Modeling Survival Data Using Frailty Models

Hanagal, David D.

doi:10.1201/b10510

Cited by 71 publications

(42 citation statements)

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“…Two important extensions in survival regression models that have received considerable attention recently are the inclusion of random effects and flexible modelling through semiparametric and nonparametric approach 21. Such analyses have an added advantage compared to ordinary GLM.…”

Section: Overview Of the Analysis Of Mortality Datamentioning

confidence: 99%

Childhood mortality in sub-Saharan Africa: cross-sectional insight into small-scale geographical inequalities from Census data

2012

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ObjectivesTo estimate and quantify childhood mortality, its spatial correlates and the impact of potential correlates using recent census data from three sub-Saharan African countries (Rwanda, Senegal and Uganda), where evidence is lacking.DesignCross-sectional.SettingNation-wide census samples from three African countries participating in the 2010 African Census round. All three countries have conducted recent censuses and have information on mortality of children under 5 years.Participants111 288 children under the age of 5 years in three countries.Primary and secondary outcome measuresUnder-five mortality was assessed alongside potential correlates including geographical location (where children live), and environmental, bio-demographic and socioeconomic variables.ResultsMultivariate analysis indicates that in all three countries the overall risk of child death in the first 5 years of life has decreased in recent years (Rwanda: HR=0.04, 95% CI 0.02 to 0.09; Senegal: HR=0.02 (95% CI 0.02 to 0.05); Uganda: HR=0.011 (95% CI 0.006 to 0.018). In Rwanda, lower deaths were associated with living in urban areas (0.79, 0.73, 0.83), children with living mother (HR=0.16, 95% CI 0.15 to 0.17) or living father (HR=0.38, 95% CI 0.36 to 0.39). Higher death was associated with male children (HR=1.06, 95% CI 1.02 to 1.08) and Christian children (HR=1.14, 95% CI 1.05 to 1.27). Children less than 1 year were associated with higher risk of death compared to older children in the three countries. Also, there were significant spatial variations showing inequalities in children mortality by geographic location. In Uganda, for example, areas of high risk are in the south-west and north-west and Kampala district showed a significantly reduced risk.ConclusionsWe provide clear evidence of considerable geographical variation of under-five mortality which is unexplained by factors considered in the data. The resulting under-five mortality maps can be used as a practical tool for monitoring progress within countries for the Millennium Development Goal 4 to reduce under-five mortality in half by 2015.

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Section: Overview Of the Analysis Of Mortality Datamentioning

confidence: 99%

Childhood mortality in sub-Saharan Africa: cross-sectional insight into small-scale geographical inequalities from Census data

2012

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“…Frailty models (Duchateau and Janssen 2008;Hougaard 2000;Wienke 2010;Hanagal 2011) are extensions of the Cox proportional hazards model (Cox 1972) which is the most popular model in survival analysis. In many clinical applications, the study population needs to be considered as a heterogeneous sample or as a cluster of homogeneous groups of individuals such as families or geographical areas.…”

Section: Introductionmentioning

confidence: 99%

frailtypack: AnRPackage for the Analysis of Correlated Survival Data with Frailty Models Using Penalized Likelihood Estimation or Parametrical Estimation

Rondeau¹,

Mazroui²,

González³

2012

J. Stat. Soft.

203

207

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Frailty models are very useful for analysing correlated survival data, when observations are clustered into groups or for recurrent events. The aim of this article is to present the new version of an R package called frailtypack. This package allows to fit Cox models and four types of frailty models (shared, nested, joint, additive) that could be useful for several issues within biomedical research. It is well adapted to the analysis of recurrent events such as cancer relapses and/or terminal events (death or lost to follow-up). The approach uses maximum penalized likelihood estimation. Right-censored or left-truncated data are considered. It also allows stratification and time-dependent covariates during analysis.

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“…It should be noted that in the case of having unobserved (difficult to observe or measure) upstream quality variables in multistage processes, frailty models can be effectively applied. 21 The frailty is a nonnegative random variable which modifies the baseline survival and hazard functions. Thus, in order to reach the unconditional functions, the Laplace transform may be used.…”

Section: Process Description and Assumptionsmentioning

confidence: 99%

Controlling autocorrelated data in multistage manufacturing processes with an application to textile industry

Keshavarz

Asadzadeh

Niaki

2019

Quality & Reliability Eng

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In recent years, much attention has been given to monitoring multistage processes in order to effectively improve the product reliability. To this end, the output of the process is investigated under special circumstances, and the values corresponding to reliability‐related quality characteristic are measured. However, analyzing reliability data is quite complicated because of their unique features such as being censored and obeying nonnormal distributions. A more sophisticated picture arises when the observations of the process are autocorrelated in some cases, which makes the application of previous control procedures futile. In this paper, the accelerated failure time (AFT) regression models have been modified in order to account for autocorrelated data. Then, a cumulative sum (CUSUM) control chart and an exponentially weighted moving average (EWMA) control chart based on conditional expected values have been proposed to monitor the quality variable with Weibull distribution while taking the effective covariates into consideration. Extensive simulation studies reveal that the CUSUM control chart outperforms its counterpart in detecting out‐of‐control conditions. Finally, a real case study in a textile industry has been provided to investigate the application of the CUSUM control scheme.

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Modeling Survival Data Using Frailty Models

Cited by 71 publications

References 0 publications

Childhood mortality in sub-Saharan Africa: cross-sectional insight into small-scale geographical inequalities from Census data

Childhood mortality in sub-Saharan Africa: cross-sectional insight into small-scale geographical inequalities from Census data

frailtypack: AnRPackage for the Analysis of Correlated Survival Data with Frailty Models Using Penalized Likelihood Estimation or Parametrical Estimation

Controlling autocorrelated data in multistage manufacturing processes with an application to textile industry

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