Multivariate parametric random effect regression models for fecundability studies

Delay until conception is generally described by a mixture of geometric distributions. Weinberg and Gladen (1986, Biometrics 42, 547-560) proposed a regression generalization of the beta-geometric mixture model where covariates effects were expressed in terms of contrasts of marginal hazards. Scheike and Jensen (1997, Biometrics 53, 318-329) developed a frailty model for discrete event times data based on discrete-time analogues of Hougaard's results (1984, Biometrika 71, 75-83). This paper is on a generalization to a three-parameter family distribution and an extension to multivariate cases. The model allows the introduction of explanatory variables, including time-dependent variables at the subject-specific level, together with a choice from a flexible family of random effect distributions. This makes it possible, in the context of medically assisted conception, to include data sources with multiple pregnancies (or attempts at pregnancy) per couple.     

Penalized partial likelihood for frailties and smoothing splines in time to first insemination models for dairy cows

In many epidemiological studies time to event data are clustered and the physiological relationship between (time-dependent) covariates and the log hazard is often not linear as assumed in the Cox model. Introducing frailties in the Cox model can account for the clustering of the data and smoothing splines can be used to describe nonlinear relations. These two extensions of the Cox model are introduced jointly and it is shown how penalized partial likelihood techniques can be used to fit the extended model. We demonstrate the need for such a model to study the relation between the physiological covariates milk ureum and protein concentration and the log hazard of first insemination in dairy cows, with the farms as clusters.     

A Bayesian approach for the analysis of panel-count data with dependent termination

We consider modeling and Bayesian analysis for panel-count data when the termination time for each subject may depend on its history of the recurrent events. We propose a fully specified semiparametric model for the joint distribution of the recurrent events and the termination time. For this model, we provide a natural motivation, derive several novel properties, and develop a Bayesian analysis based on a Markov chain Monte Carlo algorithm. Comparisons are made to other existing models and methods for panel-count data. We demonstrate the usefulness of our new models and methodologies through the reanalysis of a data set from a clinical trial.     

Semiparametric estimation of marginal hazard function from case-control family studies

Estimating marginal hazard function from the correlated failure time data arising from case-control family studies is complicated by noncohort study design and risk heterogeneity due to unmeasured, shared risk factors among the family members. Accounting for both factors in this article, we propose a two-stage estimation procedure. At the first stage, we estimate the dependence parameter in the distribution for the risk heterogeneity without obtaining the marginal distribution first or simultaneously. Assuming that the dependence parameter is known, at the second stage we estimate the marginal hazard function by iterating between estimation of the risk heterogeneity (frailty) for each family and maximization of the partial likelihood function with an offset to account for the risk heterogeneity. We also propose an iterative procedure to improve the efficiency of the dependence parameter estimate. The simulation study shows that both methods perform well under finite sample sizes. We illustrate the method with a case-control family study of early onset breast cancer.     

Estimation of effective reproduction numbers for infectious diseases using serological survey data

The effective reproduction number of an infection, denoted Re, may be used to monitor the impact of a vaccination programme. If Re is maintained below 1, then sustained endemic transmission of the infection cannot occur. In this paper we discuss methods for estimating Re from serological survey data, allowing for age and individual heterogeneity. We describe semi-parametric and parametric models, and obtain an upper bound on Re when vaccine coverage and efficacy are not known. The methods are illustrated using data on mumps and rubella in England and Wales.     

A random effects model for multistate survival analysis with application to bone marrow transplants

We present an extension of the non-homogeneous Markov model for a bone marrow transplant recovery process which allows for possible associations between the transition intensities. The associations between intensities are modeled by a correlated gamma frailty model. Based on a parametric model for the conditional transition intensities, we obtain estimates of the model parameters. We use these estimates to make predictions of patient’s eventual prognosis given the current medical history of the patient. Estimates of the uncertainty in our predictions are obtained by a modified bootstrap technique.     

A version of the EM algorithm for proportional hazard model with random effects

Proportional hazard models with multivariate random effects (frailties) acting multiplicatively on the baseline hazard have recently become a topic of an intensive research. One of the main practical problems related to the models is the estimation of parameters. To this aim, several approaches based on the EM algorithm have been proposed. The major difference between these approaches is the method of the computation of conditional expectations required at the E-step. In this paper an alternative implementation of the EM algorithm is proposed, in which the expected values are computed with the use of the Laplace approximation. The method is computationally less demanding than the approaches developed previously. Its performance is assessed based on a simulation study and compared to a non-EM based estimation approach proposed by Ripatti and Palmgren (2000).     

