Survival Analysis Using SPSS

Survival
Analysis Using
SPSS
Dr. Nermin Osman
Assistant Lecturer of Biomedical Informatics and Medical Statistics
Medical Research Institute, Alexandria University
United Nations System Staff College Intern, UNITAR
nerminosman@unssc.org

What is survival analysis
− Event history analysis
− Time series analysis
When use survival analysis
− Research interest is about time-to-event and event is discrete
occurrence.
Examples of survival analysis
− Duration to the hazard of death
− Adoption of an innovation over time
Characteristics of survival analysis
− At any time point, events may occur
− Factors influence events include two types: time-constant and
time-dependent (factor: age).
Survival analysis

Survival analysis focuses on hazard function
− Hazard: any worse event of interest occurring
− Hazard might be death, engine breakdown, etc.
− Hazard rate: is the instantaneous probability of the given
event occurring at any point in time. It can be plotted against
time on the X axis, forming a graph of the hazard rate over
time.
− Hazard function: the equation that describe this plotted line is
the hazard function.
− Hazard ratio: also called relative risk: Exp(B) in SPSS.
Survival analysis

Vertical
(y)
Hazard
rate
Horizontal (x)
Time
Yrs/ ms
Hazard
Function

Type of survival analysis
− Nonparametric: no assumption about the shape of hazard
function. Hazard function is estimated based on empirical
data, showing change over time, for example, Kaplan-Meier
survival analysis.
− Semi-parametric: no assumption about the shape of hazard
function, but make assumption about how covariates affect
the hazard function, for example: Cox regression
− Parametric: specify the shape of baseline hazard function and
covariates effects on hazard function in advance.
− Used when time is itself considered a meaningful independent
variable. (parametric)
− Used for predictive modeling (Logistic Regression)& Maximum
likelihood method(large dataset)
Survival analysis

Terms
− Events: what terminates an episode (such as death, adoption
of an innovation), it is the change which causes the subject to
transition from one state to another.
− Durations: the number of time units an individual spends in a
given state.
− Dependent: probability of an event.
− Survival function, s(t): is the cumulative frequency of the
proportion of the sample Not experiencing the event by time
t. In another word, it is the probability of event will NOT occur
until time t.
− Censored cases: data are censored if events start before (left-
censored) or ended after (right-censored) the period of
observation.
Survival analysis

Censored cases: unique characteristics of survival
analysis.
− For some cases, the event simply doesn’t occur before the end
of study.
− For some cases, they drop out from the study for reasons
unrelated to the study.
− For some cases, we lost track of their status sometime before
the end of the study.
Survival analysis

Censored cases
Survival analysis

Outline of topics
− Life tables
− Kaplan-Meier
− Cox regression
− Cox regression with a time-dependent covariate
Survival analysis

Life tables
 Life Tables is a descriptive procedure for
examining the distribution of time-to-event
variables. We also can compare the distribution
by levels of a factor variable.
 The basic idea of life tables is to subdivide the
period of observation into smaller time intervals.
Then the probability from each of the intervals
are estimated.

Variables
− Time variable (duration variable): must be a continuous
variable.
− Status variable: binary or dichotomous variable, represents
the event of interest.
− Factor variable: categorical variable.
Assumption
− The probability for the event of interest should depend only
on time. Cases that enter the study at different times should
behave similarly.
− No systematic differences between censored and uncensored
cases
Life tables

Example (from IBM SPSS 20.0): data file name: telco
− Examine distribution of customer time to churn by customer
category.
− Time variable: tenure (in month)
− Status variable: churn (binary: 1 = Churn, 0 = Not churn)
− Factor: custcat (four categories)
Go to Analyze > Survival > Life Tables
Life tables

Click Options
Life
tables
1. Survival: display the
cumulative survival
function on a linear scale
2. Hazard: display the
cumulative hazard
function on a linear
scale.

