biostatistics
2019/ 10/ 11 Originality Report
riginalityReport/ultra?attemptId=f3de2bed-83f3-41cc-bfab-b713bd282ed9&course_id=_65931_1&includeDeleted=true&print=true&download=true… 1/3
%40
%9
%2
SafeAssign Originality Report (Current Semester - الفصل الحالي)HCM-506: Appli… • Turnitin Plagiarism Checker
%51Total Score: High risk MOHAMMED ALAHMARI
Submission UUID: 94ec1f8d-1409-a638-fc12-6917225b08f4
Total Number of Repo…
1 Highest Match
51 % 12222.docx
Average Match
51 % Submitted on
10/11/19 10:32 AM GMT+3
Average Word Count
389 Highest: 12222.docx
%51Attachment 1
Internet (2)
statsdirect e-ijd
Global database (2)
Student paper Student paper
Institutional database (1)
Student paper
Top sources (3)
Excluded sources (0)
View Originality Report - Old Design
Word Count: 389 12222.docx
1 5
2 3
4
1 statsdirect 2 Student paper 3 Student paper
2019/ 10/ 11 Originality Report
riginalityReport/ultra?attemptId=f3de2bed-83f3-41cc-bfab-b713bd282ed9&course_id=_65931_1&includeDeleted=true&print=true&download=true… 2/3
Source Matches (7)
statsdirect 93%
Student paper 95%
statsdirect 98%
Title: Chapter 12
Name: Date:
This Cox's proportional hazards model for survival-time (time-to-event) outcomes on one or more predictors. It is method for investigating the effect of several variables upon the time a specified event takes to happen. Cox regression for survival analysis. The method does not assume any particular "survival model" but it is not truly nonparametric because it does assume that the effects of the predictor variables upon survival are constant over time and are additive in one scale. Provided that the assumptions of Cox regression are met, this function will provide better estimates of survival probabilities and cumulative hazard than those provided by the Kaplan-Meier function. It is frequently used in the survival analysis with time to event, it is the one of the part of the survival analysis. Exponentiation of the beta-values for a Cox proportional hazards model is useful for generating odds ratios. It is important to ensure that adjustment for potential confounders in a Cox proportional hazards model includes variables that are in the causal pathway between the independent (exposure) variable of interest and the outcome. Let conduct Cox's proportional hazards model to check the following hypothesis. Hypothesis: H0: The risk of dying is not related to the patient treatment group. (Null Hypothesis) H1: The risk of dying is related to the patient treatment group. (Alternative Hypothesis)
From the output of the test p value (0.2742) is less than 0.05, fail to reject the null hypothesis and concluded that the risk of dying is not related to the patient treatment group. Additionally we could not found any association between risk of dying and the patient treatment group.
References Hazra, A., & Gogtay, N. (2016). Biostatistics series module 6: Correlation and linear regression. Indian Journal of Dermatology, 61(6), 593-601. Retrieved from
https://doaj.org/article/7e6f5ec7ddaa4ecbad979d16 f3e59a79 ● Navratil, R., & Ehsanes Saleh, A. K., Md. (2016). Aligned rank tests in measurement error model. Applications of Mathematics, 61(1), 47-59.
Serial Time (years) 1 2 3 4 4.5 5 0.5 0.75 1 1.5 2 3.5 Satus At Serial Time (1=event; 0=censored) 1 1 1 1 1 0 1 1 1 0 1 1 Group (1 Chemo or 2 Placebo) 1 1 1 1 1 1 2 2 2 2 2 2
1 2 1
3
4 5 3
1
Student paper
This Cox's proportional hazards model for survival- time (time-to-event) outcomes on one or more predictors.
Original source
This function fits Cox's proportional hazards model for survival-time (time-to-event) outcomes on one or more predictors
2
Student paper
It is method for investigating the effect of several variables upon the time a specified event takes to happen.
Original source
A method for investigating the effect of several variables upon the time a specified event takes to happen
1
Student paper
Cox regression for survival analysis. The method does not assume any particular "survival model" but it is not truly nonparametric because it does assume that the effects of the predictor variables upon survival are constant over time and are additive in one scale. Provided that the assumptions of Cox regression are met, this function will provide better estimates of survival probabilities and cumulative hazard than those provided by the Kaplan-Meier function.
Original source
Analysis_Survival_Cox Regression The method does not assume any particular "survival model" but it is not truly nonparametric because it does assume that the effects of the predictor variables upon survival are constant over time and are additive in one scale Provided that the assumptions of Cox regression are met, this function will provide better estimates of survival probabilities and cumulative hazard than those provided by the Kaplan-Meier function
2019/ 10/ 11 Originality Report
riginalityReport/ultra?attemptId=f3de2bed-83f3-41cc-bfab-b713bd282ed9&course_id=_65931_1&includeDeleted=true&print=true&download=true… 3/3
Student paper 77%
Student paper 100%
e-ijd 71%
Student paper 79%
3
Student paper
(Null Hypothesis) H1:
Original source
The null hypothesis,
4
Student paper
References Hazra, A., & Gogtay, N.
Original source
References Hazra, A., & Gogtay, N
5
Student paper
Biostatistics series module 6:
Original source
Biostatistics series module 9
3
Student paper
Correlation and linear regression.
Original source
linear correlation, and