Case Study: Locating New Pam and Susans Store
Alexander Barton
MGSC6200 13003 Information Analysis
SEC 04 - Fall 2016
Professors Grigorios Livanis & Nizar Zaarour
Due Sunday, November 6, 2016
MGSC6200 13003 Information Analysis SEC 04 - Fall 2016
Week 4 P
MGSC6200 13003 Information Analysis SEC 04 - Fall 2016
Week 3 Assignment 1
Alexander Barton
Due: Sunday October 30, 2016
Chapter 5
Exercise 6 (Page 170)
a) No because it is a directional relationship. Reduction of the number of different illnesses
patient
MGSC6200 13003 Information Analysis SEC 04 - Fall 2016
Week 1 Assignment 1
Alexander Barton
Due: Sunday October 16, 2016
Chapter 1
Exercise 6 (Page 21) As defined by the course material, a confounding factor is a characteristic that differs between the tw
MGSC6200 13003 Information Analysis SEC 04 - Fall 2016
Week 2 Assignment 1
Alexander Barton
Due: Sunday October 22, 2016
Chapter 9
Exercise 4 (page 310)
a) Investing all your money in a few randomly chosen stocks that make up the index
b) Investing all yo
Title
: Locating New Pam and Susans Stores
Name
: Krupa Stephen Gadde
Course
: Information Analysis (MGSC6200)
Facilitator : Anthi Tsouvali
Date
: November 4, 2016
Contents
1. Introduction
2. Data
3. Results and Discussion
4. Conclusion
Introduction:
Pam
260
Design and Analysis of Clinical Dials
be calculated by hand. In practice, this is not generally possible.
Instead Murkov Chain Monte Carlo methods are used; these are
based on Monte-Carlo integration methods for tackling the numerical
problems posed b
Bayesian Methods
275
+
variance 4/(22.7 40) = 0.064 (standard deviation 0.252). For the
enthusiasts, the probability of the hazard ratio being greater than,
say, 1.5 is 1- 4(log(1.5) - 0.930)/0.252) = 1 - 0.019 = 0.981. They
would be happy for the trial t
244
Design and Analysis of Clinical Trials
inclusion runs the risk of biasing the treatment effect as a result of
their being internal, i.e., they reflect the development of the disease
process and may themselves be partly influenced by treatment. Biochem
2 84
Design and Analysis of Clinical Trials
question (e.g., the same drug, disease and setting for studies following
a common protocol) or a more generic problem (e.g.: a broad class of
treatments for a range of conditions in a variety of settings). Pococ
246
Design and Analysis of Clinical Trials
be proportional, but the baseline hazard is now quite separately estimated for each stratum. The contribution to the partial likelihood
from each failure event is modified such that the risk set over which
the de
Bayesian Methods
0
0
259
1
Fig. 9.2. Four beta distributions.
+
which is another beta distribution but with parameters a* = a Iand p* = p n - I-.
The mean of this posterior distribution is ( a .)/(a
p n),
with a corresponding expression to that given abov
Bayesian Methods
277
Table 9.3. Gain Function for a Phase I1 Trial.
Gained Successes.
During
Phase I1
n ( P -Po>
During
Phase I11
m ( P -Po)
After
Phase I11
W ( P -Po>
true probability of success of new treatment.
probability of success on standard treatm
254
Design and Analysis of Clinical Trials
survival data is the presence of censored observations and this has
led to the development of a wide range of methodology for analysing
survival times. Of the available methods, the most widely used
is Coxs propo
Survival Analysis
245
frequently arise in organ and tissue transplantation, where at the
time of randomisation, no suitably well matched donors may be available for all patients. Two comparisons then become of interest. The
first essentially defines the t
Survival Analysis
253
in survival probabilities for the two treatments, for some suitable
choice of time t , ARR = S A ( t ) - SB(t). Using the Kaplan-Meier
estimates for S ( t ) ,a variance for the ARR can be obtained as
var(ARR) = [l - sA(t)][sA(t)]2/nA
Design and Analysis of Clinical Tnals
252
Beta-Blocker Heart Attack Trial
r
Z*ue
I
1.88
0
Juna
1978
136
0
May
1979
Ocl. MMcI)
1979 1980
Ocl.
1980
%Y
Ocl.
1981
June
1482
Dab
Fig. 8.10. OBrien and Fleming boundary for log-rank test in a randomised
double bl
Design and Analysis of Clinical Dials
248
8.8.
CENSORING AND COMPETING
RISKS
The discussion so far has given rather little consideration as to how
censored observations may have come about. Indeed, because they
seem to pose no practical difficulty for ana
Meta-Analysis
281
Chalmers and Lau conclude, It seems obvious that a discipline which
requires that all available data be revealed and included in an analysis
has an advantage over one that has traditionally not presented analyses of all the data on which
288
Design and Analysis of Clinical Trials
Table 10.1 (Continued)
Head. Subhead.
Descriptor
Was it
reported?
On what
page number?
Describe prognostic
variables by treatment
group and any attempt
t p adjust for them.
Describe protocol deviations
form the s
2 72
Design and Analysis of Clinical Traals
where n is the number of events (Tsiatis, 198l), the prior can be
summarised by a mean po and an implicit number of events no.
9.8.
9.8.1.
MONITORING
The Prior as a Handicap
Subsequent real data arising out of t
Bayesian Methods
257
While this may describe well the circumstances of the developers
of a treatment, who will be building up knowledge about a treatment
through various phases of the development and trialling process, licensing authorities have typically
Meta- Analysis
Ei
283
References
from completed
Tltles and UI
Non-RCT
rejects
L
Rejects
Copy 1 of full paper
and blinded paper
in separate folders
to Reader 1
Differentially
to Reader 2
Master file
with full paper
Quality scored
using Form 6 for
blinded m
294
Design and Analysis of Clinical %als
Table 10.2. (Continued)
The confidence interval for the underlying parameter is wider in the random effects model than in the fixed effects model. A random effects
analysis suggest more uncertainty in estimating th
262
Design and Analysis of Clinical K a l s
monitor the ra.nks of the effects of each treatment from each MCMC
sample, and to obtain an estimate of their distribution, confidence
intervals and so on.
A second example, one that we illustrate later in this
Survival Analysis
249
restricted to death from rejection (all other outcomes contributing
censored observations). The data come from the much analysed Stanford Heart Transplant programme (Crowley and Hu, 1997). The increased estimate and significance for
Design and Analysis of Clinical Trials
296
legitimacy, the sample must be the result of a random process; for
the argument to make sense, the population must be identifiable. In
the case of meta-analysis, Oakes argues that there are grounds for
concern on
250
Design and Analysis of Clinical Trials
Schoenfeld (1994) present a method that uses the time to the intermediate state as a covariate for subsequent survival. Their simulations
suggest that although improvements in precision of the estimates of
main i
Meta-Analysis
295
population of such studies, and that if heterogeneiby exists, that
substantially more weight is given to the smaller studies than in the
fixed effect approach when smaller studies are often those of poorer
quality. The problem is usually