Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
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DOI：
10.1080/01621459.1999.10473862
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发表时间：
1999-12
影响因子：
3.7
通讯作者：
D. Scharfstein;A. Rotnitzky;J. Robins
中科院分区：
文献类型：
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作者：
D. Scharfstein;A. Rotnitzky;J. Robins
考虑一项研究，其设计要求研究对象从入组（时间t = 0）到时间t = t进行随访，此时要测量主要感兴趣的终点Y。本研究的设计还要求在区间[0，t)内一次或多次t对协变量向量V (t)进行测量。我们感兴趣的是，当一些受试者在随访时间t的共同固定结束之前的随机时间Q退出研究时，如何推断Y的边际平均μ0。本文的目的是展示如何在连续退出时间Q是半参数建模的情况下，对结果和其他测量变量的联合分布不加限制的情况下，推断μ0。特别地，我们考虑给定（V（T)， Y）的退出条件风险的两个模型，其中V(T)表示过程V T）在时间T， T∈[0，T]中的历史。在第一个模型中，我们假设λQ(t|V(t)， Y) exp(α0 Y)，其中α0是标量参数…
Abstract Consider a study whose design calls for the study subjects to be followed from enrollment (time t = 0) to time t = T, at which point a primary endpoint of interest Y is to be measured. The design of the study also calls for measurements on a vector V t) of covariates to be made at one or more times t during the interval [0, T). We are interested in making inferences about the marginal mean μ0 of Y when some subjects drop out of the study at random times Q prior to the common fixed end of follow-up time T. The purpose of this article is to show how to make inferences about μ0 when the continuous drop-out time Q is modeled semiparametrically and no restrictions are placed on the joint distribution of the outcome and other measured variables. In particular, we consider two models for the conditional hazard of drop-out given (V(T), Y), where V(t) denotes the history of the process V t) through time t, t ∈ [0, T). In the first model, we assume that λQ(t|V(T), Y) exp(α0 Y), where α0 is a scalar paramet...