A Survey of Survival Analysis Techniques

George Marinos

and Dimosthenis Kyriazis

Digital Systems Department, University of Piraeus, Piraeus, Greece

Keywords:

Survival Analysis, Clustering, Machine Learning, Risk Stratiﬁcation.

Abstract:

Survival analysis is a branch of statistics for analyzing the expected duration of the time until the event of inter-

est happens. It is not only applicable to biomedical problems but it can be widely used in almost every domain

since there is a relevant data structure available. Recent studies have shown that it is a powerful approach for

risk stratiﬁcation. Since it is a well established statistical technique, there have been several studies that com-

bine survival analysis with machine learning algorithms in order to obtain better performances. Additionally

in the machine learning scientiﬁc ﬁeld the usage of different data modalities has been proven to enhance the

performance of predictive models. The majority of the scientiﬁc outcomes in the survival analysis domain

have focused on modeling survival data and building robust predictive models for time to event estimation.

Clustering based on risk-proﬁles is partly under-explored in machine learning, but is critical in applications

domains such as clinical decision making. Clustering in terms of survivability is very useful when there is

a need to identify unknown sub-populations in the overall data. Such techniques aim for identiﬁcation of

clusters whose lifetime distributions signiﬁcantly differs, which is something that is not able to be done by

applying traditional clustering techniques. In this survey we present research studies in the aforementioned

domain with an emphasis on techniques for clustering censored data and identifying various risk level groups.

1 INTRODUCTION

The pursuit of accuracy is the primary purpose of al-

most all human ﬁeld endeavors. A good pursuit of

accuracy might be the dominant expectation for the

practitioners, especially in the healthcare ﬁeld, given

the extremely small margin of error on the predic-

tions which they make. Survival analysis is a major

decision technique in healthcare practices. It can be

used for a variety of reasons such as the deeper under-

standing of the effect of some genetic or proteomic

bio markers on prognosis of cancer patients, under-

standing the impact that risk factors such as diabetes,

hypertension and other cardiovascular diseases have

on Chronic Kidney Diseases (CVD) or even know the

outcome of physical exercises, diets or family health

history in understanding cardiac heart problems in pa-

tients. In general it is used to predict the time un-

til a particular event of interest happens. Although

it was initially created in terms of medical research

and the purpose was to model a patient’s survival, it

can be applied to several other application domains.

For instance, survival analysis can be used to estimate

https://orcid.org/0000-0002-2220-0009

https://orcid.org/0000-0001-7019-7214

the probability of failure of manufacturing equipment

based on the hours of operations. It turns out that the

vast majority of the survival analysis research results

have focused on time to event prediction either by us-

ing statistical methods or by using machine learning

algorithms. Despite its importance there is only lim-

ited number of research papers focused on survival

clustering. For instance in many real world cases

practitioner’s main pursuit is to discover the various

sub-groups of a cohort that corresponds to different

risk levels and not necessarily the individual risk esti-

mation of each subject. In such a scenario the target

would be the discovery of the clusters which not only

have similar traits but simultaneously have different

lifetimes. The purpose of this paper is to emerge the

importance of this type of methods used for risk strat-

iﬁcation. Such methods have been developed for risk-

proﬁle based clusters-discovery in a cohort that may

have unknown number of clusters. The remainder of

this paper is divided into four sections which include

the taxonomy of methods that have been used for

survival analysis, problem formulation and the def-

initions of the main survival functions, summary of

all the methods used for risk-based clusters discovery

and conclusion.

716

Marinos, G. and Kyriazis, D.

A Survey of Survival Analysis Techniques.

