Frailty models power variance function with cure fraction and latent risk factors negative binomial

2016 ◽  
Vol 46 (19) ◽  
pp. 9763-9776 ◽  
Author(s):  
Vinicius Fernando Calsavara ◽  
Agatha Sacramento Rodrigues ◽  
Vera Lúcia Damasceno Tomazella ◽  
Mário de Castro
Keyword(s):  
Risk Factors ◽  
Negative Binomial ◽  
Variance Function ◽  
Frailty Models ◽  
Power Variance Function ◽  
Cure Fraction ◽  
Power Variance

scholarly journals Frailty models based on Lévy processes

2003 ◽  
Vol 35 (02) ◽  
pp. 532-550 ◽  
Author(s):  
Håkon K. Gjessing ◽  
Odd O. Aalen ◽  
Nils Lid Hjort
Keyword(s):  
Lévy Process ◽  
Levy Process ◽  
Frailty Models ◽  
Hazard Rates ◽  
Survival Functions ◽  
Power Variance Function ◽  
Fixed Quantity ◽  
Power Variance ◽  
The Individual ◽  
Over Time

Generalizing the standard frailty models of survival analysis, we propose to model frailty as a weighted Lévy process. Hence, the frailty of an individual is not a fixed quantity, but develops over time. Formulae for the population hazard and survival functions are derived. The power variance function Lévy process is a prominent example. In many cases, notably for compound Poisson processes, quasi-stationary distributions of survivors may arise. Quasi-stationarity implies limiting population hazard rates that are constant, in spite of the continual increase of the individual hazards. A brief discussion is given of the biological relevance of this finding.

A Mixture of Weibull Hazard Rate with a Power Variance Function Frailty

2012 ◽  
Vol 13 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Sirithip Wasinrat ◽  
Winai Bodhisuwan ◽  
Panlop Zeephongse ◽  
Ampai Thongtheer
Keyword(s):  
Hazard Rate ◽  
Variance Function ◽  
Power Variance Function ◽  
Power Variance

scholarly journals Frailty models based on Lévy processes

2003 ◽  
Vol 35 (2) ◽  
pp. 532-550 ◽  
Author(s):  
Håkon K. Gjessing ◽  
Odd O. Aalen ◽  
Nils Lid Hjort
Keyword(s):  
Lévy Process ◽  
Levy Process ◽  
Frailty Models ◽  
Hazard Rates ◽  
Survival Functions ◽  
Power Variance Function ◽  
Fixed Quantity ◽  
Power Variance ◽  
The Individual ◽  
Over Time

Generalizing the standard frailty models of survival analysis, we propose to model frailty as a weighted Lévy process. Hence, the frailty of an individual is not a fixed quantity, but develops over time. Formulae for the population hazard and survival functions are derived. The power variance function Lévy process is a prominent example. In many cases, notably for compound Poisson processes, quasi-stationary distributions of survivors may arise. Quasi-stationarity implies limiting population hazard rates that are constant, in spite of the continual increase of the individual hazards. A brief discussion is given of the biological relevance of this finding.

610 Self-Reported Sleep and Circadian Characteristics Predict Future Substance Use: A Longitudinal Analysis from the NCANDA Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A240-A240
Author(s):  
Brant Hasler ◽  
Jessica Graves ◽  
Meredith Wallace ◽  
Stephanie Claudatos ◽  
Fiona Baker ◽  
...  
Keyword(s):  
Risk Factors ◽  
Substance Use ◽  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Parental Education ◽  
Negative Binomial ◽  
Longitudinal Design ◽  
Random Effect ◽  
Cannabis Use ◽  
Increased Risk

Abstract Introduction Growing evidence indicates that sleep characteristics predict later substance use and related problems during adolescence and young adulthood. However, most prior studies have assessed a limited range of sleep characteristics, studied only a narrow age span, and included relatively few follow-up assessments. Here, we used multiple years of data from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study, which spans the adolescent period with an accelerated longitudinal design, to examine whether multiple sleep characteristics in any year predict substance use the following year. Methods The sample included 831 participants (423 females; age 12–21 years at baseline) from NCANDA. Sleep variables included the previous year’s circadian preference, sleep quality, daytime sleepiness, timing of midsleep (weekday and weekend), and sleep duration (weekday and weekend). Each sleep variable’s association with the subsequent year’s substance use (cannabis use or alcohol binge severity) across years 1–5 was tested separately using generalized linear mixed models (zero-inflated Negative Binomial for cannabis; ordinal for binge severity) with age, sex, race, visit, parental education, previous year’s substance use (yes/no) as covariates and subject as a random effect. Results With regard to cannabis use, greater eveningness and shorter weekday sleep duration predicted an increased risk for additional days of cannabis use the following year, while greater eveningness and later weekend midsleep predicted a greater likelihood of any cannabis use the following year. With regard to alcohol binge severity, greater eveningness, greater daytime sleepiness, and shorter sleep duration (weekday and weekend) all predicted an increased risk for more severe alcohol bingeing the following year. Post-hoc stratified analyses indicated that some of these associations may differ between high school-age and college-age participants. Conclusion Our findings extend prior work, indicating that eveningness and later sleep timing, as well as shorter sleep duration, especially on weekdays, are risk factors for future cannabis use and alcohol misuse. These results underscore a need for greater attention to sleep characteristics as potential risk factors for substance use in adolescents and young adults and may inform future areas of intervention. Support (if any) Grants from NIH: R01AA025626 (Hasler) and U01AA021690 (Clark) and UO1 AA021696 (Baker & Colrain)

