This document analyzes breast cancer survival rates using survival analysis. It provides background on breast cancer being a leading cause of death for women due to abnormal breast cell growth. Survival analysis is used to determine how treatment accuracy affects survival time. The study analyzed 15 breast cancer patients diagnosed from 1989-1995, finding 60% at stage 2, 40% alive at last contact, and 53% dying of breast cancer. The key challenge in survival analysis is censoring, which occurs when the exact survival time is unknown. There are two types - left censoring when the actual time is less than observed, and right censoring when the actual time is at least as long as observed. Examples given are unknown start of breast cancer infection and deaths not

1. SURVIV
AL
ANALYS
IS
for breast cancer
By:
ANITA ROKHMAWATI
DEDY SUSANTO
LUTHFAN EKA PUTRA
MUHAMMAD HANIF NAUFAL
RICCI RAKHMAN PAMUJI
TITIS SETYA WULANDARI

2. BACKGROUND
Why breast cancer?
leading cause of
women’s death
abnormal growth of
breast cell

3. BACKGROUND
Why use survival analysis?
the accuracy of
treatment affects
survival time
survival of breast
cancer patients

4. 1989 1990 1991 1992 1993 1994 1995
1 --------- A
2 ------------ ------------ ------------ ------------ D(BC)
3 --- ------------ ------------ ------------ ------------ ------------ --------A
4 ---------- D(BC)
5 ---- ------------ ------------ -D(BC)
6 -- ------------ ---------- D(BC)
7 ----------- ------------ ------------ ----------A
8 - ------------ ------------ ------------ ------------ ----- D(O)
9 ------------ ------------ ------------ D(BC)
10 ----- D(BC)
11 ------- ------------ ------------ ------A
12 -------- ------------ ------------ ----- A
13 ----------- --- D(BC)
14 ---------- ------------ -------- A
15 --- ---------- D(BC)
CalendarYearsofDiagnosis
Study
No.

6. Stage of Cancer
Stage 2
60% (9)
Stage 1
40% (6)

7. Vital Status at Last Contact
Death
60% (9)
Alive
40% (6)

8. Cause of Death
• Breast Cancer
• Not Death
• Cause Other
53% (8)
40% (6)
7% (1)

9. Censor
Most Survival data must
consider a key analytical
problem called censoring
Censoring occurs when we
have some information about
individual survival time, but we
don’t know the survival time
exactly.
1
2
Example:
Breast cancer patient in omnission

10. Censor
A person does not experience the
event before the study ends
A person withdraws from the study
because of death ( if death is not
the event of interest) or some other
reason (e.g., adverse drug reaction
or other competing risk)
1
3
A person is lost to follow-up during
the study period
2Why
censoring
may occur

11. Censor
Left censoring occurs when
the actual survival time is less
than what is observed by the
investigator.
Right censoring occurs when
the actual survival time is at
least as long as the time
observed by the investigator.
Based on the time occurence of events,
censor are divided into two types:
1 2
Left censoring Right censoring

12. Censor
• A person’s exact start
time when infected by
breast cancer is
unknown. (patients
diagnosed at that time
might have had existing
breast cancer that had
not been previously
recognized).
• Death not caused
by breast cancer
(47%)
• Unknown exact
survival time after
observation (40%)
Left censoring Right censoring

13. Thank you