scholarly journals Influence of Tourism Seasonality and Financial Ratios on Hotels’ Exit Risk

2021 ◽  
pp. 109634802110160
Author(s):  
Dengjun Zhang ◽  
Jinghua Xie
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Hotel Industry ◽  
Financial Ratios ◽  
Market Segment ◽  
Tourism Demand ◽  
Hazards Model ◽  
Two Measures ◽  
The Impact

Tourism seasonality negatively affects hotels’ operational and financial performance and then survival probabilities. Several studies have evaluated the impact of tourism seasonality on hotels’ exit risk. However, the empirical findings are ambiguous, probably due to the overall seasonality and different measures used in these studies. Against this background, this study explores the impact of tourism seasonality on hotel firms’ exit risk, using a proportional hazards model. We controlled for financial ratios, the main factors influencing the exit risk, and used two measures of tourism seasonality by market segment, namely, leisure, business, and conference tourism. The case study is the Norwegian hotel industry. The empirical results suggest that the different seasonal patterns of tourism demand in the market segments mitigate the impact of the overall seasonality on hotels’ exit risk, and that seasonality measures of various tourism segments affect the exit risk in different ways.

scholarly journals Influence of Tourism Seasonality and Financial Ratios on Hotels’ Exit Risk

2020 ◽  
Author(s):  
Jinghua Xie ◽  
Dengjun Zhang
Keyword(s):  
Large Body ◽  
Hotel Industry ◽  
Financial Ratios ◽  
Seasonal Patterns ◽  
Market Segment ◽  
Tourism Demand ◽  
Two Measures ◽  
Main Factors ◽  
The Impact

A large body of research has documented the impact of tourism seasonality on hotels’ operational and financial performance, further affecting hotels’ competitive advantage and survival probabilities. Several studies have included the seasonality measures in the models to evaluate hotels’ exit risk. However, the empirical findings are ambiguous, probably because the overall seasonality and different measures were used in those studies. Against this background, this study explores the impact of tourism seasonality on hotel firms’ exit risk, by controlling for financial ratios, the main factors influencing the exit risk, and using two measures of tourism seasonality by market segment, namely, the leisure, business, and conference tourism. The primary hypotheses are: (1) The different seasonal patterns of tourism demand in the market segments mitigate the impact of the overall seasonality on hotels’ exit risk, and (2) Seasonality measures of various tourism segments affect the exit risk in different manners. The case study is the Norwegian hotel industry with 4,622 hotel-years in the period between 2008 and 2018. The empirical results suggest the failure to reject the hypotheses, regardless of the measures of tourism seasonality, indicating the robustness of our findings.

Between National Polarization and Local Ideology: The Impact of Partisan Competition on State Medicaid Expansion Decisions

2019 ◽  
Vol 50 (2) ◽  
pp. 237-255 ◽  
Author(s):  
Joshua Meyer-Gutbrod
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Electoral Competition ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Party Competition ◽  
Medicaid Expansion ◽  
Hazards Model ◽  
Partisan Competition ◽  
The Impact

Abstract The U.S. Supreme Court’s decision to grant states the authority to reject Medicaid expansion under the Affordable Care Act without penalty threatened the implementation of this polarized health policy. While many Republican-controlled states followed their national allies and rejected Medicaid expansion, others engaged in bipartisan implementation. Why were some Republican states willing to reject the national partisan agenda and cooperate with Democrats in Washington? I focus on the role of electoral competition within states. I conclude that although electoral competition has been shown to encourage partisan polarization within the states, the combination of intergovernmental implementation and Medicaid expansion’s association with public welfare reverses this dynamic. I employ a Cox proportional-hazards model to examine the impact of state partisan ideology and competition on the likelihood of state Medicaid expansion. I find that strong inter-party competition mitigates the impact of more extreme partisan ideologies, encouraging potentially bipartisan negotiation with the federal administration.

scholarly journals Impact of Prior Cancer History on Outcomes in Thymoma

2020 ◽  
Author(s):  
Shilong Wu ◽  
Mengyang Liu ◽  
Weixue Cui ◽  
Guilin Peng ◽  
Jianxing He
Keyword(s):  
Overall Survival ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cancer History ◽  
Treatment Methods ◽  
Hazards Model ◽  
Kaplan Meier ◽  
The Impact