SPSS Outputs: life table
Life tables

SPSS outputs: life table
− Interval Start Time. The beginning value for each interval.
Each interval extends from its start time up to the start time of
the next interval.
− Number Withdrawing during Interval: the number of censored
cases in this interval. These are still active customers, but so
far they have not been customers longer than the time period
indicated by this interval.
− Number Exposed to Risk. The number of surviving cases minus
one half the censored cases. This is intended to account for
the effect of the censored cases.
Life tables

SPSS outputs: life table
− Number of Terminal Events. The number of cases that
experience the terminal event in this interval. These are
customers with churn = 1.
− Proportion Terminating. The ratio of terminal events to the
number exposed to risk (10/264.5=0.0378).
− Proportion Surviving. One minus the proportion terminating.
Life tables

− Cumulative Proportion Surviving at End of Interval. The
proportion of cases surviving from the start of the table to
the end of the interval (266-10-17)/266=0.8984 (second
row).
− Probability Density. An estimate of the
probability of experiencing the terminal event
during the interval.
(i.e. likelihood that an item will experience the terminal
event at a certain point in time based on its survival to an
earlier time)
− Hazard Rate. An estimate of experiencing the terminal
event during the interval, conditional upon surviving to
the start of the interval. (i.e. the rate of death for an item of a
given time)
Life tables

− The greatest number and proportion of terminal events occur
within the first year, which suggests that customers should be
monitored more closely during their first year to be sure of
their satisfaction with the company's service.
Life tables

SPSS Outputs: survival function
Life
tables
1.The horizontal axis shows the
time to event. The vertical axis
shows the probability of survival.
2.Any point on the survival curve
shows the probability that a customer
of a given service category will
remain a customer past that time.
3.Total service and Basic service
customers have the lowest survival
curves, and E- service customers have
lower curves than Plus service
customers.

SPSS Outputs
Life tables
1.Wilcoxon test is used to compare
survival distribution among groups,
with the test statistic based on
differences in group mean scores.
2.Since the significance value of
the test is less than 0.05, we
conclude that the survival curves
are different across the group.
3. Pairwise comparisons show
which two groups are
significantly different in survival
curves.

The Kaplan-Meier procedure is a non-parametric
method of estimating time-to-event models in the
presence of censored cases.
A descriptive procedure for examining the distribution
of time-to-event variables. We also can compare the
distribution by levels of a factor variable or produce
separate analyses by levels of a stratification variable.
Censored cases (right-censored cases) are those for
which the event of interest has not yet happened.
Kaplan-Meier procedure

Assumptions
− Probabilities for the event of interest should depend only on
time taking in consideration a stratified factor after the
initial event without covariates effects.
− Cases that enter the study at different times (for example,
patients who begin treatment at different times) should
behave similarly.
− Censored and uncensored cases behave the same. If, for
example, many of the censored cases are patients with more
serious conditions, your results may be biased.

Example (from IBM SPSS 20.0) : data file:
pain_medication
− A pharmaceutical company is developing an anti-inflammatory
medication for treating chronic arthritic pain. The research
interest is the time it takes for the drug to take effect and how
it compares to an existing drug. Shorter times to effect are
considered better.
Event: drug takes effect

Variables
− Time variable (duration variable): must be a continuous
variable
− Status variable: categorical variable, represents the event of
interest (drug has effect or not).
− Factor variable (stratification variable): categorical variable,
represents a causal effect (type of treatment for example).

 We have Factor variable (stratification factor): Treatment (0 = New
drug, 1 = old drug), Status variable (event): status ( 0 = censored,
1 = take effect), Time variable: time
 We want to compare the effect of two different drugs. Null
hypothesis: whether survival function is the same between
different levels of the factor variable (between old and new drug)
.

Go to Analyze > Survival > Kaplan-Meier

Analyze data

Click Compare Factor
Log rank: Tests equality of survival functions by weighting all time points the
same.
Breslow: Tests equality of survival functions by weighting all time points by the
number of cases at risk at each time point.
Tarone-Ware: Tests equality of survival functions by weighting all time points by
the square root of the number of cases at risk at each time point.

Compare factor
Pooled over strata: a single test is computed for all factor levels, testing for
equality of survival function across all levels of the factor variable.
Pairwise over strata: a separate test is computed for each pair of factor levels
when a pooled test shows non-equality of survival functions.
For each stratum: a separate test is computed for group formed by the
stratification variable.
Pairwise for each stratum: a separate test is computed for each pair of factor
variable, for each stratum of the stratification variable.

Click Options

SPSS outputs: survival table
The survival table is a descriptive table
that details the time until the drug
takes effect. The table is sectioned by
each level of Treatment, and each
observation occupies its own row in
the table. The table is very large.

Survival table
− Time. The time at which the event or censoring occurred.
− Status. Indicates whether the case experienced the terminal
event or was censored.
− Cumulative Proportion Surviving at the Time. The proportion
of cases surviving from the start of the table until this time.
When multiple cases experience the terminal event at the
same time, these estimates are printed once for that time
period and apply to all the cases whose drug took effect at
that time.

