CANCER MANAGEMENT AND TREATMENT 2 Cancer Management and Treatment Introduction Cancer refers to a group of diseases that are characterized by the rapid development of abnormal cells in the body. The onset of cancerous cells is generally attributed to mutation in a cell's DNA. Cancerous cells have the ability to infect health cells thus allowing the disease to spread from one organ to the next. The two most commonly used methods of cancer treatment are chemotherapy and radiotherapy(Baskar, Lee, Yeo, & Yeoh1, 2012). A patient is recommended to undergo both treatment exercises in order tostand a greater chance of recovery. Hypothesis Since the assessment will be dealing with three groups of patients who are undergoing cancer treatment, a one-way ANOVA based hypothesis analysis will be employed for the assessment of variance in the means or lack thereof. This will be done for both Objective 1 and 2. Objective 1 H0: The mean recovery probability is the same for individuals undergoing chemotherapy, radiotherapy, or a combination of chemotherapy and radiotherapy i.e.u1=u2=u3 H1: The mean recovery probability is different for at least one of the three treatment groups (chemotherapy, radiotherapy, or a combination of chemotherapy and radiotherapy) i.e. u1≠u2≠u3 Objective 2
CANCER MANAGEMENT AND TREATMENT 3 H0: The mean percentage spread of cancer is the same for individuals undergoing chemotherapy, radiotherapy, or a combination of chemotherapy and radiotherapy i.e.u1=u2=u3 H1: The mean percentage spread of cancer is different for at least one of the three treatment groups (chemotherapy, radiotherapy, or a combination of chemotherapy and radiotherapy) i.e. u1≠u2≠u3 Definition of Variables Objective 1 There are three variables are independent and they are numerical in value. The variables are treat group undergoing chemotherapy, radiotherapy, and a combination of both radiotherapy and chemotherapy. The variables will assume percentage values between 0% and 100%. Where a probability of 0% will indicate the patient has shown no response to the treatment and his/her condition has significantly deteriorated. While a percentage of 100% will indicate the patient is responding well to the treatment and the cancer has disappeared. Objective 2 There are three independent variables that assume numerical in value. The variables are treat group undergoing chemotherapy, radiotherapy, and a combination of both radiotherapy and chemotherapy. The variables will assume percentage values between -100% and 100%. A probability of-100% will indicate that the cancer has actually decreased (instead of spreading) to the point that the patient is considered cancer free. While a percentage of 100% will indicate that the cancer has spread to all the vital organs of the body. Statistical Analysis
CANCER MANAGEMENT AND TREATMENT 4 For both Objective 1 and 2, a two tailed ANOVA analysis will be performed. A two-tailed test will be employed because the alternative hypotheses are based on the lack of equivalence in the means. The test statistic will be an F-test. The main assumption under the F-test is that all three treatment groups have equal variability(Gaston, 2014).The ANOVA analysis will be done with the help of statistical analysis software like Excel. Sample Size Using G*Power 3.1.9.2 we are able to compute an appropriate sample size for the one-way ANOVA analysis. The parameters for the sample size tabulation are as follows: alpha 0.05, power 0.95, effect size 0.25, and the number of groups is 3. The tabulation results and graph are presented below:
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CANCER MANAGEMENT AND TREATMENT 5 As such, the entire sample size for both objective one and two will have two hundred and fifty two individuals. A large sample size enhances the applicability and usability of the research interpretations in other medical studies. Moreover, a large sample ensures that the results are considerably reliable and accurate.
CANCER MANAGEMENT AND TREATMENT 6 References Baskar, R., Lee, K. A., Yeo, R., & Yeoh1, K.-W. (2012). Cancer and Radiation Therapy: Current Advances and Future Directions.International Journal of Medical Sciences, 193-199. Gaston, L. (2014).Hypothesis Testing Made Simple.Leonard Gaston Ph.D.