scholarly journals A Vulnerability Assessment for a Future HIV Outbreak Associated With Injection Drug Use in Illinois, 2017–2018

2021 ◽  
Vol 6 ◽  
Author(s):  
Cara Jane Bergo ◽  
Jennifer R. Epstein ◽  
Stacey Hoferka ◽  
Marynia Aniela Kolak ◽  
Mai T. Pho
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Drug Use ◽  
Injection Drug Use ◽  
Negative Binomial ◽  
Cook County ◽  
Injection Drug ◽  
County Level ◽  
Opioid Use ◽  
Zip Code

The current opioid crisis and the increase in injection drug use (IDU) have led to outbreaks of HIV in communities across the country. These outbreaks have prompted country and statewide examination into identifying factors to determine areas at risk of a future HIV outbreak. Based on methodology used in a prior nationwide county-level analysis by the US Centers for Disease Control and Prevention (CDC), we examined Illinois at the ZIP code level (n = 1,383). Combined acute and chronic hepatitis C virus (HCV) infection among persons <40 years of age was used as an outcome proxy measure for IDU. Local and statewide data sources were used to identify variables that are potentially predictive of high risk for HIV/HCV transmission that fell within three main groups: health outcomes, access/resources, and the social/economic/physical environment. A multivariable negative binomial regression was performed with population as an offset. The vulnerability score for each ZIP code was created using the final regression model that consisted of 11 factors, six risk factors, and five protective factors. ZIP codes identified with the highest vulnerability ranking (top 10%) were distributed across the state yet focused in the rural southern region. The most populous county, Cook County, had only one vulnerable ZIP code. This analysis reveals more areas vulnerable to future outbreaks compared to past national analyses and provides more precise indications of vulnerability at the ZIP code level. The ability to assess the risk at sub-county level allows local jurisdictions to more finely tune surveillance and preventive measures and target activities in these high-risk areas. The final model contained a mix of protective and risk factors revealing a heightened level of complexity underlying the relationship between characteristics that impact HCV risk. Following this analysis, Illinois prioritized recommendations to include increasing access to harm reduction services, specifically sterile syringe services, naloxone access, infectious disease screening and increased linkage to care for HCV and opioid use disorder.

scholarly journals Factors associated with COVID-19 infections and mortality in Africa: a cross-sectional study using publicly available data

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e042750
Author(s):  
Charles Okeahalam ◽  
Victor Williams ◽  
Kennedy Otwombe
Keyword(s):  
Risk Factors ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cross Sectional Study ◽  
Sub Saharan Africa ◽  
Sectional Study ◽  
African Countries ◽  
Cross Sectional ◽  
Life Expectancy At Birth ◽  
Services Delivery

IntroductionThe current COVID-19 pandemic is a global threat. This elicits questions on the level of preparedness and capacity of health systems to respond to emergencies relative to other parts of the world.MethodsThis cross-sectional study uses publicly available core health data for 53 African countries to determine risk factors for cumulative COVID-19 deaths and cases per million in all countries in the continent. Descriptive statistics were determined for the indicators, and a negative binomial regression was used for modelling the risk factors.ResultsIn sub-Saharan Africa, an increase in the number of nursing and midwifery personnel decreased the risk of COVID-19 deaths (p=0.0178), while a unit increase in universal healthcare (UHC) index of service coverage and prevalence of insufficient physical activity among adults increased the risk of COVID-19 deaths (p=0.0432 and p=0.0127). An increase in the proportion of infants initiating breast feeding reduced the number of cases per million (p<0.0001), while an increase in higher healthy life expectancy at birth increased the number of cases per million (p=0.0340).ConclusionDespite its limited resources, Africa’s preparedness and response to the COVID-19 pandemic can be improved by identifying and addressing specific gaps in the funding of health services delivery. These gaps impact negatively on service delivery in Africa, which requires more nursing personnel and increased UHC coverage to mitigate the effects of COVID-19.