帕金森病患者营养不良的影响因素分析及与衰弱、认知功能和跌倒风险的关系研究

摘要 目的：分析帕金森病（PD）患者营养不良的影响因素,并观察营养不良对衰弱、认知功能和跌倒风险的影响。方法：选取江苏省人民医院2019年3月至2022年3月期间收治的100例PD患者。采用简易营养评价量表（MNA）对研究对象进行营养状态评估,将100例患者分为营养不良组（n=52）和无营养不良组（n=48）。获取所有患者的一般资料,经Logistic回归分析PD患者营养不良的影响因素。同时对比无营养不良组、营养不良组的衰弱、认知功能和跌倒风险情况。结果：PD患者营养不良与年龄、体质量指数、Hoehn-Yahr分级、居住地、婚姻状况、血红蛋白、白蛋白、前白蛋白、每日左旋多巴等效剂量（LEDDs）、睡眠状况、焦虑状况、抑郁状况、味觉障碍、吞咽障碍、食欲下降有关（P<0.05）。Logistic回归分析,结果显示：年龄偏大、Hoehn-Yahr分级为III~V级、睡眠状况偏差、焦虑/抑郁状况严重、味觉障碍、吞咽障碍、食欲下降、LEDDs偏高是PD患者出现营养不良的危险因素（P<0.05）。营养不良组的衰弱发生率高于无营养不良组,衰弱前期、无衰弱发生率低于无营养不良组（P<0.05）。营养不良组的简易精神状态检查量表（MMSE）、蒙特利尔认知评估量表（MOCA）评分低于无营养不良组（P<0.05）。营养不良组的起立-行走计时测验（TUGT）时间、站起测验（CRT）时间长于无营养不良组,走直线步态测验（TGT）正确步数少于无营养不良组（P<0.05）。结论：PD患者营养不良发生风险较高,且受到年龄、睡眠状况、焦虑状况、抑郁状况、Hoehn-Yahr分级、味觉障碍、吞咽障碍、食欲下降、LEDDs的影响,且营养不良可加重衰弱、降低认知功能和增加跌倒风险。     

老年冠心病患者经皮冠状动脉介入治疗术后衰弱的影响因素及其对认知功能和预后的影响

摘要 目的：探讨老年冠心病患者经皮冠状动脉介入治疗（PCI）术后衰弱的影响因素及其对认知功能和预后的影响。方法：选取2019年3月~2021年3月期间本院收治的行PCI的老年冠心病患者134例。查阅相关问卷制定调查表,收集、整理患者资料,统计术后衰弱发生情况,衰弱的影响因素采用多因素Logistic回归分析。根据患者是否合并衰弱纳为衰弱组和非衰弱组,对比两组认知功能和预后情况。结果：接受PCI治疗的134例老年冠心病患者中,术后合并衰弱者92例,占比68.66%。未合并衰弱者42例,占比31.34%。合并衰弱的患者纳为衰弱组（n=92）,未合并衰弱的纳为非衰弱组（n=42）。单因素分析显示,老年冠心病患者PCI术后衰弱与婚姻状况、年龄、放入支架数量、家庭人均月收入、饮酒史、文化程度、吸烟史、心功能分级、合并症种类数、体质量指数、营养状况、近1年跌倒史、焦虑情况、抑郁情况有关（P<0.05）。多因素Logistic回归分析结果显示：有饮酒史、年龄偏大、合并症种类数≥3种、心功能分级III级、有焦虑情况、文化程度小学及其以下、有抑郁情况、有近1年跌倒史、家庭人均月收入≤3000元、营养状况不良是老年冠心病患者PCI术后衰弱的危险因素（P<0.05）。衰弱组的非常规就诊发生率、全因死亡率、主要不良心血管事件发生率均高于非衰弱组（P<0.05）。衰弱组的简易智力状态检查量表（MMSE）评分低于非衰弱组（P<0.05）。结论：老年冠心病患者PCI术后衰弱的发生率较高,且受到年龄、家庭人均月收入、文化程度、饮酒史、合并症种类数、心功能分级、焦虑情况、抑郁情况、有近1年跌倒史、营养状况等因素的影响。衰弱会导致患者的认知功能下降以及不良预后。