SAE Institute Clinical Trial Excel Data Analysis Worksheet

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Using the Clinical Trial on breast cancer dataset. Perform a Kaplan-Meier Analysis to determine the survival curve for the breast cancer survivors. H0 The risk of dying from breast cancer will occur within five years. (Null Hypothesis) H1 The risk of dying from breast cancer does not occur within five years. (Alternative Hypothesis) Ensure to submit the following requirements for the assignment: • Review the analysis from the standpoint of how many patients survive over the sevenyear time period that the clinical trial covered. • Present your findings as a Survival Time chart in a Word document, with a title page, introduction explaining why you would conduct a survival analysis, a discussion where you interpret the meaning of the survival analysis, and a conclusion. • Your submission should be 3-4 pages to discuss and display your findings. • Provide support for your statements with in-text citations from a minimum of three scholarly, peer-reviewed articles. One of these sources may be from the class readings, textbook, or lectures, but the others must be external. The Saudi Digital Library is a good place to find these sources and should be your primary resource for conducting research. • Follow APA 7th edition and Saudi Electronic University writing standards. Review the grading rubric to see how you will be graded for this assignment. You are strongly encouraged to submit all assignments to the Turnitin Originality Check prior to submitting them to your instructor for grading. Module 12 Chapter 11 Introduction to Survival Analysis Learning Objectives • Identify applications with time-to-event outcomes • Construct a life table using the actuarial approach • Determine the assumptions of survival analysis • Interpret the Cox proportional hazards regression • Interpret a hazard ratio Survival Analysis • Outcome is time to event. – Time to heart attack, cancer remission, death • Measure whether person has event or not. – (Yes/No) and time to event • Estimate “survival time.” • Determine factors associated with longer survival. Issues with Time to Event Data • Times are positive (often skewed). • Incomplete follow-up information – Some participants enroll late. – Some participants drop out. – Study ends. • Censoring – Measure follow-up time and not time to event. – We know survival time > follow-up time. Experiences of n = 10 Participants Experiences of Same n = 10 Participants, Time Projected to Zero Is the Following Different? Survival Curve – Survival Function Survival Curve with 95% CI 1.0 0.9 0.8 Survival Probability 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 5 10 15 Time, Years 20 25 Estimating the Survival Function • There are many parametric approaches (which make certain assumptions about survival times). • We focus on two nonparametric approaches. – Actuarial or life-table approach – Kaplan–Meier approach Example 11.2. Estimating the Survival Function (1 of 2) • Participants are 65 years and older, followed for up to 24 years until they die, the study ends, or they drop out. • n = 20 participants are enrolled over a 5-year period. Example 11.2. Estimating the Survival Function (2 of 2) • Year of death or year of last contact – Years of death: 3, 14, 1, 23, 5, 17 – Years of last contact: 24, 11, 19, 24, 13, 2, 18, 17, 24, 21, 12, 10, 6, 9 Notation Nt = number of participants who are eventfree and considered at risk during interval Dt = number who suffer event during interval Ct = number censored during interval qt = proportion suffering event during interval pt = proportion surviving interval St = proportion surviving past interval Example 11.2. Life Table Example 11.2. Life Table—Actuarial Approach Example 11.2. Life Table—Kaplan– Meier Approach Example 11.2. Survival Function Comparing Survival Curves • Log-rank test to compare survival in two or more independent groups. • Chi-square test that compares the observed numbers of events to what would be expected if the groups had equal survival Example 11.3. Comparing Survival • Clinical trial to compare two treatments for advanced gastric cancer • n = 20 participants with stage IV cancer are randomly assigned to receive chemotherapy before surgery or chemotherapy after surgery. • Primary outcome is death. • Participants are followed for up to 48 months following enrollment. RCT