Analysis of matched case-control data with multiple ordered disease states: Possible choices and comparisons
Analysis of matched case-control data with multiple ordered disease states: Possible choices and comparisons
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DOI：
10.1002/sim.2790
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发表时间：
2007-07-30
影响因子：
2
通讯作者：
Sinha, Samiran
中科院分区：
文献类型：
--
作者：
Mukherjee, Bhramar;Liu, Ivy;Sinha, Samiran
在一项个体匹配的病例对照研究中，通过条件Logistic回归（logistic regression）确定潜在危险因素的影响。已通过多分类的临床研究将临床研究扩展到具有多种疾病或参考类别的情况，并且已证明其比对每个亚组进行单独的临床研究更有效。在本文中，我们考虑匹配的病例对照研究，其中有一个对照组，但有多种疾病状态之间的自然排序。当病例可以根据疾病的严重性或进展进一步分类时，例如根据癌症的不同阶段，可以观察到这种情况。在这种情况下，我们探讨了几种流行的有序分类数据模型。我们首先采用累积logit或等效的比例优势模型来解释数据的有序性。该模型与分层二分和多分logistic回归模型的重要区别在于，该模型中的特定层干扰参数不能通过条件似然方法消除。我们讨论了一个曼特尔-Haenszel的方法来分析这些数据。我们指出了标准的可能性为基础的方法与累积logit模型应用于病例对照数据时可能存在的困难。然后，我们考虑一个替代的条件相邻类别logit模型。我们说明了方法，通过分析数据匹配的病例对照研究，低出生体重的新生儿，婴儿被分类为低出生体重和极低出生体重和正常出生体重的儿童作为对照。一个模拟研究比较了不同的有序方法与忽略有序疾病状态的子分类的方法。版权所有（C）2007约翰威利父子有限公司
In an individually matched case-control study, effects of potential risk factors are ascertained through conditional logistic regression (CLR). Extension of CLR to situations with multiple disease or reference categories has been made through polychotomous CLR and is shown to be more efficient than carrying out separate CLRs for each subgroup. In this paper, we consider matched case-control studies where there is one control group, but there are multiple disease states with a natural ordering among themselves. This scenario can be observed when the cases can be further classified in terms of the seriousness or progression of the disease, for example, according to different stages of cancer. We explore several popular models for ordered categorical data in this context. We first adopt a cumulative logit or equivalently, a proportional-odds model to account for the ordinal nature of the data. The important distinction of this model from a stratified dichotomous and polychotomous logistic regression model is that the stratum-specific nuisance parameters cannot be eliminated in this model via the conditional-likelihood approach. We discuss a Mantel-Haenszel approach for analysing such data. We point out possible difficulties with standard likelihood-based approaches with the cumulative logit model when applied to case-control data. We then consider an alternative conditional adjacent-category logit model. We illustrate the methods by analysing data from a matched case-control study on low birthweight in newborns where infants are classified according to low and very low birthweight and a child with normal birthweight serves as a control. A simulation study compares the different ordinal methods with methods ignoring sub-classification of the ordered disease states. Copyright (C) 2007 John Wiley & Sons, Ltd.