A multilevel excess hazard model to estimate net survival on hierarchical data allowing for non‐linear and non‐proportional effects of covariates

The excess hazard regression model is an approach developed for the analysis of cancer registry data to estimate net survival, that is, the survival of cancer patients that would be observed if cancer was the only cause of death. Cancer registry data typically possess a hierarchical structure: individuals from the same geographical unit share common characteristics such as proximity to a large hospital that may influence access to and quality of health care, so that their survival times might be correlated. As a consequence, correct statistical inference regarding the estimation of net survival and the effect of covariates should take this hierarchical structure into account. It becomes particularly important as many studies in cancer epidemiology aim at studying the effect on the excess mortality hazard of variables, such as deprivation indexes, often available only at the ecological level rather than at the individual level. We developed here an approach to fit a flexible excess hazard model including a random effect to describe the unobserved heterogeneity existing between different clusters of individuals, and with the possibility to estimate non‐linear and time‐dependent effects of covariates. We demonstrated the overall good performance of the proposed approach in a simulation study that assessed the impact on parameter estimates of the number of clusters, their size and their level of unbalance. We then used this multilevel model to describe the effect of a deprivation index defined at the geographical level on the excess mortality hazard of patients diagnosed with cancer of the oral cavity. Copyright © 2016 John Wiley & Sons, Ltd.     

A semi‐Markov model for stroke with piecewise‐constant hazards in the presence of left,right and interval censoring

This paper presents a parametric method of fitting semi‐Markov models with piecewise‐constant hazards in the presence of left, right and interval censoring. We investigate transition intensities in a three‐state illness–death model with no recovery. We relax the Markov assumption by adjusting the intensity for the transition from state 2 (illness) to state 3 (death) for the time spent in state 2 through a time‐varying covariate. This involves the exact time of the transition from state 1 (healthy) to state 2. When the data are subject to left or interval censoring, this time is unknown. In the estimation of the likelihood, we take into account interval censoring by integrating out all possible times for the transition from state 1 to state 2. For left censoring, we use an Expectation–Maximisation inspired algorithm. A simulation study reflects the performance of the method. The proposed combination of statistical procedures provides great flexibility. We illustrate the method in an application by using data on stroke onset for the older population from the UK Medical Research Council Cognitive Function and Ageing Study. Copyright © 2012 John Wiley & Sons, Ltd.     

中西部农村慢性病老年人住院服务利用及其影响因素分析——基于多水平模型的宁夏四县为例

目的 了解中西部农村患有慢性病的老年人住院服务利用状况,并探讨其影响因素,为政府进一步落实卫生政策和提高农村慢性病老年人卫生服务质量提供参考依据。方法 采用多阶段整群随机抽样方法,对宁夏4个县的2 450名慢性病老年人进行调查,使用χ²检验分析不同慢性病老年人住院率的差异,运用多水平模型分析慢性病老年人住院率的影响因素。结果 中西部农村地区慢性病老年人住院为37.8%(926/2 450)。多水平模型结果显示,女性较男性有0.702(0.582～0.846)倍的风险住院,65～69岁的人较60～64岁的人有0.748(0.583～0.960)倍的风险住院,≥70岁的人较60～64岁的人有0.616(0.482～0.788)倍的风险住院,非务农的人较务农的人有0.782(0.642～0.953)倍的风险住院,中高收入组的人较低收入组的人有0.603(0.448～0.812)倍的风险住院,高收入组的人较低收入组的人有0.562(0.415～0.761)倍的风险住院。结论 宁夏农村慢性病老年人住院服务利用较高,但受到性别、年龄、职业和家庭年人均收入等多方面因素的影响,应...