By Norman Breslow (auth.), D. Y. Lin, P. J. Heagerty (eds.)
This quantity includes a choice of papers offered on the moment Seattle Symposium in Biostatistics: research of Correlated info. The symposium was once held in 2000 to have a good time the 30th anniversary of the collage of Washington institution of Public healthiness and neighborhood drugs. It featured keynote lectures by means of Norman Breslow, David Cox and Ross Prentice and sixteen invited displays through different favourite researchers. The papers contained during this quantity surround fresh methodological advances in numerous very important parts, equivalent to longitudinal information, multivariate failure time information and genetic information, in addition to leading edge purposes of the prevailing thought and techniques. This quantity is a priceless reference for researchers and practitioners within the box of correlated info analysis.
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Extra info for Proceedings of the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data
When g(ylx , B) is a normal regression, B is identifi able when the number of continuous variables that X contains is no less than the dimension of Y. Intuitively, under such a condition, parameters of [YIX] are uniquely determined by the format of [XIYJ, which can be empirically estimated from the complete cases since complete cases are a random sample from [XIYj under assumption (9). Let PLO, PL1 and PL2 denote maximum pseudolikelihood estimates under scenarios (a) , (b) and (c), respectively.
3 includes regression coefficients and residual variance calculated based on ¢( r). Then th e pattern-mixture meth od of Little and Wang (1996) can be used to estimate ¢(2) based on the subset [R > 1]. Similarly, if we assume th at t he left hand and t he right hand of (6) are approximately normal , we have ¢~~~ 2 = ¢g~~ , where ¢g~~ represents t he set of t he regression coefficients and residual variance of [Y1, Y3 1Y2 , R > 1]. From t he first step, ¢~;~~ is est imated based on esti mates of ¢(3) and ¢(2) , and the sample proportions of R = 2 and R = 3.
A typical pattern-mixture model assumes that given R = r , [Yl , Y2 , Y3 ] is trivariate normal , r = 1,2 ,3. However, (4) implies that: (6) Analysis of Multivariate Monotone Missing Data 39 Th e left hand of (6) is a normal distribution and the right hand of (6) is a mixture of two norm al distributions. Equ atin g these distributions requir es st ronger assumptions t han we are willing to make. Thi s example shows t hat pat tern-mixture models have limit ed flexibility for general multivariate monotone missing dat a.