Abstract
Prostate cancer is among the most common types of cancers in men in Kenya and world- wide. The exact cause of the prostate cancer is unclear. However, previous research has indicated that there are risk factors likely to be associated with prostate cancer such as: old age, race where black people are at more risk, family history, obesity among other risk factors. In the year 2018 among men, it was the second most diagnosed cancer (1.3 million, 14.5%) after lung cancer (1.4 million, 15.5%) worldwide.
This study aims to compare survival between clinical stages and treatment categories. The study further aims to investigate the impact age on survival among prostate cancer patients in Kenya. The study used secondary population-based prostate cancer data ob- tained from the KEMRI National Cancer Registry. Kaplan-Meier estimation method was used to compare survival function for clinical stage and treatment. Cox proportional haz- ards regression model was used to model age against survival time.
The Cox proportional hazards regression results checking for association between sur- vival time and age were as follows: HR = 1.032 (CI: 1.004 – 1.061). Thus for each additional year an individual diagnosed with prostate cancer is 3.2% more likely to die per unit time (in days). However, the Kaplan-Meier results showed no significant differences in survival for clinical stage and treatment type categories.
Table of Contents
Abstract ii
Declaration and Approval iv
Dedication vii
Figures and Tables x
Acknowledgments xi
Chapter 1: Introduction
1.1 Background 1
1.2 Problem Statement 1
1.3 Objectives 2
1.3.1 General Objective 2
1.3.2 Specific Objectives 2
1.4 Significance of the Study 2
Chapter 2: Literature Review
Chapter 3: Methodology
3.1 Data Source 6
3.2 Data Management and Analysis 6
3.3 Survival Analysis 6
3.3.1 Introduction 6
3.3.2 Probability Density Function, PDF 6
3.3.3 Cumulative Distribution Function, CDF 7
3.3.4 The Survival Probability Function, S(t) 7
3.3.5 The Hazard Function, h(t) 8
3.3.6 The Cumulative Hazard Function, H(t) 8
3.3.7 Kaplan-Meier Estimator 9
3.3.8 Tests of Hypothesis on Survival Function 10
3.3.9 Non-parametric Estimation of the Hazard Function 11
3.3.10 Cox Proportional Hazards Regression Model 11
Chapter 4: Data Analysis and Results
4.1 Descriptive Statistics 16
4.2 Comparing survival between clinical stages 22
4.3 Comparing Survival Between Patients Treated With Single Treatment Type and Those Treated With 2 or More Treatment Types 24
4.4 Effect of Age on Survival 26
4.4.1 Cox Proportional Hazards Model Diagnostics 26
4.4.2 Interpretation of the Results 29
Chapter 5: Conclusion & Recommendations
5.1 Conclusion 30
5.2 Recommendations 30
5.3 Future Research 30
References 32
Figures and Tables
Figure 4.2.1. Kaplan-Meier survival estimates curves by clinical stage 23
Figure 4.3.1. Kaplan-Meier survival estimates curves by treatment type category 24
Figure 4.4.1. Schoenfeld residuals against the time (in days) 27
Figure 4.4.2. d f beta residuals 27
Figure 4.4.3. Martingale residuals for testing for non-linearity 28
List of Tables
Table 4.1.1. Counts of participants by clinical stage at diagnosis 16
Table 4.1.2. Counts of participants by treatment type categories 16
Table 4.1.3. Summary statistics for age by clinical stage at diagnosis 17
Table 4.1.4. Summary statistics for age by treatment type categories 17
Table 4.1.5. Summary statistics for age by treatment type 18
Table 4.1.6. Summary of outcome status by clinical stage at diagnosis 19
Table 4.1.7. Summary of outcome status by treatment type category 19
Table 4.1.8. Summary of outcome status by specific treatment 20
Table 4.2.1. Kaplan-Meier estimates for survival function alongside the 95% CI for stage II and stage IV 22 Table 4.3.1. Kaplan-Meier estimates for survival function alongside the 95% CI for 1 treatment and
2+ treatments 25
Table 4.4.1. Results of the Cox proportional hazards regression model 26
Table 4.4.2. Testing for proportional hazards assumption 26
Chapter 1
Introduction
1.1 Background
Cancer is characterized by an uncontrollable cell division and growth in an organ or a specific part of the body that results in destruction of the body tissue. It emerges from alteration of normal body cells into tumor cells through a multistage process starting from pre-tumor to a malignant tumor. In some instances, this abnormal growth of cells shows a tendency to proliferate and metastasize (spread from original organ to other body parts). Cancers are o en named according the body part/organ where they first appear.
Prostate cancer is one of the most common types of cancer in men. It affects the prostate gland in men. The cause of the prostate cancer is unclear. However, previous research has indicated that there are risk factors that are associated with prostate cancer such as: old age, race where black people are at more risk, family history, obesity among other risk factors.
In the year 2018, the most diagnosed cancers across the world were lung cancer (2.09 million, 12.3%), breast cancer (2.09 million, 12.3%), colorectal cancer (1.8 million, 10.6%), and prostate cancer (1.3 million, 7.5%). Among men, prostate cancer was the second most diagnosed cancer (1.3 million, 14.5%) a er lung cancer (1.4 million, 15.5%).
Cancer is the third leading cause of deaths a er infectious diseases and cardiovascular diseases. Prostate cancer is one of the cancers with incidences that have been on the rise in most regions of the world. Even though prostate cancer cases remain high in developed countries, there are relatively more deaths in low- and middle-income countries as a result of late diagnosis and lack of treatment facilities.
For some undetermined reasons, prostate cancer has the highest incident rate among men of African descent. For instance, the number of prostate cancer cases diagnosed among men of African descent is close to 60% higher than Caucasian American men, with a mortality rate of between two and three times more.
1.2 Problem Statement
Worldwide, with exception of non-melanoma skin cancer, the most common cancer among males is lung cancer, accounting for about 15.5% of all cancer cases detected during 2018, followed by prostate cancer which accounted for about 14.5%. Cancer incidence burden has been predicted to rise to over 85% by 2030 thereby resulting to an increase in cancer- related deaths. Of all cancer cases documented in the Nairobi Cancer Registry between 2004 and 2008, prostate cancer was the most diagnosed type of cancer among males with age standardized incidence rate of 40.6 per every 100,000. As a result there were more deaths due to prostate cancer among males than any other cancer type. Statistical modelling of cancer survival have not been explored adequately by previous research in the Kenyan seNing. The present study will be an important source of information for the factors significantly associated with survival time among prostate cancer in Kenya.
1.3 Objectives
1.3.1 General Objective
The general objective of this study is to investigate factors associated with survival time among prostate cancer patients in Kenya.
1.3.2 Specific Objectives
The following are the specific objectives:
1. To compare survival among prostate cancer patients between clinical stages at diagnosis in Kenya.
2. To compare survival among prostate cancer patients treated with a single treatment and those treated with a combination of two or more treatment types in Kenya.
3. To find out the impact of age on survival among prostate cancer patients in Kenya.
1.4 Significance of the Study
This study intends to identify factors significantly associated with survival among prostate cancer patients for consumption by the Government of Kenya (GoK) and the general public. Through deeper understanding of these factors the government can put in place strategies and formulate policies directed towards thwarting prostate cancer mortality, for example, by seNing up cancer centers, public awareness, and so on
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