Assessment of Deterioration of Highway Pavement using Bayesian Survival Model

2020 ◽  
Vol 2674 (6) ◽  
pp. 310-325
Author(s):  
Sylvester Inkoom ◽  
John O. Sobanjo ◽  
Eric Chicken ◽  
Debajyoti Sinha ◽  
Xufeng Niu
Keyword(s):  
Failure Time ◽  
Survival Function ◽  
Survival Model ◽  
Survival Models ◽  
Survival Times ◽  
Underlying Distribution ◽  
Kaplan Meier ◽  
Bayesian Survival Model ◽  
Parametric Survival ◽  
Highway Pavement

The size and level of complexity of highway pavement data and its associated covariates have led to the application of different approaches in the analysis of the highway pavement data for deterioration modeling. With the goal of predicting the survival of highway pavement with interpretable and reproducible models that are robust to uncertainties, errors, and overfitting, the Bayesian survival model (BSM) is proposed in this paper as a good method of estimating parameters for survival functions. Deterioration patterns in relation to the failure time distribution are treated as random quantities sampled from some stochastic prior processes. The specified priors are combined with the data sampled to obtain the distribution of the survival function using Bayes theorem and the Markov chain Monte Carlo method. A posteriori distribution of the survival function is obtained from the pavement information and compared with the classical product limit survival (Kaplan-Meier) estimate and the univariate parametric survival function. This paper reports experimental results of the three candidate models and their efficiency in describing the survival of highway pavement in the presence of deterioration. It is observed from the BSM outcomes that the posterior estimates are accurate in estimating the survival times of roadway segments at 95% credible interval. The outputs also show the robustness of the BSM in describing the uncertainties associated with the survival of highway pavement compared with the Kaplan-Meier and the univariate parametric survival models in the event of limited data and misspecification of underlying distribution.

scholarly journals Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?

2018 ◽  
Vol 28 (8) ◽  
pp. 2475-2493 ◽  
Author(s):  
NR Latimer ◽  
IR White ◽  
KR Abrams ◽  
U Siebert
Keyword(s):  
Treatment Effect ◽  
Failure Time ◽  
Survival Function ◽  
Experimental Treatment ◽  
Control Group ◽  
Survival Times ◽  
Term Survival ◽  
Long Term Survival ◽  
Treatment Switching

Treatment switching often has a crucial impact on estimates of effectiveness and cost-effectiveness of new oncology treatments. Rank preserving structural failure time models (RPSFTM) and two-stage estimation (TSE) methods estimate ‘counterfactual’ (i.e. had there been no switching) survival times and incorporate re-censoring to guard against informative censoring in the counterfactual dataset. However, re-censoring causes a loss of longer term survival information which is problematic when estimates of long-term survival effects are required, as is often the case for health technology assessment decision making. We present a simulation study designed to investigate applications of the RPSFTM and TSE with and without re-censoring, to determine whether re-censoring should always be recommended within adjustment analyses. We investigate a context where switching is from the control group onto the experimental treatment in scenarios with varying switch proportions, treatment effect sizes, treatment effect changes over time, survival function shapes, disease severity and switcher prognosis. Methods were assessed according to their estimation of control group restricted mean survival that would be observed in the absence of switching, up to the end of trial follow-up. We found that analyses which re-censored usually produced negative bias (i.e. underestimating control group restricted mean survival and overestimating the treatment effect), whereas analyses that did not re-censor consistently produced positive bias which was often smaller in magnitude than the bias associated with re-censored analyses, particularly when the treatment effect was high and the switching proportion was low. The RPSFTM with re-censoring generally resulted in increased bias compared to the other methods. We believe that analyses should be conducted with and without re-censoring, as this may provide decision-makers with useful information on where the true treatment effect is likely to lie. Incorporating re-censoring should not always represent the default approach when the objective is to estimate long-term survival times and treatment effects.

scholarly journals The Differentiation of Firm Survival Models in the Poviats of the Zachodniopomorskie Voivodeship

2017 ◽  
Vol 4 (330) ◽  
Author(s):  
Iwona Markowicz
Keyword(s):  
Survival Function ◽  
Stage Iv ◽  
Firm Survival ◽  
Intensity Function ◽  
Stage Iii ◽  
Survival Models ◽  
Survival Duration ◽  
Kaplan Meier ◽  
Study Results

