Use of statistical analysis, data mining, decision analysis and cost effectiveness analysis to analyze

236 Pages·2017·9.1 MB·English

by  Beatrice Ugiliweneza

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Electronic Theses and Dissertations
12-2011
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Beatrice Ugiliweneza
University of Louisville
Follow this and additional works at: https://ir.library.louisville.edu/etd
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Ugiliweneza, Beatrice, "Use of statistical analysis, data mining, decision analysis and cost effectiveness
analysis to analyze medical data : application to comparative effectiveness of lumpectomy and
mastectomy for breast cancer." (2011). Electronic Theses and Dissertations. Paper 1473.
https://doi.org/10.18297/etd/1473
This Doctoral Dissertation is brought to you for free and open access by ThinkIR: The University of Louisville's
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USE OF STATISTICAL ANALYSIS, DATA MINING, DECISION ANALYSIS AND
COST EFFECTIVENESS ANALYSIS TO ANALYZE MEDICAL DATA:
APPLICATION TO COMPARATIVE EFFECTIVENESS OF LUMPECTOMY AND
MASTECTOMY FOR BREAST CANCER
By
Beatrice Ugiliweneza
B.S. Niamey, NIGER, 2006
A Dissertation
Submitted to the Faculty of the
College of Arts and Sciences of the University of Louisville
in Partial Fulfillment of the Requirements
for the Degree of
Doctor of Philosophy
Department of Mathematics
University of Louisville
Louisville, Kentucky
December 2011
Copyright 2011 by Beatrice U giliweneza
All rights reserved
USE OF STATISTICAL ANALYSIS, DATA MINING, DECISION ANALYSIS AND
COST EFFECTIVENESS ANALYSIS TO ANALYZE MEDICAL DATA:
APPLICATION TO COMPARATIVE EFFECTIVENESS OF LUMPECTOMY AND
MASTECTOMY FOR BREAST CANCER

By

Beatrice Ugiliweneza

B.S. Niamey, NIGER, 2006

A dissertation approved on

November 4, 2011

By the following Dissertation Committee:

Dr. Patricia Cerrito (Director)

Dr. Ryan Gill (Co-Director)

