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2006-05-14
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2006-5-15 04:28:00
中文题名 左截断右删失模型中的非参数统计推断  
副题名
外文题名 Nonparametric statistical inference in the left truncated and right censored model
论文作者 孙六全
导师 郑忠国教授
学科专业 概率论与数理统计
研究领域\研究方向 数理统计及其应用
学位级别 博士
学位授予单位 北京大学
学位授予日期 1998
论文页码总数 156页
关键词 非参数统计 数理统计 左截断右删失 乘积限估计
馆藏号 BSLW /1999 /O212.7 /6
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2006-5-15 04:28:00
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2006-5-15 04:45:00
SAS MACRO FOR FITTING A MULTIVARIATE LOG-LOGISTIC SURVIVAL MODEL BASED ON INTERVAL-CENSORED DATA
Sofware : SAS Version : 8.2
Prepared by: Kris Bogaerts
Description : The macro can fit a multivariate log-logistic surivival model on interval-censored data using a Generalised Estimating Equations (GEE) like technique.
Download (size 123KB)
Last Updated : 12 March 2005
Download (size 123KB)
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2006-5-15 04:47:00

SAS FAQ
How can I model repeated events survival analysis in proc phreg?

http://www.ats.ucla.edu/stat/SAS/faq/survival_repeated_events.htm

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2006-5-15 04:48:00

PROC TRAJ is a SAS procedure that fits a discrete mixture model to longitudinal data. The model performs data sequence grouping, with different parameter values for the groups' data distribution. Groupings may identify distinct subpopulations. Alternatively, groups may represent distribution components approximating an unknown (possibly complex) data distribution.

Supported distributions are: censored (or regular) normal, zero inflated (or regular) Poisson, and Bernoulli distributions (logistic model). The censored normal model is useful for psychometric scale data, the zero inflated Poisson model useful for count data with extra zeros, and the Bernoulli model useful for 0/1 data. The model is appropriate for data with average values changing smoothly as a function of the dependent variable (time, age, ...). Some sharp changes can be handled through the inclusion of time dependent covariates.

MODEL STRUCTURE: Data sequences, Y, with similar shapes are grouped in a model-based manner. The probability of group membership can be a function of time stable covariates (risk factors), Z. Time dependent covariates, W, can further influence trajectories with effects differing by group, C. A trajectory model for two sets of dependent variables (joint trajectory modeling) is also supported. The model is illustrated in the figure below.

Downloads: Jones, B., Nagin, D., & Roeder, K. "A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories," Sociol Method Res, 2001, 29: 374-393.

Jones, B. & Nagin, D. "Advances in Group-Based Trajectory Modeling and a SAS Procedure for Estimating Them," submitted.

Nagin, D. "Analyzing Developmental Trajectories: A Semi-parametric, Group-based Approach," Psychol Methods, 1999, 4: 139-177.

Nagin, D. and Tremblay, R E. "Analyzing Developmental Trajectories of Distinct but Related Behaviors: A Group-Based Method," Psychol Methods, 2001, 6: 18:34.

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