Survival table
− N of Cumulative Events. The number of cases that have
experienced the terminal event from the start of the table
until this time.
− N of Remaining Cases. The number of cases that, at this time,
haven’t yet to experience the terminal event or be censored.

Means or Median for Survival time
.

Overall comparison
This table provides overall tests of the equality of
survival times across groups. Since the significance
values of the tests are all greater than 0.05, there is no
statistically significant difference between two
treatments in survival time.

Survival function
Kaplan-Meier
procedure
1. The survival curves give a
visual representation of the
life tables.
2. The horizontal axis shows
the time to event. In this
plot, drops in the survival
curve occur whenever the
medication takes effect in a
patient.
3. The vertical axis shows
the probability of survival
(probability of not experience
the treatment effect).

The Cox Regression procedure is useful for modeling the
time to a specified event, based upon the values of
given covariates.
One or more covariates are used to predict a status
(event).
The central statistical output is the hazard ratio.
Data contain censored and uncensored cases.
Similar to logistic regression, but Cox regression
assesses relationship between survival time and
covariates .
Cox regression

Terms
− Status variable: the dependent in Cox regression, should be
binary variable.
− Time variable: measures duration to the event defined by the
status variable (continuous or discrete).
− Covariates: independent/predictor variables. They can be
categorical or continuous. They also can be time-fixed or time-
dependent.
− Interaction terms
− Categorical covariates: SPSS automatically convert them into a set of
dummy variables, omitting one category (reference).
Cox regression

Example (from SPSS): data file: telco
− Use Cox Regression to determine which attributes are
associated with shorter "time to churn“.
− Time variable: tenure (month with services)
− Status variable: churn (0 = No, 1 = Yes).
− Covariates: age, marital, address, employ, retire, gender,
reside, and custcat
Go to Analyze > Survival > Cox Regression
Cox regression

Run Cox regression
Cox regression

Click Categorical
Cox regression

SPSS Outputs: variables in the equation
− Exp(B), which can be interpreted as the predicted change in
the hazard for a unit increase in the predictor.
− For binary covariates, hazard ratio is the estimate of the ratio
of the hazard rate in one group to the hazard rate in another
group.
− The value of Exp(B) for marital means that the churn hazard
for an unmarried customer is 1.395 times that of a married
customer.
− The value of Exp(B) for address means that the churn hazard is
reduced by 100%−(100%×0.943)=5.7% for each year a
customer has lived at the same address.
Cox regression

SPSS Outputs
Cox regression
This table displays the average
value of each predictor variable,
plus a pattern for each level of
custcat. The four patterns
correspond to each of the
customer types, each with
otherwise "average" predictor
values.

Cox regression
 SPSS Outputs: survival function
 The basic survival curve is
a visual display of the
model- predicted time to
churn for the "average"
customer.
 The horizontal axis shows
the
 time to event.
 The vertical axis
shows the probability
of survival.

SPSS Outputs
Cox regression
1. The plots show the effect
of customer category.
2.Total service and Basic
service customers have
lower survival curves
because, they are more
likely to have shorter times
to churn.

SPSS Outputs
Cox regression
1.The horizontal axis shows the
time to event.
2.The vertical axis shows the
cumulative hazard, equal to the
negative log of the survival
probability.

Time-dependent covariates
− Simple product of the time variable and the covariate.
− Some variables may have different values at different time
periods but aren't systematically related to time. In such
cases, you need to define a segmented time-dependent
covariate, which can be done using logical expressions.
Cox regression with a time-
dependent covariate

Example (from IBM SPSS 20.0): data file: recidivism
− A government law enforcement agency is concerned about
recidivism rates in their area of jurisdiction. One of the
measures of recidivism is the time until second arrest for
offenders. The agency would like to model time to rearrest
using Cox Regression, but are worried the proportional
hazards assumption is invalid across age categories.
Go to Analyze > Survival > Cox with time-dependent
covariate
Cox regression with a time-dependent
covariates

Run analysis: the product of variable T_ and variable age
Cox regression with a time-dependent covariate

Click Model and back to Cox regression window
dependent covariate
Dependent variable:
arrest2
Time variable: time
Covariates: age and
new product
variable: T_cov

SPSS outputs
dependent covariate
The time-dependent covariate has a significance value less than
0.05, which means it contributes to the model, but the value of the
coefficient is very small.
We found that the effect of age on recidivism is time-dependent,
and added a term to the model that helps to account for that
dependence.

Survival Analysis Using SPSS

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