Read e-book online Analysis of Longitudinal Data (2nd Edition) PDF

By Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger

ISBN-10: 0198524846

ISBN-13: 9780198524847

The recent variation of this significant textual content has been thoroughly revised and multiplied to turn into the main updated and thorough expert reference textual content during this fast-moving and critical sector of biostatistics. new chapters were extra on totally parametric versions for discrete repeated measures facts and on statistical types for time-dependent predictors the place there's suggestions among the predictor and reaction variables. It additionally includes the numerous important good points of the former variation comparable to, layout concerns, exploratory tools of study, linear versions for non-stop information, and versions and strategies for dealing with info and lacking values.


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Additional resources for Analysis of Longitudinal Data (2nd Edition)

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6. CD4+ residuals against time since seroconversion. with sequences of data from systematically selected subjects shown as connec ted line segments. patterns in individual curves. Note that the data for each individual tend to track at different levels of CD4+ cell numbers, but not nearly to the same extent as in Fig. 2. The CD4+ data have considerably more variation across time within a person. Thus far, we have considered displays of the response against time. In many longitudinal problems, the primary focus is the relationship between the response and an explanatory variable other than time.

There is some prior belief that depressive symptoms are negatively correlated with the capacity for immune response. The MACS collected a measure of depressive symptoms called CESD; a higher score indicates greater depressive symptoms. 3. 7 plots the residuals with time trends removed for CD4+ cell numbers against similar residuals for CESD scores. Also shown is a lowess curve, which is barely discernible from the horizontal line, y = 0. Thus, there is very little evidence for an association between depressive symptoms (CESD score) and immune response (CD4+ numbers), although such evidence as there is points to a negative association, a larger CESD score being associated with a lower CD4+ count.

Except when (5 is small and the common correlation p is high, there is much to be gained by conducting longitudinal studies even when the number of repeated observations is as small as two. In case 2, we consider the situation when the true correlation matrix has the form Ri k . This is the correlation structure of a first order autoregressive process discussed in Chapter 5. In this case e= 1 - p2 (1 - p)(n (n - 2)p} + (5y/(n + 1)' where n(n + 1) - 2(n - 3)(n + 1)p + (n - 3)(n - 2)p 2 . The message regarding efficiency gain is similar as can be seen in Fig.

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Analysis of Longitudinal Data (2nd Edition) by Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger

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