Some Well-Known Parametric Survival Distributions and Their Applications

2003 ◽  
pp. 134-161 ◽  
Keyword(s):  
Parametric Survival

scholarly journals Lower extremity reamputation in people with diabetes: a systematic review and meta-analysis

2021 ◽  
Vol 9 (1) ◽  
pp. e002325
Author(s):  
Rongqi Liu ◽  
Brian J Petersen ◽  
Gary M Rothenberg ◽  
David G Armstrong
Keyword(s):  
Lower Extremity ◽  
Temporal Trend ◽  
Meta Analysis ◽  
Kendall Test ◽  
Incidence Rates ◽  
Contralateral Limb ◽  
Free Survival ◽  
Patients With Diabetes ◽  
Parametric Survival ◽  
Qualitative Characteristics

In this study, we determined the reamputation-free survival to both limbs and to the contralateral limb only following an index amputation of any-level and assessed whether reamputation rates have changed over time. We completed a systematic search using PubMed and screened a total of 205 articles for data on reamputation rates. We reported qualitative characteristics of 56 studies that included data on reamputation rates and completed a meta-analysis on 22 of the studies which enrolled exclusively participants with diabetes. The random-effects meta-analysis fit a parametric survival distribution to the data for reamputations to both limbs and to the contralateral limb only. We assessed whether there was a temporal trend in the reamputation rate using the Mann-Kendall test. Incidence rates were high for reamputation to both limbs and to the contralateral limb only. At 1 year, the reamputation rate for all contralateral and ipsilateral reamputations was found to be 19% (IQR=5.1%–31.6%), and at 5 years, it was found to be 37.1% (IQR=27.0%–47.2%). The contralateral reamputation rate at 5 years was found to be 20.5% (IQR=13.3%–27.2%). We found no evidence of a trend in the reamputation rates over more than two decades of literature analyzed. The incidence of lower extremity reamputation is high among patients with diabetes who have undergone initial amputations secondary to diabetes, and rates of reamputation have not changed over at least two decades.

scholarly journals Different approaches to quantify years of life lost from COVID-19

2021 ◽  
Author(s):  
Tamás Ferenci
Keyword(s):  
Years Of Life Lost ◽  
Expected Number ◽  
Long Term Conditions ◽  
Developed Country ◽  
Individual Patient Level ◽  
Level Data ◽  
Parametric Survival Model ◽  
Epidemiologic Risk ◽  
Parametric Survival

AbstractThe burden of an epidemic is often characterized by death counts, but this can be misleading as it fails to acknowledge the age of the deceased patients. Years of life lost is therefore widely used as a more relevant metric, however, such calculations in the context of COVID-19 are all biased upwards: patients dying from COVID-19 are typically multimorbid, having far worse life expectation than the general population. These questions are quantitatively investigated using a unique Hungarian dataset that contains individual patient level data on comorbidities for all COVID-19 deaths in the country. To account for the comorbidities of the patients, a parametric survival model using 11 important long-term conditions was used to estimate a more realistic years of life lost. As of 12 May, 2021, Hungary reported a total of 27,837 deaths from COVID-19 in patients above 50 years of age. The usual calculation indicates 10.5 years of life lost for each death, which decreases to 9.2 years per death after adjusting for 11 comorbidities. The expected number of years lost implied by the life table, reflecting the mortality of a developed country just before the pandemic is 11.1 years. The years of life lost due to COVID-19 in Hungary is therefore 12% or 1.3 years per death lower when accounting for the comorbidities and is below its expected value, but how this should be interpreted is still a matter of debate. Further research is warranted on how to optimally integrate this information into epidemiologic risk assessments during a pandemic.

Sustainability Time and Its Determinant Factors of Small and Medium-scale Enterprises in East Gojjam Zone: Parametric Survival Regression Model

2021 ◽  
pp. 097215092098865
Author(s):  
Amare Wubishet Ayele ◽  
Abebaw Bizuayehu Derseh
Keyword(s):  
Regression Model ◽  
Marketing Channel ◽  
Educational Status ◽  
Weibull Regression ◽  
Service Sectors ◽  
Weibull Regression Model ◽  
Parametric Survival ◽  
Prospective Study Design ◽  
Significant Factors ◽  
Survival Regression

The contributions of small and medium-sized enterprises (SMEs) to socio-economic development are generally recognized, but they have faced several obstacles that impede their sustainability. This manuscript seeks to identify factors for the survival of SMEs in the East Gojjam Zone, Ethiopia. The prospective study design was employed. Both descriptive and inferential statistics, particularly families of parametric survival regression models, have been used. Of the 650 enterprises included in this study, 330 (50.8%) were censored (sustained enterprises) and the remaining 320 (49.2%) were events or withdrawn enterprises. The findings of this study revealed that the incidence of termination or withdrawal of SMEs in the study area is relatively common. The results from multivariable Weibull regression model revealed that woreda, sector, manger profile (gender, age, educational status, experience (in year) and source of experience), working place, marketing channel and profitability district status of enterprise were found to be statistically significant factors for the sustainability of enterprises in the study area. The bodies concerned, in particular the enterprise administrative offices at various levels, should work with collaborative organizations to develop a strong marketing platform (network), should be able to make workplaces accessible with the required infrastructure at minimal rental costs, and should prioritize the type of sector that has the highest customer needs at the onset, for instance, agriculture and service sectors.

scholarly journals Long-Term Survival Following Traumatic Brain Injury: A Population-Based Parametric Survival Analysis

2016 ◽  
Vol 47 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Gordon W. Fuller ◽  
Jeanine Ransom ◽  
Jay Mandrekar ◽  
Allen W. Brown
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
General Population ◽  
Life Expectancy ◽  
Health Planning ◽  
Population Based ◽  
Post Injury ◽  
Term Survival ◽  
Parametric Survival

Background: Long-term mortality may be increased following traumatic brain injury (TBI); however, the degree to which survival could be reduced is unknown. We aimed at modelling life expectancy following post-acute TBI to provide predictions of longevity and quantify differences in survivorship with the general population. Methods: A population-based retrospective cohort study using data from the Rochester Epidemiology Project (REP) was performed. A random sample of patients from Olmsted County, Minnesota with a confirmed TBI between 1987 and 2000 was identified and vital status determined in 2013. Parametric survival modelling was then used to develop a model to predict life expectancy following TBI conditional on age at injury. Survivorship following TBI was also compared with the general population and age- and gender-matched non-head injured REP controls. Results: Seven hundred and sixty nine patients were included in complete case analyses. The median follow-up time was 16.1 years (interquartile range 9.0-20.4) with 120 deaths occurring in the cohort during the study period. Survival after acute TBI was well represented by a Gompertz distribution. Victims of TBI surviving for at least 6 months post-injury demonstrated a much higher ongoing mortality rate compared to the US general population and non-TBI controls (hazard ratio 1.47, 95% CI 1.15-1.87). US general population cohort life table data was used to update the Gompertz model's shape and scale parameters to account for cohort effects and allow prediction of life expectancy in contemporary TBI. Conclusions: Survivors of TBI have decreased life expectancy compared to the general population. This may be secondary to the head injury itself or result from patient characteristics associated with both the propensity for TBI and increased early mortality. Post-TBI life expectancy estimates may be useful to guide prognosis, in public health planning, for actuarial applications and in the extrapolation of outcomes for TBI economic models.

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