Female Breast Cancer

This document explores the incidence and mortality of female breast cancer in England. It investigates the relationship between incidence and mortality, analyzes data from 1971 to 2011, and provides recommendations for future research. The study finds a negative correlation between incidence and mortality, suggesting that an increase in incidences leads to a decrease in deaths. The document also highlights the limitations of the investigation and provides recommendations for improving statistical skills among students.

Female Breast Cancer
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Female Breast Cancer
Female Breast Cancer in England
I. Abstract
Breast cancer has become a major challenge globally among women. Recent research that was
conducted by the World Health Organization (WHO) shows that three in every twenty people
have breast cancer and one in every five has breast cancer that has not been diagnosed. The
research further shows that of all cancer death in the world 25% of those deaths, are caused by
breast cancer. The England center for disease control has continuously conducted research on
how they can find a cure to this global disaster. And it’s from this that research was decided to be
conducted to investigate whether the incidence of female breast cancer causes mortality of the
patient. The research was conducted in England using data that we obtained from the WHO data
source that contained information about cancer occurrence and mortality from the year 1971 to
the year 2011. From this data, a random sample was obtained that was used in the analysis for
the investigation of the problem under study.
II. Introduction
Breast cancer is the development of tumors or uncontrollable cell, that mutates rapidly than the
rest of the cells causing implications to the breast and hinders the normal functioning of the usual
cells in the breast. Breast cancer treatment depends on the stage that the disease was diagnosed.
If breast cancer is diagnosed at early stages there are usually high chances of the patient getting
treatment without any side effects. However, if the disease is diagnosed at its late stages at times
it leads to the death of the patient leading to high mortality associated with such incidence.
Research into the question about the continued rise of breast cancer has raised alarm all over the
world. Recently research carried out stated that unless people change their lifestyle, breast cancer
is going to continue rising even to worse stages (Thorlacius et al, 2016). The following proposal

Female Breast Cancer
outlines the intent to research the relationship between the incidence of cancer and the associated
mortality of the victim.
III. Hypothesis
Due to the overwhelming death associated with breast cancer, it was decided an investigation to
be conducted to identify the relationship between the incidence of cancer and the mortality of the
patient (Bray et al, 2014). Generally, the incidence of the breast cancer is the time during which
the person is diagnosed and found having the cancerous tissue in her body, while the mortality,
in this case, is the death associated with a person testing positive for cancer and the death
associated with this.
An investigation to investigate if there exists any relationship between the incidence of cancer in
female and the death associated with it was carried out. As the years move on leads to
advancement in technology in all spheres of the field involving the health sector and the
discovery of new drugs (DeSantis et al, 2015). Further, it was required to investigate whether as
the years progressed the mortality rate of the females with breast cancer increased or decreased
and also whether the number of incidences of patients diagnosed with the breast cancer increased
or decreased with time.
IV. Time table for the investigation
Procedures followed in the investigation
I. First week
identifying the theme of the study
II. Second week
Identifying the source of data and determining the sample size

Female Breast Cancer
III. Third week
Data analysis
Performing the analysis of the data obtained and interpreting the results from the analysis
IV. Fourth week
Report for the investigation: this will involve the findings of the investigation based on the
data obtained and the analysis done.
I. Data collection
The data for female breast cancer patient was obtained from http://www.who.int/research/en/.
From this dataset, a random sample of 41 females with female breast cancer was generated. The
dataset contained incidences and the mortality of female breast cancer victims from the year
1971 to 2011 in England. The sample is attached in Appendix 1. The sample was selected by use
of random numbers to ensure that it represented the entire population without any biasedness.
The selection of the random sample aided in drawing conclusions that provided sufficient and
consistent information about the population (Jemal et, 2009).
II. Data Analysis
The analysis of the data was done using Microsoft Excel to generate very plots and also carry
out various computations. This is because Microsoft Excel provides good visual displays for
small datasets like the one was used (Schairer et al, 2014).
Analysis of the data began by exploring the relationship between incidence and mortality.
The mortality of female breast cancer patients depended on the incidence of female breast

Female Breast Cancer
cancer. This is because people could only die of female breast cancer if they had any
incidence of it.
The following scatter plot represents the trend of female breast cancer incidence and
mortality.
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
female breast cancer in England from 1971-20111
Incidence Mortality
years
%age of the incidence and occurrence of cancer
Scatter plot was used to represent the above information since it is a time series data (Colditz,
2008). From the scatter plot above it clearly indicate that the incidences of the female breast