DOI: 10.5220/0010382307160723

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 716-723

ISBN: 978-989-758-490-9

Non

Parametric

Semi

Parametric

Time to event

Prediction

Clustering

Based on

Risk Proﬁle

Statistical

Methods

Machine

Learning

Methods

Survival Trees

Bayesian

Methods

Neural Networks

Support Vector

Machines

Advanced

Machine

learning

Statistical

Methods

Machine

Learning

Methods

Semi

Supervised

Clustering

Pre-weighted

sparce

Clustering

Deep

Lifetime

Clustering

Machine

learning for

survival

prediction on

high

dimensional

data

Kaplan Meier

Life Table

Cox Regression

Non Parametric

Survival Analysis

Methods

Partial Cox

Regression

Analysis

Complementary

Hierarchical

Clustering

Supervised

Principal

Components

Relevant

survival types

identiﬁcation

via clustering

Network data

Clustering

method for

cansored and

collinear

survival data

Survival

Clustering

Analysis

Figure 1: Survival Analysis Taxonomies.

2 TAXONOMY OF SURVIVAL

ANALYSIS METHODS

Survival Analysis methods can be divided in Statisti-

cal and Machine Learning methods. Statistical meth-

ods can be divided into three wide categories: (i) para-

metric, (ii) non-parametric and (iii) semi-parametric

methods. Under the assumption of having a data

set that follows a particular distribution, parametric

methods can be very efﬁcient and accurate for time to

event prediction. For example assuming the time of

the examined data set follows a well known theoreti-

cal distribution such as exponential it is quite simple

to use it for time to event estimation. Although in real

life datasets, it is difﬁcult to obtain data that follows a

known theoretical distribution. Non-parametric meth-

ods can be used in this case since there is no underly-

ing distribution for the event time and there are no as-

sumptions that need to be met. Kaplan Meier (Kaplan

and Meier, 1958) method is one of the most popular

methods of this category. Third category is a hybrid of

the parametric and non-parametric approaches. Like

non-parametric methods, at semi-parametric models

the knowledge of the underlying distribution of time

to event is not required. Cox model (Cox, 1972) is the

most widely used semi-parametric survival analysis

method in this category and it assumes the attributes

have a multiplicative effect in the hazards function

that is constant over time.

2.1 Machine Learning Techniques for

Time to Event Predictions

Despite statistical techniques which aim to character-

ize the distribution of the event times as well as the

statistical properties of the parameters of each (statis-

tical) model, machine learning methods seek for mak-

ing prediction of event phenomenon at a given time

point. Decision tree algorithm (Bou-Hamad et al.,

2011) which is grounded on splitting the data recur-

sively based on a particular splitting criterion adapted

for survival analysis (Bou-Hamad et al., 2011). Since

the main characteristic of such an algorithm is the

splitting criterion there have been some research stud-

ies focusing on the ﬁnding of a splitting criterion that

can be effectively used for survival analysis (Ciampi

et al., 1987). Bayesian Analysis is one of the most

fundamental principles in statistics which links the

posterior probability and the prior probability. Several

studies have used such models to predict the proba-

bility of the event of interest (Kononenko, 1993) and

beneﬁt from the good properties of Bayesian mod-

eling (Ibrahim et al., 2014) such as interpretability.

Support vector machines (Van Belle et al., 2007) also

are a very important category of machine learning al-

gorithms which can be used both for classiﬁcation

(Shivaswamy et al., 2007) and regression and have

been successfully adapted to survival analysis prob-

lems.

A Survey of Survival Analysis Techniques

717

In addition neural network - based machine learn-

ing models have been proposed to predict lifetime of

a subject. (Katzman et al., 2018) is based on the semi

parametric Cox Proportional Hazard model. Further-

more there have been introduced deep learning tech-

niques for life time prediction such as (Giunchiglia

et al., 2018) that is based on a recurrent neural net-

work architecture. The proposed neural network takes

as inputs at each time the features characterizing the

patient and the identiﬁer of the time step, creates an

embedding, and outputs the value of the survival func-

tion in that time step. In Figure 1 we demonstrate a

graphic illustration for the taxonomy of all the sur-

vival methods that have been introduced in literature

not only for solving the task of time to event predic-

tion but also techniques that have been proposed for

clustering based on risk proﬁle.