Abstract Background Thymoma is an uncommon intrathoracic malignant tumor and has a long natural history. It is uncertain whether the survival of thymoma patient is affected by prior cancer history. Finding out the impact of a prior cancer history on thymoma survival has important implications for both decision making and research. Method The Surveillance, Epidemiology, and End Results (SEER) database was queried for thymoma patients diagnosed between 1975 and 2015. Kaplan-Meier methods and Cox proportional hazards model were used to analyze overall survival across a variety of stages, age, and treatment methods with a prior cancer history or not. Results A total of 3604 patients with thymoma were identified including 507 (14.1%) with a prior cancer history. The 10-year survival rate of patients with a prior cancer history (53.8%) was worse than those without a prior cancer history (40.32%, 95%CI 35.24-45.33, P < 0.0001). However, adjusted analyses showed that the impact of a prior cancer history was heterogenous across age and treatment methods. In subset analyses, prior cancer history was associated with worse survival among patients who were treated with chemoradiotherapy (HR: 2.80, 95% CI: 1.51-5.20, P = 0.001) and age ≤ 65 years (HR: 1.33, 95%CI: 1.02-1.73, P = 0.036). Conclusions Prior cancer history provides an inferior overall survival for patients with thymoma. But it does not worsen the survival in some subgroups and these thymoma patients should not be excluded from clinical trials.

Glucocorticoid receptor (GR) expression in circulating tumor cells (CTCs) to prognosticate overall survival (OS) for metastatic castration-resistant prostate cancer (mCRPC) patients (pts) treated with androgen receptor signaling inhibitors (ARSi).

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 194-194 ◽  
Author(s):  
David Wise ◽  
James Kelvin ◽  
Ryon Graf ◽  
Nicole A. Schreiber ◽  
Brigit McLaughlin ◽  
...  
Keyword(s):  
Protein Expression ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Predictive Biomarker ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Castration Resistant Prostate Cancer ◽  
Hazards Model ◽  
Kaplan Meier ◽  
The Impact

194 Background: Upregulation of GR protein expression in metastatic biopsies from pts with CRPC has previously been shown to correlate with resistance to enzalutamide and has been validated as a therapeutic target in pre-clinical studies. We sought to determine whether upregulated GR protein expression in CTCs from pts with progressing mCRPC predicted clinical outcomes following treatment with enzalutamide (E) or abiraterone (A). Methods: Pre-therapy blood samples from 54 pts with progressing mCRPC were subjected to CTC analysis using the Epic Sciences platform. Samples were examined to identify CK+ (CK+, CD45- cells, with intact nuclei, morph distinct) CTCs for GR protein expression. GR+ CTCs were defined as having expression greater than the 95th percentile of GR expression in the GR negative LNCAP cell line. Kaplan-Meier analysis was used to test the impact of GR+ CTCs on OS following treatment with A or E. A Cox proportional hazards model with CTC number and GR positivity was used in a multivariate analysis. Results: 37 out of 54 pts (69%) had detectable and viable CK+ CTCs. 28 out of 37 pts (76%) had CTCs with upregulated GR staining with a median of 6 GR+/CK+ cells/ml per patient (range 0.7 – 244 cells/ml). The OS of patients with GR+ CTCs treated with ARSi was significantly worse than that of patients without detectable GR+ CTCs (11.4 mo. vs NA, p < 0.01), an effect independent and additive to the presence of viable CTCs, a previously described prognostic biomarker (see Table). Conclusions: GR protein upregulation in CTCs can be detected in a significant percentage of pts with progressing mCRPC and the presence of GR+ CTCs predicts worse OS in response to ARSi. The data supports previously reported pre-clinical data proposing a pathogenic role for GR in mediating resistance to ARSi therapy. Detection of GR in patient CTCs may be a useful predictive biomarker to guide GR-directed therapies. [Table: see text]

Supportive care in cancer: Impact of marital status on outcome of end-stage head and neck cancer.