Female Breast Cancer
cancer increased as years progressed while the mortality decreased in as years progressed.
This means that there a negative correlation between incidence and mortality of female breast
cancer. This implies that an increase in the number of incidences leads to a decrease in the
number of deaths.
Since breast cancer is a lifestyle disease that is, breast cancer is affected by our daily ways of
life, the number of incidence increases because the increase in years is associated with the
change of the ways of life. As years progresses females are more exposed to the risk factors
that cause breast cancer like some of the colognes used to contain heavy metals like mercury
which is high-risk cancer if breast cancer. Recent research by the American Statistical
Association stated that some of the bras that ladies wear to in their breasts contain partial lead
which is also carcinogenic (Beral et al, 2015).
The number of death decreasing in the increase of years is because of the revolution of the
health sector lead to the discovery of new drugs that have aided in the reduction of death
associated with breast cancer. Further, there has been the discovery of high tech cancer
detection instrument such as laser scans which makes it possible for cancer to be detected in
its early stages and hence is easily cured. There has been the development of new ways of
cancer treatment which cures cancer even in its late stages which include chemotherapy and
radiotherapy which were not there in the early years (Prentice and Gloeckler, 2009).
It was later investigated how the incidence and mortality were distributed using the following
bar graph

Female Breast Cancer
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
female breast cancer
Incidence Mortality
% for incidences and mortality
count
From the graph above there is a clear indication that mortality reduced as years progressed as
the incidences increases as the years progressed. It clearly shows that the incidences were
more skewed to the left while the mortality was more skewed to the right. Both the mortality
and the incidences had bimodal distributions. Also, it was noted that the highest number of
incidences occurred in the year 2011 while the highest number of death occurred in 1971,
this because of the negative correlation between the incidences and the mortality (Dupont
and Page, 2015). It can be estimated that in the next year more incidences of female breast
cancer and less mortality of the female breast cancer could be expected.
A descriptive statistics for both incidences and mortality was done which is provided in the
table below;
incidence Mortality
Mean
99.168
35.685
Median
104.156
38.779
Mode 0 0

Female Breast Cancer
Range 60.245 17.386
Standard deviation 20.93 5.855
IQR 56.4 43.1
SRCC 45.8 54.1
From the summary of the descriptive statistics above incidence had an average of 99.1679
meaning that most of the incidences were around that average. Incidence had a standard
deviation of 20.9372 while mortality had 5.8553. This implies that incidence had a greater
variation than mortality. The median for incidence was 104.156 which means that any given any
year the incidence of breast cancer was 104.156 while that of mortality was 38.7794 which
means that in any year there was likely to have a mortality of 38.7794.
Finally, inferential statistics were performed to determine whether mortality was related to
incidence based on the following hypothesis;
H0: Incidence is not significant in explaining the mortality
Against
H1: Incidence is significant in explaining mortality.
The analysis was based on the following regression model Y = B0 + B1 X, where B0 and B1
are regression coefficients, y represent the mortality and X represents the incidence at 5%
level of significance (Tusher et al, 2010). The output of the summary for the regression
model is as follows;
SUMMARY OUTPUT

Female Breast Cancer
Regression Statistics
Multiple R
0.8554448
4
R Square
0.7317858
8
Adjusted R
Square
0.7249085
9
Standard Error
3.0710371
4
Observations 41
ANOVA
df SS MS F
Significanc
e F
Regression 1
1003.5456
5
1003.5456
5
106.40621
5
1.0544E-
12
Residual 39
367.81949
6
9.4312691
2
Total 40
1371.3651
5
Coefficient
s
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
59.408925
1
2.3493635
4
25.287242
3
8.4541E-
26
54.656888
8
64.160961
4
54.656888
8
64.1609
Incidence
-
0.2392316
0.0231918
4
-
10.315339
1.0544E-
12
-
0.2861416
-
0.1923217
-
0.2861416 0.19232
The coefficient for the incidence is -0.23923, this means that the mortality of people with
female breast cancer decreases by 0.239 units per unit increase the incidence of female breast
cancer. The model has an adjusted R-squared of 72.5. This was interpreted that 72.5 of the
variability was explained by the incidence this implies that the model fits the data
appropriately. The conclusion for the hypothesis was made by comparing the p-value with
the level of significance (Tabar, 2013). The model had a p-value of 0.0000006478 which is
much less than 0.05 level of significance. Therefore, the null hypothesis was rejected and