The aim of the study was to construct models of firms’ survival duration for individual poviats in the Zachodniopomorskie Voivodeship. The first stage was the calculation of the Kaplan‑Meier estimator and the use of a test for the verification of similarities in the survival function for the analysed poviats. As a result, groups of poviats were created. The next stage of research was the construction of duration tables of the studied firms and an analysis of the intensity function of firms’ liquidation for the poviats. The percentage of firms liquidated after two years of activity in different poviats (stage III) was presented. An analysis of correlation between the percentage of firms liquidated in the analysed period and the number of entities registered per 10 thousand of population in the poviats (stage IV) was also conducted. This study used data from the registry of REGON related to companies established in the Zachodniopomorskie Voivodeship in 2009–2011. These entities were observed till the end of 2013. The study results reveal the differentiation of firm survival models in the poviats of the Zachodniopomorskie Voivodeship. Five groups of poviats were distinguished and characterised.

Do institutional networks affect winery survival?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marta Fernández-Olmos ◽  
Ana Felicitas Gargallo-Castel ◽  
Giulio Malorgio
Keyword(s):  
Policy Implications ◽  
Wine Industry ◽  
Primary Data ◽  
Survival Models ◽  
Data Set ◽  
Policy Makers ◽  
Content Type ◽  
Probability Of Survival ◽  
Kaplan Meier ◽  
Parametric Survival

PurposeThe present study aims to provide new evidence regarding the factors that determine the survival of firms in the Spanish wine industry and to improve the understanding of sector dynamics.Design/methodology/approachThe empirical analysis, conducted over a representative sample of wineries in the DOC Rioja wine industry, is based on non-parametric (Kaplan–Meier graph) and semi-parametric survival models (Cox proportional hazard model).FindingsThe empirical model finds that wineries with a higher number of networks with institutions enjoy better survival prospects. This study also shows that a winery’s previous performance affects the winery’s survival probability; therefore, successful wineries in the past encounter a smaller hazard of exit. Although spending on R&D and exporting are factors likely to improve wineries' efficiency and competitiveness, these factors did not contribute significantly to the survival of DOC Rioja wineries.Originality/valueThis paper makes a significant contribution to the understanding of the determinants of wineries' survival and has important policy implications. In order to raise the probability of survival, policy makers should promote the networks that link wineries and institutions. Moreover, this study is based on survival analysis which, although frequently used in medical and behavioural sciences, has rarely been applied to wine economics. Finally, it uses a unique data set obtained from primary data collection, which previous studies have not analysed in relation to the probability of winery survival.

The importance of adjusting for treatment crossover bias in oncology clinical trials: An analysis of the EGF104900 trial in metastatic breast cancer (mBC).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 636-636
Author(s):  
Norman Paracha ◽  
Andrew Davies ◽  
Nicholas Latimer ◽  
Christopher Parker ◽  
Stuart Mealing ◽  
...  
Keyword(s):  
Failure Time ◽  
Metastatic Breast ◽  
Phase Iii ◽  
Survival Times ◽  
Time Model ◽  
Phase Iii Trial ◽  
Oncology Clinical Trials ◽  
Event Times ◽  
Intent To Treat ◽  
Parametric Survival

636 Background: EGF104900 is a phase III trial in mBC that compared lapatinib + trastuzumab (LAPTRZ, n=146) with LAP monotherapy (LAP, n=145). The trial allowed LAP subjects to switch to LAPTRZ on documented disease progression (following ≥4 weeks of LAP). Conventional intent-to-treat (ITT) analysis does not control for potential bias due to treatment crossover. The Rank Preserving Structural Failure Time Model (RPSFTM) estimates event times had patients not switched, and performs re-censoring. The approach preserves randomization and ordering of patients’ observed survival times, assuming that patients switching benefit from the treatment effect seen in those initially randomized to the treatment arm. The method is recognized by NICE, UK, as an appropriate method for adjusting for crossover bias. Methods: 53% of LAP subjects crossed over to LAPTRZ. Analyses were stratified by hormone receptor status and visceral/non-visceral disease. RPSFTM was implemented in Stata (using White’s strbee procedure). Absolute overall survival (OS) was estimated using a parametric survival distribution fitted to the trial data with/without crossover adjustment. The unadjusted ITT Cox hazard ratio (HR) was compared with the crossover adjusted RPSFTM estimate, and the absolute gain in OS evaluated. Results: Median OS in the LAPTRZ and LAP arms was 61 and 41 weeks, respectively (ITT HR 0.74, 95% CI: 0.56, 0.96); RPSFTM crossover-adjusted median OS for LAP was 32 weeks (RPSFTM HR 0.52; 95% CI: 0.29, 0.92). Mean OS for LAPTRZ was estimated under a Weibull distribution as 74 weeks, compared with 57 (ITT) and 44 (RPSFTM) weeks for LAP. Treatment with LAPTRZ was estimated to extend mean OS by 30 weeks controlling for treatment crossover using RPSFTM compared with 17 weeks by ITT. Conclusions: Oncology trials are often subject to treatment crossover. Controlling for potential treatment crossover bias can result in greater estimates of gain in OS compared with ITT analysis. In the context of mBC such differences are of great importance to patients, clinicians, and healthcare payers. Treatment crossover-analyses are therefore also important for estimating cost-effectiveness in oncology.