to Compare Two Treatments for Advanced Gastric Cancer Module 13 Chapter 11 The Survival Curve Learning Objectives • Evaluate the data for survival analysis if it is normally distributed • Evaluate the statistical tests used in survival analysis • Perform and interpret the log-rank test • Evaluate the concept of censored survival data Log-Rank Test H0: Two survival curves are identical H1: Two survival curves are not identical Test statistic: χ = 2  (O jt − E jt ) 2 E jt Reject H0 if c2 > c2,df where df = k – 1 and k = number of comparison groups. RCT to Compare Two Treatments for Advanced Gastric Cancer Example 11.3. Log-Rank Test (1 of 2) H0: Two survival curves are identical H1: Two survival curves are not identical Test statistic: χ2 =  (O jt − E jt ) 2 E jt (6 − 2.620) 2 (3 − 6.380) 2 = + = 6.151 2.620 6.380 Example 11.3. Log-Rank Test (2 of 2) • Reject H0 if c2 ≥ 3.84. • Reject H0 because 6.151 > 3.84. We have statistical evidence that two survival curves are not identical. Comparing Survival Curves H0: Two survival curves are equal c2 Test with df=1. Reject H0 if c2 > 3.84 c2 = 6.151. Reject H0. Cox Proportional Hazards Regression (1 of 2) • Model h(t) = h0(t) exp (b1X1 + b2X2 + … + bpXp) • Where h(t) = hazard at time t (risk of failure at time t), h0(t) = baseline hazard, Xi are predictors, bi are regression coefficients. Cox Proportional Hazards Regression (2 of 2) • Model ln(h(t)/h0(t)) = b1X1 + b2X2 + … + bpXp • exp(bi) = hazard ratios Example 11.5. Cox Proportional Hazards Regression (1 of 3) • Framingham Study – – – – Outcome = all-cause mortality N = 5180 participants ≥ 45 years 10-year follow-up Analysis with Cox proportional hazards regression Example 11.5. Cox Proportional Hazards Regression (2 of 3) Age Male sex bi p 0.11149 0.0001 0.67958 0.0001 HR 1.118 1.973 Example 11.5. Cox Proportional Hazards Regression (3 of 3) • Multivariable model bi p HR (95% CI) Age 0.11691 0.0001 1.12 (1.11 – 1.14) Male sex 0.40359 0.0001 1.50 (1.22 – 1.85) SBP 0.11691 0.0001 1.02 (1.01 – 1.02) Current smoker 0.40359 0.0001 2.16 (1.76 – 2.64) Total chol. 0.40359 0.0001 1.00 (0.99 – 1.00) Diabetes 0.40359 0.0001 0.82 (0.62 – 1.08) SUBJECTID age Alive 1118 39.23 1230 53.45 1056 35.37 1146 50.15 1057 44.24 1102 57.46 1172 44.59 1049 50.43 1168 44.59 1199 37.68 1206 43.03 1136 60.03 1174 49.1 1045 49.58 1034 46.57 1129 52.45 1054 42.76 1061 54.19 1050 63.17 1234 38.1 1156 65.39 1043 38.88 1169 44.15 1194 33.83 1236 38.61 1062 34.61 1101 42.84 1209 43.44 1196 35.44 1142 28.76 1235 64.06 1237 46.46 1224 61.13 1197 44.73 1233 27.85 1225 47.84 1229 50.79 1096 42.99 1198 59.54 1222 55.52 1002 37.79 1239 48.57 1211 48.11 1106 50.41 1154 65.45 1161 56.83 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 Survival_length 0.410284 0.512329 0.575342 0.775342 0.860274 0.868493 0.893151 0.942466 0.945205 1.30411 1.317808 1.345205 1.353425 1.468493 1.520548 1.676712 1.684932 1.80274 1.816438 1.90137 1.928767 2.041096 2.071233 2.219178 2.279452 2.380822 2.586301 2.657534 2.739726 2.769863 2.810959 2.824658 2.871233 2.876712 2.947945 3.054795 3.060274 3.082192 3.084932 3.145205 3.16 3.164384 3.175342 3.221918 3.260274 3.260274 1170 1232 1166 1220 1191 1228 1210 1144 1001 1226 1065 1163 1221 1152 1180 1216 1223 1204 1189 1188 1185 1200 1138 1165 1214 1175 1109 1181 1207 1130 1218 1091 1098 1123 1107 1193 1116 1113 1202 1187 1179 1183 1114 1162 1122 1089 1121 44.35 49.97 54.43 43.12 45.88 53.25 40.58 56.28 38.73 42.08 55.22 38.85 52.54 51.29 31.45 34.43 59.82 39.27 68.82 38.26 49.79 43.79 35.13 39.66 47.02 64.33 52.53 34.71 36.27 39.08 47.82 54.64 62.29 61.86 58.37 59.6 34.57 53.49 53.45 47.06 49.61 53.78 44.82 56.44 59.7 60.95 41.51 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3.260274 3.279452 3.353425 3.361644 3.391781 3.427397 3.441096 3.454795 3.46 3.484932 3.487671 3.506849 3.506849 3.526027 3.542466 3.567123 3.569863 3.580822 3.6 3.610959 3.621918 3.630137 3.641096 3.643836 3.657534 3.682192 3.69589 3.717808 3.717808 3.767123 3.778082 3.791781 3.821918 3.876712 3.890411 3.90411 3.909589 3.923288 3.947945 3.983562 3.989041 3.989041 4.016438 4.027397 4.057534 4.068493 4.10411 1201 1112 1177 1157 1149 1151 1083 1171 1124 1147 1158 1070 1135 1134 1125 1155 1150 1060 1164 1086 1090 1148 1110 1058 1141 1066 1041 1139 1128 1137 1132 1071 1073 1055 1084 1099 1092 1097 1047 1095 1115 1037 1111 1082 1117 1038 1063 50.12 46.06 40.79 41.39 59.18 50.15 51.89 