3 SURVIVAL ANALYSIS

PROBLEM FORMULATION

A given observation i in our dataset represented by a

triplet (X

, y

, z

), where X

∈ R

1×P

is the feature vec-

tor, z

is the binary event indicator which is marked

as 1 when the subject has experienced the event of in-

terest, otherwise it is marked as 0. Finally y

is the

observed time which is equal to the survival time T

if the given observation is uncensored otherwise C

, if

the given observation is censored.

(

, if z

= 1

, if z

= 0

(1)

The purpose of survival analysis is to estimate the

time to the event of interest for a new instance k with

feature predictors denoted by a new feature vector X

3.1 Concept of Censorship/ Censored

Data

In real world scenarios the collection of complete data

sets may be a challenging expectation due to various

reasons. Especially in clinical studies the data col-

lection period may last for several years and would

require consistency of the participants of the study in

order to keep tracking their data. It is possible that

the event of interest can not be observed for some in-

stances. Censorship is related to the problem of miss-

ing data, consequently this is the main reason which a

traditional regression model cannot be ﬁtted for mak-

ing predictions in such kind of data (Prinja et al.,

2010). Censorship can generally be observed in three

variations (Clark et al., 2003): (i) right-censoring,

which is the most common type of censoring and it

occurs when the observed survival time is less than

or equal to the true survival time, (ii) left-censoring,

for which the observed survival time is greater than

or equal to the true survival time, and (iii) interval-

censoring, for which we only know that the event oc-

curs during a given time interval. In survival analysis

when utilizing censored data the time to the event of

interest is the target variable. This is only known for

those instances who have experienced the event dur-

ing the study period. In Figure 2 we cite an example

of a cohort that may be used in the context of survival

analysis. The cohort is made up of four subjects each

one of them belongs to a different category of censor-

ship and one not censored subject. Subject 1 is not

censored since we have monitored the duration of its

life from the early start until the event occurs. Subject

2 and subject 3 are considered to be right censored

because we have track them from the early start of

our study but it had not been experienced the event of

interest until the end of study so we do not have pre-

cisely information about the duration of its lifetime.

Subject 4 is considered to be left censored since it is

unknown when it entered the study and subject 5 is

considered to be interval censored since data collec-

tors lost its signals in the middle of the study.

Time

End of study

Start of study

Not Censored

Right Censored

Left Censored

Died

Alive

Known Period

Unknown Period

Interval Censored

Sub.2

Sub.3

Sub.4

Sub.5

Sub.1

Figure 2: Concept of Censorship.

3.2 Survival Analysis Formulas and

Deﬁnitions

The survival function (Lee and Wang, 2003) repre-

sents the probability that the time to the event of in-

terest is not earlier than a speciﬁed time t. Often sur-

vival function is referred as: the survivor function or

survivorship function in problems of biological sur-

vival, and as reliability function in mechanical sur-

HEALTHINF 2021 - 14th International Conference on Health Informatics

718

vival problems. Survival function is represented as

follows:

S(t) = P(T > t) (2)

The function above denotes an individual that sur-

vives longer than t. Survival function decreases when

the t increases. Its starting value is 1 for t = 0 which

represents that in the beginning of the observation all

subjects survive. From the deﬁnition of cumulative

death distribution function F(t),

S(t) = 1 − F(t) (3)

Cumulative death function represents the probability

that the event of interest occurs earlier than time t .The

survival function is therefore associated with a contin-

uous probability density function by

S(t) = P(T > t) =

P(x)dx (4)

Similarly the survival function is related to a discrete

probability P(t) by

S(t) = P(T > t) =

∑

T >t

P(x) (5)

3.3 Survival Hazard Function

In survival analysis, another commonly used function

is the hazard function h(t), which is also called the

force of mortality, the instantaneous death rate or the

conditional failure rate (Dunn and Clark, 2009). The

hazard function t (Lee and Wang, 2003) does not in-

dicate the prospect or probability of the event of in-

terest, but it is the rate of event at time t as long as no

event occurred before time t. In this sense, the hazard

is a measure of risk. The hazard function is deﬁned

as:

h(t) =

f (t)

