Survival Analysis Project (Spring 2015)
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Due date: Thursday, July 30 (5 pm)
Your job: provide results for this clinical investigators manuscript
The following should be included
o description/reiteration of the questions/aims
o statistical methods section (as if for the manuscript)
o results section
o conclusion/discussion of the implications of results
Limitations: No more than 5 typed pages in Arial 11 (or comparable font) with at
least 1 inch margins. The 5 page limit excludes figures, tables, R code and any
references. Do not feel obligated to use up all 5 pages. If you can satisfactorily
complete the project in fewer pages, please do so!
You can ask Dr. Wolf questions about this. She will play the role of the clinical
investigator (to the best of her ability). Once classes are over, email any questions
you have and if needed, schedule a meeting. If deemed appropriate, Dr. Wolf will
share with the class.
The only exception is questions about R code: if you have questions about R code in
reference to this project, please email it to Dr. Wolf. She will share with the class if
she thinks it is appropriate.
Things to remember:
 This is designed to simulate an interaction with a clinical investigator: detailed
instructions are deliberately not included.
 Clinical investigators are not statisticians: they do not see statistical issues in their
datasets like we do. Just because they may not have mentioned a major statistical
issue does not mean that it does not exist! Be sure to think about the design and any
necessary complications that arise from it.
 Clinical investigators sometimes ask for things that are not possible with their data
(which may or may not be true in this case: I just don’t want you to go on a wild
goose chase if you’ve reasoned that something is not feasible).
 Clinical investigators can only phrase the hypothesis or aim: they generally do not
know what statistical approach will address the hypothesis or aim.
 There is no ‘right’ answer in a project like this (although there may be incorrect
approaches).
 You need to use your statistical judgment along with scientific reasoning to tackle
this project.
 Note to take a look at the data: you may need to do some data management to
create the necessary variables to address the problem.
 Try not to be redundant. If you are repeating the same analysis using a different
outcome, you needn’t reiterate the methods. Remember that most journals have
stringent page limits so being succinct is important.
 It is generally a good idea to put numerical results in tables or figures and then refer
to them from the text. The text should be used for interpreting and making
inferences. You do not need to detail every numerical result in the text.
 Keep it organized!
Impact of diabetes and hypertension on graft failure and death in kidney transplant
patients
Background of the problem:
Evaluation for kidney transplant has traditionally required Human Leukocyte Antigen
[HLA] typing. HLA plays a critical role in determining self versus non-self tissue and
cells and consequently as implications of graft survival. An attempt is made to match the
HLA typing for the donor kidney with the patients, however, this is not always feasible
and thus not all kidney/patient pairs are matched. Induction therapy is designed to
prevent or reduce incidence of graft rejection and thus control for failure to match HLA
typing between the donor and host.
Thus it is of interest to investigate the impact of the degree to which patients are HLA
mismatched and the type of induction therapy they receive. Scientists would like to
understand if a particular type of induction therapy is better able to compensate for the
degree of HLA mismatch.
Description of dataset: The data include 555 unique subjects who underwent kidney
transplant at 1 academic medical center between 2005 and 2008. Data was collected on
time to graft failure and time to death. The data also included the demographic variables
gender, age, and ethnicity. Additionally, the data included indicators delayed graft
function (yes, no), donor type, whether or not a subject experiences a rejection episode
and timing of the rejection episode, the number of HLA DR mismatches, induction
therapy type, years on dialysis, cold ischemic time, warm ischemic time, diagnosis,
serum creatinine levels at 1 year post-procedure, and serum creatinine 3 years postprocedure.
The variables included in the dataset are as follows:
1.
2.
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Subject number
Creatinine at 1 year post-op
Age at time of transplant
Sex: 0=male, 1=female
Race: 0=Caucasian, 1=AA, 2=Other
Donor type: 0=dead donor, 1=living donor
Diagnosis
Number of HLA mismatches
Delayed graft function: 0=No, 1=Yes
Cold ischemic time (hours)
Warm ischemic time (minutes)
Did they experience a rejection episode?
Induction therapy
Induction therapy class: 1=IL2, 2=Thymo/Campath
Indicator of graft failure
Indicator of death
Time to graft failure
18.
Time to death
Questions of the clinical investigators:
1. Are induction therapy type and number of HLA mismatches related to either
death or graft failure?
2. Are age, gender, and race related to graft failure or death?
3. Similarly, what about warm/cold ischemia time, serum creatinine at 1 year,
occurrence of rejection, or whether or not the patient has diabetes, hypertension,
or some type of nephritis?
4. Is the impact of number of HLA mismatches on graft failure or death different
depending on which induction therapy (or class of induction therapy) was given?
What about other factors?
5. Do these factor impact graft failure only?