Problem 2: Use
R or SOCR to simulate a timeseries of a clinical data including
1,000 observations St satisfying: St=So
e(vt+σ√tZ), where Z∼Normal(0,1) and the time, t, varies
between 0, 1, 2, ..., 999.
Choose some parameters for the baseline measurement So, the
variance parameter σ2 and the slope constant v.
Identify 2 different sets of parameters, (So,σ2,v),
yielding significantly different timeseries (e.g., increasing, decreasing,
static, stationary), and explain these discordances.
Problem 4: Suppose a cognitive performance measure for TBI patients
has variability associated with it quantified by σX=10. In many
clinical studies we are interested in contrasting different cohorts (e.g.,
comparing an experimental intervention against placebo or classical treatment
protocols). In such situations, we are interested in the
sampling distribution
of the mean response of a group of patients.
If we take a sample of 10 TBI patient, what will be sampling standard deviation,
σX¯ in terms of the standard deviation of the cognitive performance
measure (σX)? Does it depend on the shape of the original process?
How large should the patient cohort be so that σX¯=(1/2)σX?
σX¯=0.1σX?
Suppose the distribution of the cognitive performance scores for TBI patients
is
Generalized Beta(α=4, β=3, A=0,B=100), what is the probability that
a randomly chosen TBI patient will have a cognitive test exceeding 90?
Use the
SOCR Modeler and (see
RNG Activity) to generate a sample of 1,000 random cognitive scores from the same distribution
(
Generalized Beta(α=4, β=3, A=0,B=100)). Find the proportion of
cognitive scores above 90 and contrast this with the probability you computed above.