S(t)

(6)

Speciﬁcally is the ratio of the probability density

function to the survival function. In particular since

by deﬁnition the probability density function is:

f (t) = lim

dt→ 0

F(t + dt) − F(t)

(7)

where dt denotes the time interval we can write the

hazard function as:

h(t) = lim

dt→ 0

Pr(t ≤ T < t + dt|T ≥ t)

(8)

It is deﬁned as of failure during a very small time in-

terval assuming that the individual has survived to the

beginning of the interval. The hazard function can

also be deﬁned in terms of the cumulative distribu-

tion function F(t) and the probability density function

f (t) as:

h(t) =

f (t)

1 − F(T )

f (t)

S(t)

(9)

and ﬁnally the cumulative hazard function describes

the accumulated risk up to time t given by

H(t) =

h(x)dx (10)

In addition to the above relations, there is another

important connection between h(t) (or H(t)) and S(t)

given by

S(t) = exp(−

h(x)dx) = exp(−H(t)) (11)

4 CLUSTERING BASED ON RISK

PROFILE

Li and Gui proposed a different extension of partial

least squares (PLS) regression to the censored sur-

vival data in the framework of the Cox model by pro-

viding a parallel algorithm for constructing the latent

components (Li and Gui, 2004). The proposed al-

gorithm involves constructing predictive components

by iterated least square ﬁtting of residuals and Cox

regression ﬁtting. These components can then be

used in the Cox model for building a useful predic-

tive model for survival. Although the purpose of

constructing such a method is not towards survival

clustering it can also be used for clustering survival

data since the principal components are constructed

as well.

In a relevant study, researchers noticed the impor-

tance of cancer subtype discovery using genes expres-

sion data and clinical data together (Bair and Tibshi-

rani, 2004). Discovered subtypes appeared to have

signiﬁcant differences in terms of patients survival

when the semi supervised proposed technique was

used. The authors addressed the problem of can-

cer subtypes identiﬁcation without having any prior

knowledge of the existence or the number of cancer

subtypes in the dataset. The whole process of this

approach has two parts. Firstly only genes expres-

sion data are utilized using Cox regression in order to

assign each of them a (“Cox”) score and then select

only those genes with high score. After that proce-

dure only signiﬁcant genes have been chosen for the

dataset. Having chosen a subset of genes expression

they apply traditional clustering techniques e.g. K-

means only on genes expression data and they obtain

the desirable number of clusters. At the second part

of the proposed approach they test the cluster assign-

ment using only the clinical data. Utilizing clinical

data they set cluster assignment as the dependent vari-

able and apply classiﬁcation algorithms. Finally the

classiﬁcation algorithm performs well which means

A Survey of Survival Analysis Techniques

719

that clusters assignments have been correctly identi-

ﬁed.

Bair (Bair et al., 2006) and Tibshirani (Bair and

Tibshirani, 2004) mentioned the drawback of the us-

age of principal components for regression and sur-

vival model which is the fact that few principal com-

ponents may summarise a large proportion of the vari-

ance present in the data in this way there is no guar-

antee that these principal components are associated

with the outcome of interest. Therefore they proposed

(Bair et al., 2006) a semi-supervised approach, which

they called supervised principal components (SPC).

In this method univariate Cox scores are computed

for each feature and the choice of the most signiﬁcant

features is done by picking only the features with the

best Cox scores obtained . Mainly supervised prin-

cipal components method is similar to conventional

principal components analysis except that it uses a

subset of the predictors selected based on their asso-

ciation with the outcome. An improved variation of

previous method “pre-weighted sparse clustering” has

also been proposed (Gaynor and Bair, 2017). As men-

tioned before Sparse clustering method and also semi-

supervised clustering method have signiﬁcant limita-

tions mainly because they are heavily depend on the

number of features that have been characterized as

“signiﬁcant”. Pre-weighted sparse clustering aims to

overcome the limitations of sparse clustering by per-

forming conventional sparse clustering. It identiﬁes

features whose mean values differ across the clusters.