Estimating Rates of Recurrent Peritonitis for Patients on CAPO

1985 ◽  
Vol 5 (1) ◽  
pp. 59-65 ◽  
Author(s):  
Edward F. Vonesh
Keyword(s):  
Risk Factors ◽  
Medical Devices ◽  
Negative Binomial ◽  
Survival Curve ◽  
Probability Model ◽  
Major Complication ◽  
Poisson Model ◽  
Negative Binomial Model ◽  
Binomial Model ◽  
The Mean

Recurrent peritonitis is a major complication of Continuous Ambulatory Peritoneal Dialysis (CAPD). As a therapy for patients with end stage renal disease, CAPD entails a continuous interaction between patient and various medical devices. The assumptions one makes regarding this interaction play an essential role when estimating the rate of recurrent peritonitis for a given patient population. Assuming that each patient has a constant rate of peritonitis, two models for evaluating the risk of recurrent peritonitis are considered. One model, the Poisson probability model, applies when the rate of peritonitis is the same from patient to patient. When this occurs, the frequency of peritoneal infections will be randomly distributed among patients (Corey, 1981). A second model, the negative binomial probability model, applies when the rate of peritonitis varies from one patient to another. In this event, the distribution of peritoneal infections will differ from patient to patient. The poisson model would be applicable when, for example, patients behave similarly with respect to their interactions with the medical devices and with potential risk factors. The negative binomial model, on the other hand, makes allowances for patient differences both in terms of their handling of routine exchanges and in their exposure to various risk factors. This paper provides methods for estimating the mean peritonitis rate under each model. In addition, “survival” curve estimates depicting the probability of remaining peritonitis free (i.e. “surviving”) over time are provided. It is shown, using data from a multi-center clinical trial, that the risk of peritonitis is best described in terms of survival curves rather than the mean peritonitis rate. For both models, the mean peritonitis rate was found to be 0.85 episodes per year. However, under the negative binomial model, the one-year survival rate, expressed as the percentage of patients remaining free of peritonitis, is 52% as compared with only 42% under the Poisson model. Moreover, the negative binomial model provided a significantly better fit to the observed frequency of peritonitis. These findings suggest that the negative binomial model provides a more realistic and accurate portrayal of the risk of peritonitis and that this risk is not nearly as high as would otherwise be indicated by a Poisson analysis.

Risikofaktoren für die Diagnoseverzögerung bei idiopathischer Lungenfibrose vermeiden

2019 ◽  
pp. III-IV
Author(s):  
Fotios Drakopanagiotakis ◽  
Andreas Günther
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Negative Binomial ◽  
Diagnostic Delay ◽  
Delayed Diagnosis ◽  
Final Diagnosis ◽  
Inhalation Therapy ◽  
Total Delay ◽  
Specific Patient ◽  
Male Sex

Background: Surveys and retrospective studies of patients with idiopathic pulmonary fibrosis (IPF) have shown a significant diagnostic delay. However, the causes and risk factors for this delay are not known. Methods: Dates at six time points before the IPF diagnosis (onset of symptoms, first contact to a general practitioner, first hospital contact, referral to an interstitial lung disease (ILD) centre, first visit at an ILD centre, and final diagnosis) were recorded in a multicentre cohort of 204 incident IPF patients. Based on these dates, the delay was divided into specific patient-related and healthcare-related delays. Demographic and clinical data were used to determine risk factors for a prolonged delay, using multivariate negative binomial regression analysis. Results: The median diagnostic delay was 2.1 years (IQR: 0.9-5.0), mainly attributable to the patients, general practitioners and community hospitals. Male sex was a risk factor for patient delay (IRR: 3.84, 95% CI: 1.17-11.36, p = 0.006) and old age was a risk factor for healthcare delay (IRR: 1.03, 95% CI: 1.01-1.06, p = 0.004). The total delay was prolonged in previous users of inhalation therapy (IRR: 1.99, 95% CI: 1.40-2.88, p < 0.0001) but not in patients with airway obstruction. Misdiagnosis of respiratory symptoms was reported by 41% of all patients. Conclusion: Despite increased awareness of IPF, the diagnostic delay is still 2.1 years. Male sex, older age and treatment attempts for alternative diagnoses are risk factors for a delayed diagnosis of IPF. Efforts to reduce the diagnostic delay should focus on these risk factors.

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