论文标题:危险度评估中的多阶段混合效应模型
Multi-stage Mixed Effects Models with Applications to Risk Assessment
论文作者
论文导师 陈峰,论文学位 硕士,论文专业 流行病与卫生统计学
论文单位 南京医科大学,点击次数 133,论文页数 85页File Size3859K
2007-05-01论文网 http://www.lw23.com/lunwen_122299577/
meta regression model;; mixed effects model;; multi-stage model;; dose-response relationship;; risk assessment;; bootstrap estimation;; lead;; reproductive toxicity
剂量-反应关系评定一直是危险度评估的核心部分，目前随着对致病机制的进一步认识，可以将传统的多阶段模型与疾病的发展进程结合起来，建立基于生物学背景的多阶段剂量-反应关系模型，提高危险度评估的准确度和精密度。 由于几乎没有单独的研究能够获得建立完整多阶段模型所需的数据，本研究将meta分析思路应用到危险度评估过程的剂量-反应关系研究中，建立剂量-反应关系的meta回归模型，为基于机制的危险度评估提供依据。并对建模策略进行了初步探讨。 本研究分别介绍了线性，广义线性和非线性meta回归模型的模型结构及参数估计方法。按照meta分析系统评价原则，收集26篇关于铅致雄性生殖毒性的英文文献，建立数据库。并以此数据为例，构建基于生物学背景的多阶段混合效应模型，思路如下：①建立年龄、体重与铅暴露前后睾丸重量变化的线性meta回归模型，与单纯随机效应模型相比，研究间方差明显减少，说明在meta分析中考虑协变量是必要的。②分别建立血-脑屏障及血-睾丸屏障通路下从暴露到疾病的多阶段meta回归模型，其模型结构包括logistic模型，指数模型，线性模型和双曲线模型，这些模型的生物学意义明确。③初步建立暴露剂量、暴露时间与血铅关系的交互作用模型，随机效应结果显示自变量间不存在交互作用。 由于缺乏原始数据，非线性meta回归模型参数的估计难以实现，本文采用参数bootstrap方法进行参数估计与假设检验，在迭代次数达500轮时结果较稳定。 根据研究体会，对多阶段混合效应模型的建模策略提出以下几点粗浅建议： 1．首先根据散点图趋势建立固定效应模型，并以此作为初始模型，指定该模型回归系数的估计值为下阶段混合效应模型的迭代初始值。 2．在危险度评估中建立的各阶段剂量-反应关系模型应符合研究指标间的生物学意义。 3．基于各研究中因变量与自变量间的曲线形状可能存在较大差异，建模时可依次考虑固定效应模型、随机效应模型和带协变量的随机系数模型，并观察研究水平的方差与残差方差的变化，以及与协变量的关系，从而选择最优模型。 4．在资料允许的情况下，meta回归中可以考虑协变量间的交互作用。这对于机制的深入解释是有帮助的。 5．在统计软件尚不能直接实现对非线性meta回归模型的WIGLS、REML估计时可根据研究指标的经验分布，采用参数bootstrap方法进行参数估计及假设检验。
The description and evaluation of dose-response relationship is a critical component of riskassessment. According to the rapid development of molecular biology, the mechanism ofcausing disease has been more and more clearly. So through combining the traditionalmulti-stage models with disease mechanism, biologically based dose-response models could beestablished in order to improve the accuracy of risk assessment. Since it"s hardly to obtain the complete data for multi-stage models from a single study,this paper applied the thought of meta analysis to the evaluation of dose-response relationship,and focused on the structure of meta-regression models as well as strategy of modelconstructing. In this search, the model structures and parameter estimation methods of three types ofmeta regression model, linear, general linear and nonlinear, were introduced. According to theguidelines of systematic review, twenty-six papers about male reproductive toxicity of leadwere carefully collected to establish a database. With these data, biologically based multi-stagemixed effects models were constructed. The main ideas were as follows: First, linear metaregression model was established in which the mean difference of testes weight was defined asdependent variable, age and body weight was defined as two covariates. Comparing with thesingle random effect model, the estimate value of between-study variance was significantlyreduced. Thus, it indicated considering covariates in meta analysis were necessary. Second, several stages of mixed effects model about corresponding variables were constructed under thepath of blood-brain barrier and blood-testis barrier. The model structures included were logistic,exponential, linear as well as hyperbola, and they were all reasonable for the explanation ofbiological relationship. Third, an interactive effects model of logistic form was trying toestablish among exposure variables and blood lead, but according to the result of randomeffects model, there was no significant interactive effect between exposure dose and exposuretime. Due to lack of original data, it"s hard to carry out direct parameter estimation methods ofnonlinear meta regression model at present. Parametric bootstrap technique was used forparameter estimation and hypotheses test in this paper, and the results would be acceptable after500 iterations. Based on the conclusions above, the following strategies of constructing multi-stage mixedeffects models were suggested by the author: 1. A fixed effects model should be estimated first according to the scatter plot, and thecoefficients of the model would be identified as the initial values for next iterations whenrandom or mixed effects model were further constructed. 2. Dose-response models in risk assessment might be not only mathematically based, theyshould be endowed with biological meanings also. 3. Since in some instance, the variance of response variable might be changeable along withthe alteration of explanatory variable, in the procedure of model constructing, a fixedeffects model should be considered firstly, then the random effects model or randomcoefficient model when necessary. The best fit model should be chosen by comparing thealterations of between-study variance and residual variance, as well as their relationshipswith covariates. 4. With sufficient collected data, it might be possible to study the interactive effects amongcovariates. This would be quite useful for the explanation of disease mechanism. 5. As at present, there"s no appropriate software to carry out the WIGLS or REML estimate directly for nonlinear meta regression model, the parametric bootstrap would be a suitableway for parameter estimation and hypotheses test if the population distribution of observedvariable was already known.
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