Female Breast Cancer
thus the incidence was statistically significant in explaining the mortality of the female breast
cancer victims.
III. Limitation of the investigation
The major limitation of the study was the time. The supervisor should provide more time to
the student so that the students can make maximum utilization of the study. The cost of
conducting the investigation was also very high, the school should provide a subsidy for the
investigation. The school should also ensure that all students are well equipped with
statistical skills which were found quite challenging for students that were not from statistic
background (Shah and Hoeffner, 2014).
IV. Recommendation
The school should ensure that all students in the university go through at least two statistical
courses. This will aid them in their research and any data handling activity they get involved
in. Also, the supervisors should ensure they are in close contact with the students during the
research period to ensure they guide them through. Finally, the students should be taught
various statistical software that will aid them in doing the analysis for the data during their
research period.
V. Conclusion
In conclusion, to increase the adjusted R-squared and in order to obtain high accuracy of the
model, a large sample should be used (Van Dam and Groen, 2010). The purpose of the study
was achieved since the right answers to the problem of the statement were found. In case
using a very large sample, we should opt to other statistical software like R package and JMP
they aid in visualizing the data more precisely and even produces 3D models.

Female Breast Cancer
References
Beral, V., Banks, E. and Reeves, G., 2012. Evidence from randomised trials on the long-term
effects of hormone replacement therapy. The Lancet, 360(9337), pp.942-944.
Bray, F., McCarron, P. and Parkin, D.M., 2004. The changing global patterns of female
breast cancer incidence and mortality. Breast cancer research, 6(6), p.229.
Colditz, G.A., 2008. Relationship between Thorlacius, S., Olafsdottir, G., Tryggvadottir, L.,
Neuhausen, S., Jonasson, J.G., Tavtigian, S.V., Tulinius, H., Ögmundsdottir, H.M. and
Eyfjörd, J.E., 2016. A single BRCA2 mutation in male and female breast cancer families
from Iceland with varied cancer phenotypes. Nature genetics, 13(1), p.117.
DeSantis, C.E., Ferlay, J., Lortet-Tieulent, J., Anderson, and B.O., 2015. International
variation in female breast cancer incidence and mortality rates. Cancer Epidemiology and
Prevention Biomarkers, 24(10), pp.1495-1506.
Dupont, W.D. and Page, D.L., 2015. Risk factors for breast cancer in women with
proliferative breast disease. New England Journal of Medicine, 312(3), pp.146-151.
Jemal, A., Siegel, R., Ward, E., Hao, Y., Xu, J. and Thun, M.J., 2009. Cancer statistics,
2009. CA: a cancer journal for clinicians, 59(4), pp.225-249.
Prentice, R.L. and Gloeckler, L.A., 2009. Regression analysis of grouped survival data with
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Schairer, C., Mink, P.J., Carroll, L. and Devesa, S.S., 2014. Probabilities of death from breast
cancer and other causes among female breast cancer patients. Journal of the National Cancer
Institute, 96(17), pp.1311-1321.

Female Breast Cancer
Shah, P. and Hoeffner, J., 2014. Review of graph comprehension research: Implications for
instruction. Educational Psychology Review, 14(1), pp.47-69.
Tabar, L., Yen, M.F., Vitak, B., Chen, H.H.T., Smith, R.A. and Duffy, S.W., 2013.
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before and after introduction of screening. The Lancet, 361(9367), pp.1405-1410.
Tusher, V.G., Tibshirani, R. and Chu, G., 2010. Significance analysis of microarrays applied
to the ionizing radiation response. Proceedings of the National Academy of Sciences, 98(9),
pp.5116-5121.
Van Dam, P.M. and Groen, J.G., Medtronic Inc, 2010. Method of and apparatus for
classifying arrhythmias using scatter plot analysis. U.S. Patent 7,657,307.

Female Breast Cancer
Appendix 1
Year Incidence Mortality
1971 66.1 38.9
1972 70.5 38.8
1973 71.3 39.6
1974 75.7 38.8
1975 72.8 40.1
1976 70.8 40.1
1977 73.1 39.8
1978 75.0 40.3
1979 74.6 40.2
1980 78.2 40.3
1981 78.1 41.2
1982 80.2 40.7
1983 80.0 41.0
1984 79.1 41.3
1985 86.1 41.4
1986 86.0 41.7
1987 89.5 41.4
1988 90.9 41.1
1989 96.8 41.4
1990 100.2 40.7
1991 107.1 40.3
1992 109.8 39.5
1993 104.2 37.6
1994 106.1 36.9
1995 106.7 35.9
1996 108.2 34.4
1997 114.2 33.6
1998 115.5 32.7
1999 120.8 31.6
2000 117.4 30.9
2001 118.6 30.7
2002 118.1 30.2
2003 124.1 29.2
2004 124.6 28.4
2005 125.6 28.2
2006 124.6 27.5
2007 123.1 26.7
2008 126.4 26.2
2009 124.9 25.4

Female Breast Cancer
2010 125.7 24.3
2011 125.3 24.3