scholarly journals How Uncertain is the Survival Extrapolation? A Study of the Impact of Different Parametric Survival Models on Extrapolated Uncertainty About Hazard Functions, Lifetime Mean Survival and Cost Effectiveness

2019 ◽  
Vol 38 (2) ◽  
pp. 193-204
Author(s):  
Ben Kearns ◽  
John Stevens ◽  
Shijie Ren ◽  
Alan Brennan
Keyword(s):  
Cost Effectiveness ◽  
Case Studies ◽  
Good Practice ◽  
Survival Model ◽  
Survival Models ◽  
Multivariate Normal ◽  
Hazard Functions ◽  
Point Estimates ◽  
Parametric Survival ◽  
The Impact

Abstract Background and Objective The extrapolation of estimated hazard functions can be an important part of cost-effectiveness analyses. Given limited follow-up time in the sample data, it may be expected that the uncertainty in estimates of hazards increases the further into the future they are extrapolated. The objective of this study was to illustrate how the choice of parametric survival model impacts on estimates of uncertainty about extrapolated hazard functions and lifetime mean survival. Methods We examined seven commonly used parametric survival models and described analytical expressions and approximation methods (delta and multivariate normal) for estimating uncertainty. We illustrate the multivariate normal method using case studies based on four representative hypothetical datasets reflecting hazard functions commonly encountered in clinical practice (constant, increasing, decreasing, or unimodal), along with a hypothetical cost-effectiveness analysis. Results Depending on the survival model chosen, the uncertainty in extrapolated hazard functions could be constant, increasing or decreasing over time for the case studies. Estimates of uncertainty in mean survival showed a large variation (up to sevenfold) for each case study. The magnitude of uncertainty in estimates of cost effectiveness, as measured using the incremental cost per quality-adjusted life-year gained, varied threefold across plausible models. Differences in estimates of uncertainty were observed even when models provided near-identical point estimates. Conclusions Survival model choice can have a significant impact on estimates of uncertainty of extrapolated hazard functions, mean survival and cost effectiveness, even when point estimates were similar. We provide good practice recommendations for analysts and decision makers, emphasizing the importance of considering the plausibility of estimates of uncertainty in the extrapolated period as a complementary part of the model selection process.

scholarly journals A nationwide, retrospective, data-linkage, cohort study of epilepsy and incident dementia

Neurology ◽  
2020 ◽  
Vol 95 (12) ◽  
pp. e1686-e1693
Author(s):  
Christian Schnier ◽  
Susan Duncan ◽  
Tim Wilkinson ◽  
Gashirai K. Mbizvo ◽  
Richard F.M. Chin
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Data Linkage ◽  
Survival Models ◽  
Retrospective Data ◽  
Kaplan Meier ◽  
Incident Dementia ◽  
History Of ◽  
Associated Risk Factors ◽  
Parametric Survival

