digital multi-sided platforms penetration such as aggregator Yandex.Market, marketplace Wildberries, electronic store Ozon and classified-ad service Avito. The panel data set of 130 thousand firms was analyzed using two methods: non-parametric Kaplan—Meier estimator and semi-parametric Cox proportional hazards model with time dependent covariates. Kaplan—Meier estimator calculates the survival function for censored data. Cox proportional hazards model examines the effect of platform penetration on hazard rates of differently sized firms in various industry spheres. Platforms-aggregators Yandex.Market and Wildberries have a strong positive impact on firm survival while platformsdisruptors Ozon and Avito increase likelihood of firm failure. The main results of platform influence in various industry spheres are as follows: the aggregator of price offers has a more positive impact on segments with high information asymmetry; and firms specialized on Wildberries key product categories enjoy lower hazard ratios of bankruptcy or liquidation. These hypotheses are not supported for Ozon and Avito platforms.

scholarly journals Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

2021 ◽  
Author(s):  
Casper Wilstup ◽  
Chris Cave
Keyword(s):  
Heart Failure ◽  
Medical Records ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Symbolic Regression ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model

AbstractHeart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone.We used a newly invented symbolic regression method called the QLat-tice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations.Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

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