Dr. Kiseop Lee

Dr. Jiaxu Li

Dr. Ibrahim Imam

Dr. Mehmed Kantardzic
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DEDICATION
This thesis is dedicated to my wonderful family:
My Husband Thacien Ntihinyurwa for his continued support
My daughters Benita Bwiza Ntihinyurwa and Elisa Hora Ntihinyurwa for the inspiration I
get from their eyes every morning
My sister Pacifique Munezero, my brothers Desire Isidore Munyeshuli
and Romeo Irere K wihangana for their invaluable encouragement
My beloved mother Annonciata Nyiramahoro for the strength she put in me, for making
me who I am today and without whom all this could not have happened
My late father Valere Munyakazi whom I love very much and miss every day for
teaching me at a very young age that the most important things in life are to obey God's
commandments and respect people
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ACKNOWLEDGMENT
I would like to thank my advisor Dr. Patricia Cerrito, for her guidance, help and
patience. I would also like to thank my co-advisor Dr. Ryan Gill for invaluable help,
suggestions and comments. My thanks go to the other committee members, Dr. Mehmed
Kantardzic, Dr. Ibrahim Imam, Dr. Kiseop Lee and Dr. Jiaxu Li as well for their
comments. I would also like to express my thanks to my husband, Thacien, who had to be
mommy many times, and to my daughters, Benita and Elisa, who had to understand at a
very young age that I could not be there every time they needed me. Also, many thanks to
my mother, Annonciata, my sister, Pacifique, my brothers, Desire and Romeo, who gave
me the encouragement I needed to stay on track. Finally, I would like to thank all other
member of my family and friends in Louisville, Kentucky, in San Diego, California, in
Dayton, Ohio, in Kenya, in Rwanda, in France, in Belgium, in Niger, and all over the
world.
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ABSTRACT
USE OF STATISTICAL ANALYSIS, DATA MINING, DECISION ANALYSIS
AND COST EFFECTIVENESS ANALYSIS TO ANALYZE MEDICAL DATA:
APPLICATION TO COMPARATIVE EFFECTIVENESS OF LUMPECTOMY
AND MASTECTOMY FOR BREAST CANCER
Beatrice Ugiliweneza
November 4t\ 2011
Statistical models have been the first choice for comparative effectiveness in clinical
research. Though effective, these models are limited when the data to be analyzed do not
fit the assumed distributions; which is mostly the case when the study is not a clinical
trial. In this project, data mining, decision analysis and cost effectiveness analysis
methods were used to supplement statistical models in comparing lumpectomy to
mastectomy for surgical treatment of breast cancer. Mastectomy has been the gold
standard for breast cancer treatment for since the 1800s. In the 20th century, an
equivalence of mastectomy and lumpectomy was established in terms of long-term
survival and disease free survival. However, short term comparative effectiveness in
post-operative outcomes has not been fully explored. Studies using administrative data
are lacking and no study has used new technologies of self-expression, particularly the
internet discussion board. In this study, data used were from the Nationwide Inpatient
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Sample (NIS) 2005, the Thomson Reuter's MarketScan 2000 - 2001, the medical
literature on clinical trials and online individuals' posts in discussion boards on
breastcancer.org. The NIS was used to compare lumpectomy to mastectomy in terms of
hospital length of stay, total charges and in-hospital death at the time of surgery.
MarketScan data was used to evaluate the comparative follow-up outcomes in terms of
risk of repeat hospitalization, risk of repeat operation, number of outpatient services,
number of prescribed medications, length of stay, and total charges per post-operative
hospital admission on a period of eight months average. The MarketScan was also used
to construct a simple post-operative hospital admission predictive model and to perform
short-term cost-effectiveness analysis. The medical literature was used to analyze long
term -10 years- mortality and recurrence for both treatments. The web po stings were used
to evaluate the comparative cost to improve quality of life in terms of patient satisfaction.
In NIS and MarketScan data, International Classification of Disease, 9th revision, Clinical
Modification (lCD-9-CM) diagnosis codes were used to extract cases of breast cancer;
and ICD-9-CM procedure codes and Current Procedural Terminology, 4th edition
procedure codes were used to form groups of treatment.
Data were pre-processed and prepared for analysis using data mining techniques such as
clustering, sampling and text mining. To clean the data for statistical models, some
continuous variables were normalized using methods such as logarithmic transformation.
Statistical models such as linear regression, generalized linear models, logistic and
proportional hazard (Cox) regressions were used to compare post-operative outcomes of
lumpectomy versus mastectomy. Neural networks, decision tree and logistic regression
predictive modeling techniques were compared to create a simple predictive model
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predicting 90-day post-operative hospital re-admission. Cost and effectiveness were
compared with the Incremental Cost Effectiveness Ratio (lCER). A simple method to
process and analyze online po stings was created and used for patients' input in the
comparison oflumpectomy to mastectomy. All statistical analyses were performed in
SAS 9.2. Data Mining was performed in SAS Enterprise Miner (EM) 6.1 and SAS Text
Miner. Decision analysis and Cost Effectiveness Analysis were performed in TreeAge
Pro 2011.
A simple comparison of the two procedures using the NIS 2005, a discharge-level data,
showed that in general, a lumpectomy surgery is associated with a significantly longer
stay and more charges on average. From the MarketScan data, a person-level data where
a patient can be followed longitudinally, it was found that for the initial hospitalization,
patients who underwent mastectomy had a non-significant longer hospital stay and
significantly lower charges. The post-operative number of outpatient services, prescribed
medications as well as length of stay and charges for post-operative hospital admissions
were not statistically significant. Using the MarketScan data, it was also found that the
best model to predict 90-day post-operative hospital admission was logistic regression. A
logistic regression revealed that the risk of a hospital re-admission within 90 days after
surgery was 65% for a patient who underwent lumpectomy and 48% for a patient who
underwent mastectomy. A cost effectiveness analysis using Markov models for up to 100
days after surgery showed that having lumpectomy saved hospital related costs every day
with a minimum saving of $33 on day 10. In terms of long-term outcomes, the use of
decision analysis methods on the literature review data revealed that, 10-years after
surgery, 739 recurrences and 84 deaths were prevented among 10,000 women who had
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mastectomy instead oflumpectomy. Factoring patients' preferences in the comparison of
the two procedures, it was found that patients who undergo lumpectomy are non
significantly more satisfied than their peers who undergo mastectomy. In terms of cost, it
was found that lumpectomy saves $517 for each satisfied individual in comparison to
mastectomy.
In conclusion, the current project showed how to use data mining, decision analysis and
cost effectiveness methods to supplement statistical analysis when using real world non
clinical trial data for a more complete analysis. The application of this combination of
methods on the comparative effectiveness of lumpectomy and mastectomy showed that in
terms of cost and patients' quality of life measured as satisfaction, lumpectomy was
found to be the better choice.
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