Biomedical Engineering Reference
In-Depth Information
treatment of radiotherapy with adjuvant chemotherapy significantly increases
the risk of the breast retraction, and the result is comparable with what was
concluded in Finkelstein and Wolfe (1985).
Example 2: Bladder Tumor Study. The data set of the bladder tumor
randomized clinical trial conducted by the Veterans Administration Coop-
erative Urological Research Group (Byar, Blackard, and VACURG, 1977) is
extracted from Andrews and Herzberg (1985, p. 253{260). In this study, a
randomized clinical trial of three treatments | placebo, pyridoxine pill, and
thiotepa instillation into bladder|was conducted for patients with supercial
bladder tumor (a total of 116 subjects: 40 were randomized to placebo, 31
to pyridoxine pill, and 38 to thiotepa instillation) when entering the trial. At
each follow-up visit, tumors were counted, measured, and then removed after
being found. The treatments as originally assigned will continue after each
visit. The number of follow-up visits and follow-up times varied greatly from
patient to patient, and hence the observation of bladder tumor counts in this
study falls in the framework of panel count data as described in Section 9.4.
For this trial, the treatment effects, particularly the thiotepa instillation
method, on reducing bladder tumor recurrence have been the focal point of
interest in many studies; see, for example, Wei et al. (1989), Sun and Wei
(2000), Zhang (2002), and Wellner and Zhang (2007). In this chapter, we study
the proportional mean model as proposed by Wellner and Zhang (2007),
EfN(t)jZg =
0
(t) exp(
0;1
Z
1
+
0;2
Z
2
+
0;3
Z
3
+
0;4
Z
4
);
(9.18)
where Z
1
and Z
2
are the baseline tumor count and size, measured when sub-
jects entered the study, and Z
3
and Z
4
define the indicators of the pyridoxine
pill and instillation treatments, respectively. Lu et al. (2009) have used the cu-
bic B-splines sieve semiparametric MLE method for this model and estimated
the asymptotic standard error of the estimate of
0
based on the bootstrap
approach. In this chapter, we reanalyze the data using the same method but
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