Then the sparse clustering algorithm is run a second

time, but rather than giving equal weights to all fea-

tures as in the ﬁrst step, this pre-weighted version of

sparse clustering assigns a weight of 0 to all features

that differed across the ﬁrst set of clusters. The mo-

tivation is that this procedure will identify secondary

clusters that would otherwise be obscured by clusters

that have a larger dissimilarity measure. Moreover in

this study it is proposed the supervised version of pre-

weighted sparse clustering which assigns the initial

weights of the chosen clustering algorithm by giving

non zero weights to the features that are most strongly

associated with the outcome variable.

The identiﬁcation of “secondary” clusters that

may be “covered” by the primary clusters involving

large numbers of high variance features has been a

case of research (Nowak and Tibshirani, 2008). At the

step of this method traditional hierarchical clustering

is performed on a data matrix X. A new data matrix

X results from this hierarchical clustering procedure

and is deﬁned to be the expected value of the resid-

uals when each row of X is regressed on the group

labels when the hierarchical clustering tree is cut at a

given height. The expected value is taken over all pos-

sible cuts. This has the effect of removing high vari-

ance features that may be obscuring secondary clus-

ters. Traditional hierarchical clustering is then per-

formed on this modiﬁed matrix X, yielding secondary

clusters.

Zhang (Zhang et al., 2016) following the Bair

approach (Bair and Tibshirani, 2004) used a mixed

methodology composed by statistical and machine

learning methods. The proposed method was fo-

cused on clusters discovery over clinical and genes

expression data. Authors proposed a semi supervised

pipeline for survival clustering discovery. Authors

initially used only the clinical part of the data in or-

der to estimate the censored lifetimes. Actually they

utilized penalized logistic regression and penalized

proportional hazard model with the Expectation min-

imization algorithm in order to select only the most

signiﬁcant clinical features which are correlated with

the event of interest. After a list of signiﬁcant clinical

variables have been identiﬁed they used the K neigh-

bors based method (with 10 neighbors) on the ﬁltered

data set for the survival time estimation for the pa-

tients with censored survival time. After they applied

silhouette method on the ﬁltered data set in order to

identify the optimal number of clusters. Then Fast

correlation based ﬁlter method is applied on genes ex-

pression data n order to select the most signiﬁcant fea-

tures. With this method redundant features with lower

relevancy are removed from the list until the number

of last features reaches a targeted low bound or there

are no more features to be removed. Finally a classi-

ﬁer is used in the selected genes in order to predict the

label identiﬁed from the clinical data set. The perfor-

mance of the classiﬁer can be considered as a measure

of the robustness of the performed clustering and also

if the identiﬁed groups share the same survival distri-

bution.

Mouli (Mouli et al., 2017) proposed a decision

tree based approach which aim for survival cluster-

ing. The ﬁnal purpose of this research paper was to

cluster censored data and identify two or more pop-

ulations with different risk level. Paper’s objective

was the survival distributions identiﬁed to be differ-

ent across clusters. The initial step of this method is

to concretely break the data set in two populations and

based on attribute - values test to observe the identi-

ﬁed populations survival distribution. This is done by

using the Kaplan-Meier estimates. Kuiper (Kuiper,

1960) statistics is used after in order to quantify the

the signiﬁcance of the difference across survival dis-

tributions. The procedure is repeated for all attribute

- value pairs in order to choose the one with the best

results is used as a node in the constructed decision

tree. In this step of the algorithm best result is consid-

HEALTHINF 2021 - 14th International Conference on Health Informatics

720

ered to be the lowest p-value of the aforementioned

Kuiper statistic measure where the signiﬁcance level

of the algorithm can be speciﬁed by the user. Au-

thors describe that the process of performing many

statistical tests at each node conduct a multiple hy-

pothesis problem which can be corrected using Bon-

ferroni (Rupert Jr et al., 2012) correction as proposed

in the paper. The suggested methodology results in a

tree where each leaf node has an associated popula-

tion of users and thus clusters can be observed at leaf

nodes. Despite the fact that in his way, subjects with

similar survival distributions will be placed closer in

the tree diagram, the degree of dissimilarity between

identiﬁed clusters may not necessarily be signiﬁcant.

