biostatistics

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Module 13

Name: Date:

Kaplan-Meier plots: The goal is to estimate a population survival curve from a sample. i) If every patient is followed until death, the curve may be estimated simply by computing the fraction surviving at each time. ii) However, in most studies patients tend to drop out, become lost to follow-up, move away, etc. iii) A Kaplan-Meier analysis allows estimation of survival over time, even when points drop out or are studied for different lengths of time.

H0 The survival time is not related to the patient treatment group. (Null Hypothesis) H1 The survival time is related to the patient treatment group. (Alternative Hypothesis)

Baseline Survivor Function (at predictor means)... 0.0000 0.8465 1.0000 0.3114

Interpretation of the curve: Vertical axis represents estimated probability of survival for a hypothetical cohort, not actual % surviving. • Precision of estimates depends on # observations; therefore, estimates at left-hand side are more precise than at right-hand side (because of small #’s due to deaths and dropouts). • Curves may give the impression that a given event occurs more frequently early than late, because of high survival rate and large # people at beginning

Yes, Kaplan-Meier survival curves prognostic indicator of UCEC because it can be generated by selecting the cancer type, survival type and protein(s) of interest. This technique may also be used for breast, colon and other cancers because its a statistical technique which gives proper result of survival data of the patients undergone cancer or other treatment.

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

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Student paper

Kaplan-Meier plots: The goal is to estimate a population survival curve from a sample.

Original source

-A Kaplan-Meier The goal is to estimate a population survival curve from a sample

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Student paper

i) If every patient is followed until death, the curve may be estimated simply by computing the fraction surviving at each time.

Original source

If every patient is followed until death, the curve may be estimated simply by computing the fraction surviving at each time

1

Student paper

ii) However, in most studies patients tend to drop out, become lost to follow-up, move away, etc.

Original source

However, in most studies patients tend to drop out, become lost to follow-up, move away, etc

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Student paper

iii) A Kaplan-Meier analysis allows estimation of survival over time, even when points drop out or are studied for different lengths of time.

Original source

A Kaplan-Meier analysis allows estimation of survival over time, even when patients drop out or are studied for different lengths of time

2019/ 10/ 11 Originality Report

iginalityReport/ultra?attemptId=929ba456-7401-4cb2-8818-df87061d712f&course_id=_65931_1&includeDeleted=true&print=true&download=true… 3/3

Student paper 82%

Student paper 78%

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Student paper

Vertical axis represents estimated probability of survival for a hypothetical cohort, not actual % surviving.

Original source

Vertical axis represents the estimated probability of survival and not the actual % surviving for a hypothetical cohort Precision of estimates depends on # observations

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Student paper

• Curves may give the impression that a given event occurs more frequently early than late, because of high survival rate and large # people at beginning

Original source

therefore, estimates at left- hand side are more precise than at right-hand side Curves may give the impression that a given event occurs more frequently early than late, because of high survival rate and large # people at beginning