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 ofWisconsin 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

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 Analyses for Bridge Decks in Northern United States

The use of deicing salts in northern regions of the United States is a major contributor to the long-term deterioration of bridge decks. In this study, the 2008 U.S. National Bridge Inventory (NBI) records were used to develop survival models for non-reconstructed bridge decks in six northern states of Minnesota, Wisconsin, Michigan, Ohio, Pennsylvania, and New York. The hypertabastic accelerated failure model was used to develop survival (reliability) and hazard (failure rate) functions for all six states. The NBI parameters included were the deck rating, type of superstructure (concrete or steel), deck surface area, age, and average daily traffic (ADT). A recorded NBI deck rating of 5 was considered to be the end of service life. Results show that ADT and deck surface area are both important factors affecting reliability and failure rates in all six states studied. In general, deck reliability and failure rates correspond reasonably well with qualitative measure of the harshness of each state’s winters. The type of superstructure has a varied influence in different states. It is recommended that deck area and ADT be considered as important factors when planning maintenance operations

Hyperbolastic modeling of tumor growth with a combined treatment of iodoacetate and dimethylsulphoxide

<p>Abstract</p> <p>Background</p> <p>An understanding of growth dynamics of tumors is important in understanding progression of cancer and designing appropriate treatment strategies. We perform a comparative study of the hyperbolastic growth models with the Weibull and Gompertz models, which are prevalently used in the field of tumor growth.</p> <p>Methods</p> <p>The hyperbolastic growth models H1, H2, and H3 are applied to growth of solid Ehrlich carcinoma under several different treatments. These are compared with results from Gompertz and Weibull models for the combined treatment.</p> <p>Results</p> <p>The growth dynamics of the solid Ehrlich carcinoma with the combined treatment are studied using models H1, H2, and H3, and the models are highly accurate in representing the growth. The growth dynamics are also compared with the untreated tumor, the tumor treated with only iodoacetate, and the tumor treated with only dimethylsulfoxide, and the combined treatment.</p> <p>Conclusions</p> <p>The hyperbolastic models prove to be effective in representing and analyzing the growth dynamics of the solid Ehrlich carcinoma. These models are more precise than Gompertz and Weibull and show less error for this data set. The precision of H3 allows for its use in a comparative analysis of tumor growth rates between the various treatments.</p

Hypertabastic survival model

A new two-parameter probability distribution called hypertabastic is introduced to model the survival or time-to-event data. A simulation study was carried out to evaluate the performance of the hypertabastic distribution in comparison with popular distributions. We then demonstrate the application of the hypertabastic survival model by applying it to data from two motivating studies. The first one demonstrates the proportional hazards version of the model by applying it to a data set from multiple myeloma study. The second one demonstrates an accelerated failure time version of the model by applying it to data from a randomized study of glioma patients who underwent radiotherapy treatment with and without radiosensitizer misonidazole. Based on the results from the simulation study and two applications, the proposed model shows to be a flexible and promising alternative to practitioners in this field

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

The role of patient-centered communication scale in patients’ satisfaction of healthcare providers before and during the COVID-19 pandemic

Assess the effect of patient-centered communication (PCC) scale on the patient satisfaction of healthcare providers (HCPs). The 2020 Health Information National Trends Survey (HINTS) was used to analyze the patient’s satisfaction of HCPs. This survey includes 2466 patients’ responses and were analyzed using the multivariable binary Hyperbolastic regression model of type II. The study examines the effects of PCC scale on patients’ satisfaction of HCPs while controlling for pandemic status, employment, education, marital status, race, political views, waiting time status, sex, income, and age. PCC scale was the most significant predictor of patients’ satisfaction of their HCPs (P-value \u3c 0.001) followed by waiting time status (P-value \u3c 0.001), and age (P-value = 0.016). The odds of patient satisfaction with the healthcare provider services were approximately 20% higher prior to the pandemic than during the pandemic (P-value = 0.415). The odds of satisfaction for patients earning $100k+ was approximately three times more than those making less than$35,000 (P-value = 0.003). PCC scale is a powerful measure that may be used as a metric for patients’ satisfaction of HCPs. Taking steps to improve communication between HCPs and patients is a key factor in patient satisfaction. Concentrating on the seven domains of PCC will result in higher patient satisfaction of HCPs. The improvement in PCC will encourage each patient to disclose vital information about his or her health. This may increase the accuracy of diagnosis, quality of care, and health outcomes. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework (https://theberylinstitute.org/experience-framework/). Access other PXJ articles related to this lens. Access other resources related to this lens

Disparities in cervical cancer mortality rates as determined by the longitudinal hyperbolastic mixed-effects type II model.

We analyze the dynamics of cervical cancer mortality rates for African American and White women residing in 13 states located in the eastern half of the United States of America from 1975 through 2010. Despite decreasing trends in cervical cancer mortality rates for both races, racial disparities in mortality rates still exist. In all 13 states, Black women had higher mortality rates at all times. The degree of disparities and pace of decline in mortality rates over time differed among these states. In all 13 states, cervical cancer mortality rates for both racial groups have fallen. Disparities in the pace of decline in mortality rates in these states may be due to differences in the rates of screening for cervical cancers. Of note, the gap in cervical cancer mortality rates between Black women and White women is narrowing

This Article Corrects: “Assessment of Physician Well-being, Part Two: Beyond Burnout”

Part One of this two-article series reviews assessment tools to measure burnout and other negative states. Physician well-being goes beyond merely the absence of burnout. Transient episodes of burnout are to be expected. Measuring burnout alone is shortsighted. Well-being includes being challenged, thriving, and achieving success in various aspects of personal and professional life. In this second part of the series, we identify and describe assessment tools related to wellness, quality of life, resilience, coping skills, and other positive states