50.98 47.6 59.3 61.79 36.78 58.89 43.06 44.85 42.65 51.49 67.18 60.11 41.51 51.54 50.81 56.94 54.72 49.15 57.42 46.64 46.26 33.18 29.38 51.42 38.53 49.24 52.82 34.53 50.35 44.13 44.5 26.69 43.57 51.84 51.14 50.59 46.58 49.01 37.81 52.17 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4.128767 4.136986 4.158904 4.167123 4.20274 4.230137 4.273973 4.284932 4.293151 4.29589 4.331507 4.372603 4.372603 4.380822 4.424658 4.424658 4.430137 4.443836 4.449315 4.50137 4.509589 4.509589 4.526027 4.534247 4.553425 4.575342 4.6 4.6 4.613699 4.679452 4.709589 4.712329 4.726027 4.761644 4.838356 4.846575 4.887671 4.890411 4.928767 4.936986 4.936986 4.947945 4.953425 4.964384 5.024658 5.049315 5.10137 1103 1075 1085 1100 1048 1022 1077 1042 1078 1033 1069 1088 1074 1029 1039 1015 1021 1010 1026 1019 1030 1008 1011 1009 1016 1013 1003 1012 50.15 33.47 43.93 63.32 52.32 51.41 41.65 58.07 31.33 59.05 57.18 53.05 36.51 63.63 30.99 42.16 48.43 41.53 44.41 52.24 52.46 64.51 40.82 40.66 35.52 49.36 49.83 53.78 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5.167123 5.180822 5.191781 5.221918 5.224658 5.271233 5.276712 5.282192 5.312329 5.320548 5.334247 5.389041 5.427397 5.542466 5.561644 5.586301 5.641096 5.89 5.912329 6.027397 6.052055 6.41 6.41 6.45 6.463014 6.528767 6.54 6.643836 Saudi Electronic University College of Health Science Master of Health Administration HCM-506: Applied Biostatistics in Health Module 12: Critical Thinking Dr. Ermias Weldemicael Mohammed Sarhan Student ID: G210015171 April 23, 2022 1 Introduction In this paper, I will be discussing the survival rate of breast cancer patients from the time of diagnosis. Cancer has been increasing in modern times due to most people’s lifestyles, exposure to harmful contaminated environments, and even gene inheritance from a person’s lineage that is prone to cause cancers. (Mr et al., 2015) A good understanding of how cancer affects the body and its ability to render a patient unresponsive is vital; thus, a study of how long a patient can survive is crucial to give clarity to the patient while helping doctors and researchers obtain information that can be used to develop cures. (Mr et al., 2015) Additionally, the insight will help develop stages for cancer and determine the type of medical and physiological care the patient needs at the different stages to ensure they are leading a quality life even with the diagnosis. The data to be used in the study was obtained from a Clinical Trial on breast cancer patients for a new drug. (Chen et al., 2021) Clinical trials are essential to determine the effects of a new drug on the patients they are introduced to. The clinical trials use a sample of population which is then divided into different groups of two. One group is given a placebo drug (pretend drug) while the other is given the intended drug. The sample population is chosen randomly to ensure that the results reflect the entire population to the drug that will be presented in the marketplace. (Chen et al., 2021) Therefore, we can consider the data we are using as fairly normalized due to its random nature of sample choice. Thus insights into the data will be reflective on a larger population from which the sample was obtained. The dataset contains one hundred and sixty-eight patient records subdivided into columns of subject id, their age, the condition of if they are alive or not (1 denoting alive and 0 denoting dead), and the survival duration in years. (Chen et al., 2021) 2 To perform the survival curve analysis, I will be using the Kaplan-Meier estimate. For calculating the proportion of participants who survive for a certain duration after treatment, it is one of the best approaches. During clinical studies or public trials, the effectiveness of a treatment is evaluated over time by measuring the number of people who survived or were saved as a result of the intervention. (Chen et al., 2021) There are two definitions for survival time: the period between a certain moment in time to the occurrence of a particular event, such as death, and the examination of group data sets. These results may be skewed if participants are recalcitrant and refuse to participate, if individuals do not have an incident or die before the conclusion of the research, regardless of the fact that they’ve already had an event or died if observation had proceeded, or if researchers lose touch with them midpoint through the