2018 ◽  
Vol 36 (34_suppl) ◽  
pp. 18-18
Author(s):  
Matthew C Simpson ◽  
Aleksandr R Bukatko ◽  
Allison P Knewitz ◽  
Connor L Donovan ◽  
Eric Adjei Boakye ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Marital Status ◽  
Head And Neck ◽  
Neck Cancer ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Never Married ◽  
Hazards Model ◽  
End Stage ◽  
The Impact

18 Background: The impact of marital status on cancer survival, including head and neck cancer (HNC), has been previously described. However, no previous study has shown whether being married impacts head and neck cancer patients with end-stage disease. The objective of this study was to determine the impact of marital status on survival of patients with stage IVc HNC. Methods: Patients ≥18 years from the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with end-stage (AJCC stage IVc) head and neck squamous cell carcinoma from 2007-2015 ( n=2,886) were included. Kaplan-Meier survival estimated crude survival differences stratified by marital status (married/partnered, never married, divorced/separated, widowed) using log-rank test, and in-between differences were determined using Bonferroni adjustments. Competing risks proportional hazards model determined the effect of marital status on death from HNC while controlling for covariates (age, year of diagnosis, county-level poverty percentage, sex, race/ethnicity, insurance, anatomic subsite, and treatment modality). Results: Patients were predominantly male (81%) and white (65%), with mean age of 62 years. Median overall survival for the cohort was 11 months. The Kaplan-Meier curves indicated at the end of follow-up that divorced/separated (HNSCC-specific survival percentage=13%), never married (8%), and widowed patients (12%) had significantly lower survival than married/partnered patients (20%) (Bonferroni p<0.01). After adjusting for covariates, the proportional hazards model indicated that divorced/separated (aHR=1.16, 95% CI 1.01, 1.33), never married (aHR=1.20, 95% CI 1.07, 1.36), and widowed patients (aHR=1.23, 95% CI 1.02, 1.48) were significantly more likely to die from HNSCC than married/partnered patients. Conclusions: Married patients with HNC enjoy better survival outcomes than those unmarried, and those widowed and divorced have worse outcomes. This study illustrates that supportive care, in the form of being married, impacts patients, including those with end-stage disease.

scholarly journals Antibiotics modulate neoadjuvant therapy efficiency in patients with breast cancer: a pilot analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xi Zhang ◽  
Long Yu ◽  
Jiajie Shi ◽  
Sainan Li ◽  
Shiwei Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Therapy ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Antibiotic Administration ◽  
Hazards Model ◽  
Kaplan Meier ◽  
The Impact

AbstractMounting evidence suggests that microbiota dysbiosis caused by antibiotic administration is a risk factor for cancer, but few research reports focus on the relationships between antibiotics and chemotherapy efficiency. We evaluated the influence of antibiotic administration on neoadjuvant therapy efficacy in patients with breast cancer (BC) in the present study. BC patients were stratified into two groups: antibiotic-treated and control based on antibiotic administration within 30 days after neoadjuvant therapy initiation. Disease-free survival (DFS) and overall survival (OS) were assessed using the Kaplan–Meier method, and the Cox proportional hazards model was used for multivariate analyses. The pathologic complete response rate of the control group was significantly higher than that of the antibiotic-treated group (29.09% vs. 10.20%, p = 0.017). Further univariate analysis with Kaplan–Meier calculations demonstrated that antibiotic administration was strongly linked with both reduced DFS (p = 0.04) at significant statistical levels and OS (p = 0.088) at borderline statistical levels. Antibiotic administration was identified as a significant independent prognostic factor for DFS [hazard ratio (HR) 3.026, 95%, confidence interval (CI) 1.314–6.969, p = 0.009] and OS (HR 2.836, 95% CI 1.016–7.858, p = 0.047) by Cox proportional hazards model analysis. Antibiotics that initiated reduced efficiency of chemotherapy were more noticeable in the HER2-positive subgroup for both DFS (HR 5.51, 95% CI 1.77–17.2, p = 0.003) and OS (HR 7.0395% CI 1.94–25.53, p = 0.003), as well as in the T3-4 subgroup for both DFS (HR 20.36, 95% CI 2.41–172.07, p = 0.006) and OS (HR 13.45, 95% CI 1.39–130.08, p = 0.025) by stratified analysis. Antibiotic administration might be associated with reduced efficacy of neoadjuvant therapy and poor prognosis in BC patients. As a preliminary study, our research made preparations for further understanding and large-scale analyses of the impact of antibiotics on the efficacy of neoadjuvant therapy.