ObjectiveTo determine the association of epilepsy with incident dementia by conducting a nationwide, retrospective data-linkage, cohort study to examine whether the association varies according to dementia subtypes and to investigate whether risk factors modify the association.MethodsWe used linked health data from hospitalization, mortality records, and primary care consultations to follow up 563,151 Welsh residents from their 60th birthday to estimate dementia rate and associated risk factors. Dementia, epilepsy, and covariates (medication, smoking, comorbid conditions) were classified with the use of previously validated code lists. We studied rate of dementia and dementia subtypes in people with epilepsy (PWE) and without epilepsy using (stratified) Kaplan-Meier plots and flexible parametric survival models.ResultsPWE had a 2.5 (95% confidence interval [CI] 2.3–2.6) times higher hazard of incident dementia, a 1.6 (95% CI 1.4–1.8) times higher hazard of incident Alzheimer disease (AD), and a 3.1 (95% CI 2.8–3.4) times higher hazard of incident Vascular dementia (VaD). A history of stroke modified the increased incidence in PWE. PWE who were first diagnosed at ≤25 years of age had a dementia rate similar to that of those diagnosed later in life. PWE who had ever been prescribed sodium valproate compared to those who had not were at higher risk of dementia (hazard ratio [HR] 1.6, 99% CI 1.4–1.9) and VaD (HR 1.7, 99% CI 1.4–2.1) but not AD (HR 1.2, 99% CI 0.9–1.5).ConclusionPWE compared to those without epilepsy have an increased dementia risk.

scholarly journals Survival analysis of asthma patients attended to at Chitungwiza Central Hospital in Zimbabwe

2019 ◽  
Author(s):  
Pisirai Ndarukwa ◽  
Moses John Chimbari ◽  
Elopy Sibanda
Keyword(s):  
Cox Regression ◽  
Survival Function ◽  
Smoking History ◽  
Rank Test ◽  
Global Public Health ◽  
Central Hospital ◽  
Survival Times ◽  
Hospital Data ◽  
Kaplan Meier ◽  
Asthma Patients

Abstract Background Asthma is one of the leading global public health problems with an estimated 300 thousand deaths occurring annually worldwide. Deaths due to asthma in Zimbabwe reached 1 301 or 1.02% of total deaths in 2014. The association between asthma survival and socio-demographic and pathologic factors has not been done in Zimbabwe. We aimed to determine the survival of asthma patients at Chitungwiza Central Hospital in Zimbabwe over a period of 20 years. Methods Records for 158 asthma patients were analysed in this retrospective cohort study. The patient records were sampled from the computerised health information department at the hospital. Data were collected using a patient record checklist which was divided into four sections: (i) demographic information, (ii) clinical characteristics of asthma patients, (iii) health service utilization and (iv) asthma self-management. Descriptive data analysis was performed using the Kaplan Meier survival function curves. The Kaplan Meier survival curves were differentiated by the log-rank test, median survival times and mortality rates. Significant hazard ratios were used for multivariate cox regression model and a test on proportional hazards assumption based on Schoenfeld residuals was conducted. Results The total follow-up time was 2208 person years. The majority of the participants (60.7%) were female. The mortality rate was 61.4%. The median age at death was 25.5 years (IQR; 21-34). Smoking history [p=<0.001], presence of respiratory disease and cardiovascular disease [p=0.002] were significantly associated with higher mortality. Having an income level

Comprehensive Analysis of Autophagy-related Genes in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Li Wang ◽  
Jialin Qu ◽  
Na Zhou ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Survival Model ◽  
Survival Models ◽  
Clinicopathological Parameters ◽  
Lasso Regression ◽  
Kaplan Meier ◽  
Human Protein Atlas ◽  
Independent Risk Factors

Abstract Background: Hepatocellular carcinoma (HCC) is the common type of cause of cancer-related death among human cancers. There are ample evidences to showing that autophagy-related genes (ARGs) may play a significant role in the biological process of HCC. Methods: In this study, we aim to identify survival model and nomogram that could effectively predict the prognosis of HCC based on ARGs. First, we download the data of HCC patients from TCGA database. Second, we analysis the function of ARGs by utilized GO and the KEGG analysis. Finally, we screen 5 ARGs (SQSTM1, CAPN10, EIF2S1, ATIC, RHEB) for survival model by performed the Cox regression and Lasso regression analysis. We further built and verified a prognostic nomogram base on prognostic ARGs. Moreover, its efficacy was validated by the ICGC database. The expressions level of 5 ARGs was performed using Oncomine database, the Human Protein Atlas and Kaplan-Meier plotter.Result: We found patients the survival of patients in the different groups was significantly different both in the TCGA cohort and ICGC cohort. The survival model showed good performance for predicting the prognosis of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion: our survival models and prognostic ARGs nomogram can be independent risk factors for hepatocellular carcinoma patients.

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