Lecture 8: Life Tables
Transcript of Lecture 8: Life Tables
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Copyright 2008, The Johns Hopkins University Marie Diener-West, and Sukon Kanchanaraksa. All rights reserved. Use of these materials permitted only in accordance with license rights granted. Materials provided “AS IS”; no representations or warranties provided. User assumes all responsibility for use, and all liability related thereto, and must independently review all materials for accuracy and efficacy. May contain materials owned by others. User is responsible for obtaining permissions for use from third parties as needed.
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Life Tables
Marie Diener-West, PhDSukon Kanchanaraksa, PhD
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Section A
Clinical Life Tables, Part 1
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Clinical Life Table
A fundamental technique of survival analysis that deals with “time to event”−
A basic example is “time to death”
It can answer the question of the chance of survival after being diagnosed with the disease or after beginning the treatmentThe event can be any other health event—not just death−
It can be relapse, receiving organ transplant, pregnancy (in a study of infertility), failure of treatment, recovery, etc.
It handles variable time of entry and (variable time of) withdrawal of individuals from the populationIt calculates cumulative event-free probabilities and generates a survival curve
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Time since beginning of
follow-up (Year)
Number at beginning
Deaths
1 200 20
2 30
3 40
Example
A group of 200 subjects were followed for three yearsDeaths (events) occurred throughout the three yearsWhat is the chance of surviving at the end of the three years?
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Following a Population
Pati
ent
Begin follow-up Death
1995 2000Year
Year since follow-up1 2 30
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Clinical Life Table Notation
= number alive at the beginning of time t= number of deaths during the time interval
lt
dt
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Apply Notation
Apply notation to the table in the example
Time since beginning of
follow-up (Year)
Number at beginning
lt
Deaths
dt
1 200 20
2 30
3 40
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Fill in the “Number at Beginning”
Column
Fill in the missing cells
Time since beginning of
follow-up (Year)
Number at beginning
lt
Deaths
dt
1 200 20
2 30
3 40
200–20 = 180200–20 = 180
180
180–30 = 150180–30 = 150
150
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lt
dt
Clinical Life Table Notation
= number alive at the beginning of time t= number of deaths during the time interval
= probability of dying during the time interval = probability of surviving in the time interval
qt = dt /lt
pt =1−qt
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Interval lt dt qt pt
1 200 20
2 180 30
3 150 40
20/200 = 0.120/200 = 0.1
0.1
1.0–0.1 = 0.91.0–0.1 = 0.9
0.9
30/180 = 0.1730/180 = 0.17
0.17
1.0–0.17 = 0.831.0–0.17 = 0.83
0.83
Calculate Probabilities of Dying (q) and Surviving (p)
0.27 0.73
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lt
dt
Clinical Life Table Notation
= number alive at the beginning of time t= number of deaths during the time interval
= probability of dying during the time interval = probability of surviving in the time interval
= cumulative probability of surviving at the beginning ofthe time interval
= cumulative probability of surviving at the end of theprevious interval
−
At the beginning of the study (zero time), P(1) = 1.0 −
P(t+1)
=
qt = dt /lt
pt =1−qt
Pt
pt ∗Pt
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lt
dt
Clinical Life Table Notation
= number alive at the beginning of time t= number of deaths during the time interval
= probability of dying during the time interval = probability of surviving in the time interval
= cumulative probability of surviving at the beginning ofthe time interval
= cumulative probability of surviving at the end of theprevious interval
−
At the beginning of the study (zero time), P(1) = 1.0 −
P(t+1)
=
−
For example:
qt = dt /lt
pt =1−qt
Pt
pt ∗Pt
P1 =1.0
P2 = p1∗P1
P3 = p2 ∗P2
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Calculate the Cumulative Probabilities of Surviving (P)
P1 =1.0
P2 = p1∗P1
P3 = p2 ∗P2
P4 = p3 ∗P3
1.0
0.747
0.83 x 0.9 = 0.7470.83 x 0.9 = 0.747
0.9 x 1.0 = 0.90.9 x 1.0 = 0.9
0.9
Interval lt dt qt pt Pt
1 200 20 0.1 0.9
2 180 30 0.17 0.83
3 150 40 0.27 0.73
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Calculate the Cumulative Probabilities of Surviving (P)
P1 =1.0
P2 = p1∗P1
P3 = p2 ∗P2
P4 = p3 ∗P3
1.0
0.747
0.83 x 0.9 = 0.7470.83 x 0.9 = 0.747
0.9 x 1.0 = 0.90.9 x 1.0 = 0.9
0.9
0.73 x 0.747 = 0.5450.73 x 0.747 = 0.545 0.545
Interval lt dt qt pt Pt
1 200 20 0.1 0.9
2 180 30 0.17 0.83
3 150 40 0.27 0.73
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Interval Pt
1 1.000
2 0.900
3 0.747
0.545
Quick Check
What is the (cumulative) probability of surviving at the beginning? (time 0)What is the cumulative probability of surviving to the beginning of the second year?What is the cumulative probability of surviving at the end of the first year? What is the cumulative probability of surviving to the beginning of year 3?What is the cumulative probability of surviving to the beginning of year 4 or end of year 3?
