       Document 0303
 DOCN  M9550303
 TI    Non-parametric estimation and doubly-censored data: general ideas and
       applications to AIDS.
 DT    9505
 AU    Jewell NP; Division of Biostatistics, University of California,
       Berkeley; 94720.
 SO    Stat Med. 1994 Oct 15-30;13(19-20):2081-95. Unique Identifier : AIDSLINE
       MED/95149004
 AB    In many epidemiologic studies of human immunodeficiency virus (HIV)
       disease, interest focuses on the distribution of the length of the
       interval of time between two events. In many such cases, statistical
       estimation of properties of this distribution is complicated by the fact
       that observation of the times of both events is subject to
       intervalcensoring so that the length of time between the events is never
       observed exactly. Following DeGruttola and Lagakos, we call such data
       doubly-censored. Jewell, Malani and Vittinghoff showed that, with
       certain assumptions and for a particular doubly-censored data structure,
       non-parametric maximum likelihood estimation of the interval length
       distribution is equivalent to non-parametric estimation of a mixing
       distribution. Here, we extend these ideas to various other kinds of
       doubly-censored data. We consider application of the methods to various
       studies generated by investigations into the natural history of HIV
       disease with particular attention given to estimation of the
       distribution of time between infection of an individual (an index case)
       and transmission of HIV to their sexual partner.
 DE    Acquired Immunodeficiency Syndrome/EPIDEMIOLOGY/*TRANSMISSION
       California/EPIDEMIOLOGY  Cohort Studies  Disease Progression  Disease
       Susceptibility  Female  Forecasting/*METHODS  Human  HIV
       Infections/EPIDEMIOLOGY/*TRANSMISSION  HIV Seropositivity/EPIDEMIOLOGY
       Likelihood Functions  Male  *Models, Biological  Sex Behavior  Sexual
       Partners  Statistics, Nonparametric  Support, Non-U.S. Gov't  Support,
       U.S. Gov't, P.H.S.  Time Factors  JOURNAL ARTICLE

       SOURCE: National Library of Medicine.  NOTICE: This material may be
       protected by Copyright Law (Title 17, U.S.Code).