scholarly journals Integrating multi-omics data with deep learning for predicting cancer prognosis

2019 ◽  
Author(s):  
Hua Chai ◽  
Xiang Zhou ◽  
Zifeng Cui ◽  
Jiahua Rao ◽  
Zheng Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Small Sample ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Omics Data ◽  
Hazards Model ◽  
Small Sample Sizes ◽  
The Impact

AbstractMotivationAccurately predicting cancer prognosis is necessary to choose precise strategies of treatment for patients. One of effective approaches in the prediction is the integration of multi-omics data, which reduces the impact of noise within single omics data. However, integrating multi-omics data brings large number of redundant variables and relative small sample sizes. In this study, we employed Autoencoder networks to extract important features that were then input to the proportional hazards model to predict the cancer prognosis.ResultsThe method was applied to 12 common cancers from the Cancer Genome Atlas. The results show that the multi-omics averagely improves 4.1% C-index for prognosis prediction over single mRNA data, and our method outperforms previous approaches by at least 7.4%. A comparison of the contribution of single omics data show that mRNA contributes the most, followed by the DNA methylation, miRNA, and the copy number variation. In the case study for differential gene expression analysis, we identified 161 differentially expressed genes in the cervical cancer, among which 77 genes (65.8%) have been proven to be associated with cancer. In addition, we performed the cross-cancer test where the model trained on one cancer was used to predict the prognosis of another cancer, and found 23 pairs of cancers have a C-index larger than 0.5, with the largest value of 0.68. Thus, this study has provided a deep learning framework to effectively integrate multiple omics data to predict cancer prognosis.

scholarly journals Exploring the Dynamics of Triglyceride With Stroke Onset in the Chinese Population Through Parameterized Joint Models

2021 ◽  
Author(s):  
Qian Mao ◽  
Wenfeng Gao ◽  
Liping Yang ◽  
Qian Zhao ◽  
Yujie Liu ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Stroke Onset ◽  
Shanxi Province ◽  
Joint Models ◽  
Dynamic Changes ◽  
Hazards Model ◽  
The Impact

Abstract Background: Stroke has become one of the diseases with the highest mortality and disability rates in the world, especially in low-income and developing countries. Our objective was to discuss the relationship between the longitudinal dynamic changes of TG and stroke onset in healthy population by constructing different parametric joint models.Methods: 298 participants aged 23 to 69 in Xijing hospital of Xi’an City in Shanxi Province from 2008 to 2015 were included. The Cox proportional hazards model was performed to analyze the correlation between TG and stroke incidence at baseline. Different parameterized joint models were used to analyze the impact of dynamic changes of TG on the incidence of stroke under longitudinal data.Results: Of the 298 participants, a total of 70 (23.49%) subjects developed stroke during the study period. Cox proportional hazards model showed that the risk of disease increased by 1.056 times (95%CI=0.920-0.975) for each 1 unit of baseline age decrease. Each 1 mmol/L increase in sqrt(TG) increased the risk by 1.816 times (95%CI=1.017-3.245). Joint model showed that the risk of sqrt (TG) increased by 4.869 times (95%CI=3.987-8.857) for each 1 mmol/L increase in longitudinal direction.The lagged effects (HR=5.284, 95%CI=4.397-9.680) and cumulative effects (HR=1.786, 95%CI=1.613-3.399) of sqrt (TG) dynamic trajectory were also statistically related to the incidence of stroke.Conclusions: Over time, the longitudinal growth of TG levels in individuals will increase the risk of stroke even more. People should pay more attention to the dynamic changes of individual TG value, as well as the lagged effect and cumulative effect, to reduce the incidence of stroke.

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