= 1.0
= 0.9
= 0.9
= 0.747
= 0.545
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Section B
Clinical Life Tables: Part 2
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Another View of Cumulative Probabilities (P)
Interval lt dt qt pt pt
1 200 20 0.1 0.9 1.000
2 180 30 0.17 0.83 0.900
3 150 40 0.27 0.73 0.747
0.545
1.0
0.90.9
0.747
= cumulative probability of surviving at the beginning of the time interval Pt
0.7470.545
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Censoring
Observations are considered to be censored if:−
Individuals withdraw
from the study or are lost to
follow-up−
Individuals are not followed long enough
to experience
the event of interest−
Individuals experience an event which precludes the event of interest
Those who are censored during an interval are assumed to have been followed, on average, for half the interval
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wt
′ l = lt − wt /2
qt = dt / ′ l t
Clinical Life Table Notation
= number withdrew (“censored”) during the interval= adjusted number at risk of the event in the
interval
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Life Table with Censored Observations
Interval lt dt wt l't qt pt Pt
1 200 20
2 ? 30
3 ? 40
130
60
50
40
20
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Life Table with Censored Observations
Interval lt dt wt l't qt pt Pt
1 200 20 ?
2 ? 30 ?
3 ? 40 ?
175
50
110 0.273 0.727130
60
50
40
20
200–(50/2) = 200–25 = 175
0.8860.114
20/175 = 0.114
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Life Table with Censored Observations
Interval lt dt wt l't qt pt Pt
1 200 20 ?
2 ? 30 ?
3 ? 40 ?
175
50
110
0.114 0.886
0.273 0.727
0.800 0.200
0.886
0.644
0.129
130
60
1.00050
40
20
200–(50/2) = 200–25 = 17520/175 = 0.114
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Clinical Life Table: Assumptions
1.
There are no changes in survivorship over calendar time
2.
The experience of individuals who are lost to follow-up is the same as the experience of those who are followed
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Clinical Life Table: Assumptions
1.
There are no changes in survivorship over calendar time
2.
The experience of individuals who are lost to follow-up is the same as the experience of those who are followed
3.
Withdrawal occurs uniformly within the interval
4.
Event occurs uniformly within the interval
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Example 1: Clinical Life Table—No Withdrawal/No Loss
21 patients with leukemia were followed after treatment over time Time from treatment to relapse was observed for all patientsThe remission times (months) were:6, 6, 6, 6, 7, 8, 10, 10, 11, 13, 16, 17, 19, 20, 22, 23, 25, 32, 32, 34,35
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Example 1: Clinical Life Table—No Withdrawal/No Loss
Time lt dt qt = dt/lt pt Pt
0–<6 21 0 0.0000 1.0000 1.0000
6–<12 21 9 0.4286 0.5714 1.0000
12–<18 12 3 0.2500 0.7500 0.5714
18–<24 9 4 0.4444 0.5556 0.4286
24–<30 5 1 0.2000 0.8000 0.2381
30–<36 4 4 1.0000 0.0000 0.1905
0
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Example 1: Plot of Time to Relapse Pt
Prob
abili
ty o
f rem
issi
on
0 6 12 18 24 30 360
0.2
0.4
0.6
0.8
1
Time in months
Pt
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= the cumulative probability in remission at time t−
At the beginning of the time interval
The cumulative probability still in remission at 24 months is:−
0.2381 or 24%
The probability of relapse between 24 and 30 months is:−
= 0.20 or 20%
Pt
q30
Example 1: Clinical Life Table—No Withdrawal/No Loss
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Example 2
50 patients with skin melanoma were treated in one hospital during the time period October, 1952–June, 1967Patients were followed annuallyThe study was closed to patient follow-up on December 31, 196920 deaths occurred30 observations were censored due to withdrawal or lack of follow-upWhat are the two-year and five-year survival rates?