research. (Chen et al., 2021) Moreover, these are referred to as censored observations. The Kaplan-Meier estimation is the most straightforward method for calculating long-term survival notwithstanding all the complications associated with individuals or circumstances. (Kishore et al., 2010) In a variety of situations, the survival curve could well be constructed. It involves evaluating the probability of an event happening at a certain period and multiply these probabilities according to any previously calculated probability to obtain a final estimation. If there is a statistically significant difference in survival rates among two groups of individuals, it may also be computed. This can be employed in Ayurveda research when comparing two medications and determining subject survival. (Kishore et al., 2010) Analysis To build a survival curve, we have to rearrange the data set into a usable format and then use the inbuilt excel chart function to plot the curve. As an initial step, we need to identify all of the 3 distinct values in the time columns. In this case, we use the survival length column after the duplicate values have been extracted from the column. The new time column is copied to a different column and, using the countif function, find whether the patient is dead or alive from the original dataset. This counts the number of patients whose time in the clinical data is more than 0. The resultant value gives the number of alive patients through each period of time. In the next step, we calculate the reciprocal of the division between the dead and alive patients. The value obtained can now be used to generate a function of time against the start of the patient, either dead or alive. The function of t (time) is then given by the product of the patient’s initial state, which is alive, and the reciprocal value of the division of dead and alive variables. Fig 1: Sample values 4 Discussion S(t) 3.5E+177 3E+177 2.5E+177 2E+177 1.5E+177 1E+177 5E+176 0 0 1 2 3 4 5 6 7 Fig 2: survival analysis plot of the breast cancer patients. From the plot, we can visualize the displacement of the patient data across the time period they are under treatment. For example, we can see that most of the patients have been in the trial for less than 6 years, with most of those patients being in the trial between the periods of 3 to 6 years, inferencing that most of the patients in the trial have been in the trial for a longer time. The survival curve shows that as the number of years increases that the patient in on the clinical trial, their survival rate increases. These can be seen from the survival analysis plot. As soon as the patient on the clinical trial exceeds 6 years in the trial, their rate of survival increases significantly. Therefore, we can deduce from the analysis that the drug’s effectiveness in the clinical trial can be seen after a patient has used the drug consistently for more than 6 years. (Hung et al., 2018) 5 Conclusion Running such tests on the clinical datasets plays an important role in what to expect when administering clinical trials drugs, especially for diseases such as cancer. The benefits of running tests are known to the medical community to inform their decision when developing the different medicine to combat the same problem. (Mr et al., 2015) References Chen, Z., Zhang, H., Guo, Y., George, T. J., Prosperi, M., Hogan, W. R., He, Z., Shenkman, E. A., Wang, F., & Bian, J. (2021). Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer’s disease. Npj Digital Medicine, 4(1). https://doi.org/10.1038/S41746-021-00452-1 Hung, M., Bounsanga, J., Voss, M. W., & Saltzman, C. L. (2018). Establishing minimum clinically important difference values for the Patient-Reported Outcomes Measurement Information System Physical Function, hip disability and osteoarthritis outcome score for joint reconstruction, and knee injury and osteoarthritis ou. World Journal of Orthopedics, 9(3), 41–49. https://doi.org/10.5312/wjo.v9.i3.41 Kishore, J., Goel, M., & Khanna, P. (2010). Understanding survival analysis: Kaplan-Meier estimate. International Journal of Ayurveda Research, 1(4), 274. https://doi.org/10.4103/0974-7788.76794 Mr, A., Sharifi J, Mr, P., & Paknahad A. (2015). Breast cancer and associated factors: a review. Journal of Medicine and Life, 8(4), 6–11. https://pubmed.ncbi.nlm.nih.gov/28316699/%0A28316699

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