Gamma-Gompertz shared frailty model for analysis of the time of stay in an Anglo-Nubian goat herd

Purpose: To determine the association between training-load (TL) factors, baseline characteristics, and new injury and/or pain (IP) risk in an endurance sporting population (ESP). Methods: Ninety-five ESP participants from running, triathlon, swimming, cycling, and rowing disciplines initially completed a questionnaire capturing baseline characteristics. TL and IP data were submitted weekly over a 52-wk study period. Cumulative TL factors, acute:chronic workload ratios, and exponentially weighted moving averages were calculated. A shared frailty model was used to explore time to new IP and association to TL factors and baseline characteristics. Results: 92.6% of the ESP completed all 52 wk of TL and IP data. The following factors were associated with the lowest risk of a new IP episode: (a) a low to moderate 7-d lag exponentially weighted moving averages (0.8–1.3: hazard ratio [HR] = 1.21; 95% confidence interval [CI], 1.01–1.44; P = .04); (b) a low to moderate 7-d lag weekly TL (1200–1700 AU: HR = 1.38; 95% CI, 1.15–1.65; P < .001); (c) a moderate to high 14-d lag 4-weekly cumulative TL (5200–8000 AU: HR = 0.33; 95% CI, 0.21–0.50; P < .001); and (d) a low number of previous IP episodes in the preceding 12 mo (1 previous IP episode: HR = 1.11; 95% CI, 1.04–1.17; P = .04). Conclusions: To minimize new IP risk, an ESP should avoid high spikes in acute TL while maintaining moderate to high chronic TLs. A history of previous IP should be considered when prescribing TLs. The demonstration of a lag between a TL factor and its impact on new IP risk may have important implications for future ESP TL analysis.

Download Full-text

A time-varying shared frailty model with application to infectious diseases

The Annals of Applied Statistics ◽

10.1214/13-aoas693 ◽

2014 ◽

Vol 8 (1) ◽

pp. 430-447 ◽

Cited By ~ 7

Author(s):

Doyo G. Enki ◽

Angela Noufaily ◽

C. Paddy Farrington

Keyword(s):

Infectious Diseases ◽

Frailty Model ◽

Time Varying ◽

Shared Frailty Model ◽

Shared Frailty

Download Full-text

Determinants of Time-to-recovery from Hypertension using a Comparison of Various Parametric Shared Frailty Models: In Case of Felege Hiwot Refral Hospital.

10.21203/rs.3.rs-608412/v1 ◽

2021 ◽

Author(s):

Nigist Mulu ◽

Yeshambel Kindu ◽

Abay Kassie

Keyword(s):

Survival Time ◽

Blood Urea Nitrogen ◽

Normal Condition ◽

Recovery Time ◽

Frailty Model ◽

Frailty Models ◽

Inverse Gaussian ◽

Urea Nitrogen ◽

Shared Frailty Model ◽

Shared Frailty

Abstract Background: Hypertension is a major public health problem that is responsible for morbidity and mortality. In Ethiopia hypertension is becoming a double burden due to urbanization. The study aimed to identify factors that affect time-to-recovery from hypertension at Felege Hiwot Referral Hospital. Retrospective study design was used at FHRH. Methods: The data was collected in patient’s chart from September 2016 to January 2018. Kaplan-Meier survival estimate and Log-Rank test were used to compare the survival time. The AFT and parametric shared frailty models were employed to identify factors associated with the recovery time of hypertension patients. All the fitted models were compared by using AIC and BIC. Results: Eighty one percent of sampled patients were recovered to normal condition and nineteen percent of patients were censored observations. The median survival time of hypertensive patients to attain normal condition was 13 months. Weibull- inverse Gaussian shared frailty model was found to be the best model for predicting recovery time of hypertension patients. The unobserved heterogeneity in residences as estimated by the Weibull-Inverse Gaussian shared frailty model was θ=0.385 (p-value=0.00). Conclusion: The final model showed that age, systolic blood pressure, related disease, creantine, blood urea nitrogen and the interaction between blood urea nitrogen and age were the determinants factors of recovery status of patients at 5% level of significance. The result showed that patients creantine >1.5 Mg/dl compared to creantine ≤1.5 Mg/dl and SBP were prolonged the recovery time of patients whereas patients having kidney disease, other disease and had no any disease compared to diabetic patients and the interaction BUN and age were shorten recovery status of hypertension patients.

Download Full-text

A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis

Statistical Methods in Medical Research ◽

10.1177/0962280219890893 ◽

2019 ◽

Vol 29 (8) ◽

pp. 2295-2306 ◽

Cited By ~ 1

Author(s):

MC Jones ◽

Angela Noufaily ◽

Kevin Burke

Keyword(s):

Survival Analysis ◽

Weibull Distribution ◽

Survival Data ◽

Frailty Model ◽

Weibull Model ◽

Flexible Parametric Model ◽

Shared Frailty Model ◽

Shared Frailty ◽

Power Variance ◽

Parametric Survival

We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we argued in favour of an adapted form of the ‘power generalized Weibull’ distribution as an attractive vehicle for univariate parametric survival analysis. Here, we additionally observe a frailty relationship between a power generalized Weibull distribution with one value of the parameter which controls distributional choice within the family and a power generalized Weibull distribution with a smaller value of that parameter. We exploit this relationship to propose a bivariate shared frailty model with power generalized Weibull marginal distributions linked by the BB9 or ‘power variance function’ copula, then change it to have adapted power generalized Weibull marginals in the obvious way. The particular choice of copula is, therefore, natural in the current context, and the corresponding bivariate adapted power generalized Weibull model a novel combination of pre-existing components. We provide a number of theoretical properties of the models. We also show the potential of the bivariate adapted power generalized Weibull model for practical work via an illustrative example involving a well-known retinopathy dataset, for which the analysis proves to be straightforward to implement and informative in its outcomes.

Download Full-text