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Example 2: Clinical Life Table
Interval l d w l’ q p P
0–1 50 9 0 50.0 0.180 0.820 1.000
1–2 41 6 1 40.5 0.148 0.852 0.820
2–3 34 2 4 32.0 0.063 0.937 0.699
3–4 28 1 5 25.5 0.039 0.961 0.655
4–5 22 2 3 20.5 0.098 0.902 0.629
5–6 17 0 17 8.5 0 1.000 0.567
0.567
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Example 2: Cumulative Probability of Survival
P is the cumulative probability of surviving at the beginning of the time interval or at the end of the previous intervalTwo-year survival (rate) is the probability of surviving at the end of two years or at the beginning of year 3The two-year survival (rate) is 0.699, or 69.9%The five-year survival (rate) is 0.567, or 56.7%
Interv
al
l d w l’ q p P
0–1 50 9 0 50.0 0.180 0.820 1.000
1–2 41 6 1 40.5 0.148 0.852 0.820
2–3 34 2 4 32.0 0.063 0.937 0.699
3–4 28 1 5 25.5 0.039 0.961 0.655
4–5 22 2 3 20.5 0.098 0.902 0.629
5–6 17 0 17 8.5 0 1.000 0.567
0.567
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Section C
The Kaplan-Meier Method
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Kaplan-Meier Method
Kaplan-Meier is also a survival analysis method−
It is very similar to the clinical life table method
It uses the exact times that events occurred—rather than the intervals of follow-upThe probability of the event is equal to the number of events at that time divided by the number at risk at that point in time (including those who had the events) If there are withdrawals before the time of event, they are subtracted from the number at risk
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Example: Kaplan-Meier Method
From Gordis textbook6 patients−
4 died
−
2 lost to follow-upDeaths occurred at 4, 10, 14, and 24 monthsLost occurred before 10 months and before 24 months
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Example: Kaplan-Meier Table and Kaplan-Meier Plot
The table is similar to the clinical life table−
Instead of intervals, it uses the exact time of events
In this example, the events occurred at 4, 10, 14, and 24 months (so there will be 4 rows in the table)All other calculations (q, p, and P) are the sameThe calculated cumulative probability of surviving is for that time pointUp to that time point, the cumulative probability of surviving takes on the value of the previous time point, thus leading to a step function (see K-M plot)When there is no event, the survival curve in a K-M plot will be drawn out horizontally over time and only drop (vertically) down at the time of events (e.g., deaths) to the calculated cumulative probability of surviving
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One died at 4 months, and one was lost to follow-up before 10 months; therefore, 4 were known to be alive at 10 months
One died at 4 months, and one was lost to follow-up before 10 months; therefore, 4 were known to be alive at 10 months
Kaplan-Meier Survival Table
Time to deaths Number alive dt qt pt Pt
4 6 1 0.167 0.833 0.833
10 4 1 0.250 0.750 0.625
14 3 1 0.333 0.667 0.417
24 1 1 1.000 0.000 0.000
Another “lost”
occurred here before 24 monthsAnother “lost”
occurred here before 24 months
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Kaplan-Meier Plot of Survival Study
Prob
abili
ty o
f sur
vivi
ng
0 4 14 240
0.2
0.4
0.6
0.8
1
Months after enrollment10
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Use of Kaplan-Meier Method
K-M method takes advantage of all information available in the calculation and is useful for small sample size studies Clinical studies use K-M plots to display prognosis over timeK-M estimates can be used for comparison purpose in clinical trials when groups are similar and adjustment is not needed
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Review
What is the difference between l and l’?What is the difference between p and P?What are the assumptions in a clinical life table?