hiv(Icens)
hiv()所属R语言包：Icens
Intervals for infection time and disease onset for 257
感染的时间和发病257区间
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The hiv data frame has 257 rows and 4 columns.
hiv数据框有257行和4列。
格式----------Format----------
This data frame contains the following columns:
这个数据框包含下列资料：
The left end point of the infection time interval.
感染的时间间隔的左端点。
The right end point of the infection time interval.
感染的时间间隔的终点。
The left end point of the disease onset interval.
发病间隔的左端点。
The right end point of the disease onset interval.
发病间隔的右端点。
Age Coded as 1 if the estimated age at infection was less than 20
为1编码，如果在感染的估计年龄不到20岁
Trt Treatment, Light or Heavy
TRT治疗，轻或重
Details
详情----------Details----------
The setting is as follows. Individuals were infected with the HIV virus at some unknown time they subsequently develop AIDS at a second unknown time. The data consist of two intervals, (y_L, y_R) and (z_L,z_R), such that the infection time was in the first interval and the time of disease onset was in the second interval. A quantity of interest is the incubation time of the disease which is T=Z-Y. The authors argue persuasively that this should be considered as bivariate interval censored data. They note that simply forming the differences (z_L-y_R, z_R-y_L) and analysing the resultant data assumes an incorrect likelihood. DeGruttola and Lagakos transform the problem slightly to study the joint distribution of Y and T=Z-Y. This is equivalent to estimating the joint distribution of Z and Y then transforming. The data, as reported, have been discretized into six month intervals.
设置如下。个人被感染了艾滋病毒，他们随后在第二个未知的时间发展的艾滋病在一些不知名的时间。数据包括两个区间，(y_L, y_R)和(z_L,z_R)，这种感染的时间是在第一区间和发病时间是在第二区间。一个利益的数量，这是T=Z-Y疾病的潜伏期时间。作者认为有说服力，这应该被视为二元区间数据。他们指出，只要形成差异(z_L-y_R, z_R-y_L)和由此产生的数据分析，假定一个不正确的可能性。 DeGruttola和Lagakos改造问题略有研究Y和T=Z-Y的联合分布。这是相当于估计Z和Y然后转化的联合分布。的数据，据报道，已被离散成6个月的时间间隔。
We use the data as reported in Table 1 of DeGruttola and Lagakos, 1989. The patients were 257 persons with Type A or B hemophilia treated at two hospitals in France. They were then examined intermittently (as they came in for treatment?) and their HIV and AIDS status was determined. Kim, De Gruttola and Lagakos report some covariate information and their paper is concerned with the modeling of that information. In this paper we concentrate only on the event times and ignore the covariate information; that topic being worthy of separate investigation.
我们使用的数据，如表1，1989 DeGruttola和Lagakos报道。患者257人在法国医院治疗A型或B型血友病。然后，他们被检查间歇性（因为他们在接受治疗了吗？）和他们的艾滋病毒和艾滋病状况确定。金德Gruttola和Lagakos的报告一些协信息和有关他们的信息建模。在本文中，我们只集中对事件的时间和忽略协信息;主题是值得的独立调查。
源----------Source----------
DeGruttola, V. and Lagakos, S.W., 1989, Analysis of doubly-censored survival data, with application to AIDS, Biometrics.
DeGruttola，五和Lagakos，申银万国，1989年，双删失生存数据分析与应用，生物识别艾滋病。
Kim, Mimi Y. and De Gruttola, Victor G. and Lagakos, Stephen W., 1993, Analyzing Doubly Censored Data With Covariates, With Application to AIDS, Biometrics.
金咪咪华和德Gruttola的的，维克多G.和Lagakos，斯蒂芬·1993年，协变量双截尾数据分析，随着艾滋病，生物识别的应用。
举例----------Examples----------
data(hiv)
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-25 22:09:47
