Hypertabastic survival model

The role of histological subtypes in the survival of patients diagnosed with cutaneous or mucosal melanoma in the United States of America

BACKGROUND

Literature presents limited information on histological subtypes and their association with other factors influencing the survival of melanoma patients. To explore the risk of death due to melanoma associated with histological subtypes, this retrospective study used the Surveillance, Epidemiology, and End Results program (SEER) data from 1998 to 2019.

METHODS

A total of 27,532 patients consisting of 15,527 males and 12,005 females. The Hypertabastic Accelerated Failure Time model was used to analyze the impact of histology on the survival of patients with cutaneous or mucosal melanoma.

RESULTS

The median survival time (MST) for cutaneous patients was 149 months, whereas those diagnosed with mucosal melanoma was 34 months. Nodular melanoma had a hazard ratio of 3.40 [95% CI: (2.94, 3.94)] compared to lentigo maligna melanoma. Across all histological subtypes, females had a longer MST, when compared to males. The hazard ratio (HR) of distant to localized melanoma was 9.56 [95% CI: (7.58, 12.07)].

CONCLUSIONS

Knowledge of patients' histological subtypes and their hazard assessment would enable clinicians and healthcare providers to perform personalized treatment, resulting in a lower risk of complication and higher survivability of melanoma patients. Significant factors were stage of the disease, age, histology, sex, and income. Focus should be placed on high-risk populations with severe and aggressive histological subtypes. Programs that emphasize preventive measures such as awareness, education, and early screening could reduce risk.

A Comprehensive Analysis of the Effect of Histological Subtypes on the Survival Probability of Kidney Carcinoma Patients: A Hypertabastic Survival Analysis

The purpose of this study is two-fold. First, to find out whether the histological subtypes can serve as an independent prognostic factor for kidney carcinoma; and second, whether it's role can be maintained when we control for confounders. Using National Cancer Institute data from 1975-2016, we have modeled the impact of histological subtypes on the survival probability of kidney carcinoma patients. A total of 134,150 individuals were examined from the Surveillance, Epidemiology, and End Results program (SEER) [1]. The study variables are age, race/ethnicity, sex, tumor grade, type of surgery, geographical location of patient and stage of disease. We have applied the Hypertabastic proportional hazards survival model [2-6] to analyze the survival time of patients diagnosed with kidney carcinoma in order to explore the effect of histological subtypes on their survival probability. In particular, our intention was to assess the relationship between the histological subtypes and tumor stage, grade, and type of surgery. Our results indicated that histology plays an important role both when used as the sole predictor in the survival model (P < 0.001), as well as when controlling for confounding variables (P < 0.001).

Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

Conditional survival analysis for concrete bridge decks

Bridge decks are a significant factor in the deterioration of bridges, and substantially affect long-term bridge maintenance decisions. In this study, conditional survival (reliability) analysis techniques are applied to bridge decks to evaluate the age at the end of service life using the National Bridge Inventory records. As bridge decks age, the probability of survival and the expected service life would change. The additional knowledge gained from the fact that a bridge deck has already survived a specific number of years alters (increases) the original probability of survival at subsequent years based on the conditional probability theory. The conditional expected service life of a bridge deck can be estimated using the original and conditional survival functions. The effects of average daily traffic and deck surface area are considered in the survival calculations. Using Wisconsin data, relationships are provided to calculate the probability of survival of bridge decks as well as expected service life at various ages. The concept of survival dividend is presented and the age when rapid deterioration begins is defined.

Survival Analysis of Bridge Superstructures in Wisconsin

Although survival analyses have long been used in biomedical research, their application to engineering in general, and bridge engineering in particular, is a more recent phenomenon. In this research, survival (reliability) of bridge superstructures in Wisconsin was investigated using the Hypertabastic accelerated failure time model. The 2012 National Bridge Inventory (NBI) data for the State of Wisconsin were used for the analyses. A recorded NBI superstructure condition rating of 5 was chosen as the end of service life. The type of bridge superstructure, bridge age, maximum span length (MSL) and average daily traffic (ADT) were considered as possible risk factors in the survival of bridge superstructures. Results show that ADT and MSL can substantially affect the survival of bridge superstructures at various ages. The reliability of Wisconsin superstructures at the ages of 50 and 75 years is on the order of 63% and 18%, respectively, when the ADT and MSL values are at Wisconsin’s mean values.

Complete hazard ranking to analyze right-censored data: An ALS survival study

Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

Comparison of hypertabastic survival model with other unimodal hazard rate functions using a goodness-of-fit test

We studied the problem of testing a hypothesized distribution in survival regression models when the data is right censored and survival times are influenced by covariates. A modified chi-squared type test, known as Nikulin-Rao-Robson statistic, is applied for the comparison of accelerated failure time models. This statistic is used to test the goodness-of-fit for hypertabastic survival model and four other unimodal hazard rate functions. The results of simulation study showed that the hypertabastic distribution can be used as an alternative to log-logistic and log-normal distribution. In statistical modeling, because of its flexible shape of hazard functions, this distribution can also be used as a competitor of Birnbaum-Saunders and inverse Gaussian distributions. The results for the real data application are shown. Copyright © 2017 John Wiley & Sons, Ltd.

Clinical and multiple gene expression variables in survival analysis of breast cancer: analysis with the hypertabastic survival model

BACKGROUND

We explore the benefits of applying a new proportional hazard model to analyze survival of breast cancer patients. As a parametric model, the hypertabastic survival model offers a closer fit to experimental data than Cox regression, and furthermore provides explicit survival and hazard functions which can be used as additional tools in the survival analysis. In addition, one of our main concerns is utilization of multiple gene expression variables. Our analysis treats the important issue of interaction of different gene signatures in the survival analysis.

METHODS

The hypertabastic proportional hazards model was applied in survival analysis of breast cancer patients. This model was compared, using statistical measures of goodness of fit, with models based on the semi-parametric Cox proportional hazards model and the parametric log-logistic and Weibull models. The explicit functions for hazard and survival were then used to analyze the dynamic behavior of hazard and survival functions.

RESULTS

The hypertabastic model provided the best fit among all the models considered. Use of multiple gene expression variables also provided a considerable improvement in the goodness of fit of the model, as compared to use of only one. By utilizing the explicit survival and hazard functions provided by the model, we were able to determine the magnitude of the maximum rate of increase in hazard, and the maximum rate of decrease in survival, as well as the times when these occurred. We explore the influence of each gene expression variable on these extrema. Furthermore, in the cases of continuous gene expression variables, represented by a measure of correlation, we were able to investigate the dynamics with respect to changes in gene expression.

CONCLUSIONS

We observed that use of three different gene signatures in the model provided a greater combined effect and allowed us to assess the relative importance of each in determination of outcome in this data set. These results point to the potential to combine gene signatures to a greater effect in cases where each gene signature represents some distinct aspect of the cancer biology. Furthermore we conclude that the hypertabastic survival models can be an effective survival analysis tool for breast cancer patients.