scholarly journals Forward Mortality Rates in Discrete Time I: Calibration and Securities Pricing

2015 ◽  
Author(s):  
Andrew Hunt ◽  
David P. Blake
Keyword(s):  
Discrete Time ◽  
Mortality Rates ◽  
Securities Pricing

Inequalities and Impact of Socioeconomic-Cultural Factors in Suicide Rates Across Italy

Crisis ◽  
2011 ◽  
Vol 32 (4) ◽  
pp. 178-185 ◽  
Author(s):  
Maurizio Pompili ◽  
Marco Innamorati ◽  
Monica Vichi ◽  
Maria Masocco ◽  
Nicola Vanacore ◽  
...  
Keyword(s):  
Suicide Rate ◽  
Age Groups ◽  
Premature Death ◽  
Mortality Rates ◽  
Mortality Data ◽  
Italian Population ◽  
Comprehensive Overview ◽  
Suicide Rates ◽  
Younger Age ◽  
Age And Sex

Background: Suicide is a major cause of premature death in Italy and occurs at different rates in the various regions. Aims: The aim of the present study was to provide a comprehensive overview of suicide in the Italian population aged 15 years and older for the years 1980–2006. Methods: Mortality data were extracted from the Italian Mortality Database. Results: Mortality rates for suicide in Italy reached a peak in 1985 and declined thereafter. The different patterns observed by age and sex indicated that the decrease in the suicide rate in Italy was initially the result of declining rates in those aged 45+ while, from 1997 on, the decrease was attributable principally to a reduction in suicide rates among the younger age groups. It was found that socioeconomic factors underlined major differences in the suicide rate across regions. Conclusions: The present study confirmed that suicide is a multifaceted phenomenon that may be determined by an array of factors. Suicide prevention should, therefore, be targeted to identifiable high-risk sociocultural groups in each country.

Global Suicide Mortality Rates – A Light at the End of the Tunnel?

Crisis ◽  
2012 ◽  
Vol 33 (5) ◽  
pp. 249-253 ◽  
Author(s):  
José Manoel Bertolote ◽  
Diego De Leo
Keyword(s):  
Mortality Rates ◽  
Suicide Mortality

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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