Hence the target is to identify clusters with differ-

ent survival distributions authors propose the usage

of complete graph. The graph consists of leaf nodes

as vertices and p-values as edge weights. Each nodes

will be connected to the other with the edges ( normal-

ized p-values) which will denote a signiﬁcant or non

signiﬁcant relationship between them. Markov clus-

tering algorithm (Van Dongen, 2001) is applied to the

ﬁnal graph .

A recent research study (Mouli et al., 2019) intro-

duced DeepCLife an inductive neural network based

clustering model architecture. This framework aims

for the observation of empirical lifetime distribution

of underlying clusters. The ﬁnal purpose of this

framework is to conduct clusters which have different

lifetime distributions whereas the subjects of the same

cluster share the same lifetime distribution. The pro-

posed model does not assume proportional hazards.

A very important asset of this research paper is that

it addresses the issue of unobservability of termina-

tion signals, meaning that it can be applied on data

sets that termination signals have not been recorded.

The main contribution of this work is the proposal

of a novel clustering loss function which is based on

Kuiper two-sample test. Authors provide a tight up-

per bound of the Kuiper p-value, without computa-

tionally expensive gradients which until then was the

main difﬁculty of its usage as a loss function (due

to the test’s inﬁnite sum - this is not going to be in-

cluded). In this paper authors describe the usage of

a feedforward neural network although the proposed

model is not restricted to a feedforward architecture,

which makes this approach ﬂexible and worthy to try

different models in order to observe differences in the

performance. The proposed model aims to identify

clusters with maximizing the divergence between the

empirical lifetime distributions of each cluster.

Liverani (Liverani et al., 2020) proposed a Dirich-

let process mixture model for censored survival data

with covariates, inspired predominantly by (Molitor

et al., 2011) and (Molitor et al., 2010). The proposed

model is a mixture of Weibull distributions and also

distributions suitable for the data set covariates which

non parametrically assigning data to clusters. In this

approach the response variable (the presense of ab-

sense of the event) and the covariates are modeled in-

dependently which allows the exploration of the com-

plex relationship between them. Despite the fact of

independently modelling this approach can uncover

linear and non-linear relationships between covariates

and response.

Unlike DeepCLife which aims to identify clusters

by maximizing pairwise differences between the sur-

vival function of all cluster pairs Chapfuwa’s (Chap-

fuwa et al., 2020) study focuses on characterization

of time-to-event predictive distributions from a clus-

tered latent space conditioned on covariates. In par-

ticular authors at Survival Cluster Analysis produced

a complex model which provides not only risk pro-

ﬁle based clustering but also acts as a deterministic

encoder that maps covariates into a latent representa-

tion and on top of that a stochastic survival predictor

feeds from the latent representation. In this paper a

Bayesian non parametric approach was used for the

clustering process. The Bayesian approach embold-

ened the latent representation to act like a mixture of

distributions while distribution matching approach (in

this study) follows a Dirichlet Process. Table 1 sum-

marize all the algorithms developed in the literature

that aim to address the problem of distinguish sub-

jects in a dataset based on their risk proﬁle taking into

account also the censorship which is usually met in

real world datasets.

5 CONCLUSIONS

Survival analysis can be used widely in almost every

domain since there is a relevant data structure avail-

able. Notwithstanding the fact that there have been

published many scientiﬁc reports applying machine

learning techniques for time to event predictions

which have been obtaining very good performances,

published scientiﬁc researches dedicated to survival

clustering techniques were signiﬁcantly fewer. Clus-

tering in terms of survivability is very useful when

there is a need to identify subpopulations in the over-

all dataset who are unknown. Such techniques aim

for identiﬁcation of clusters whose lifetime distribu-

tions signiﬁcantly differs which is something that is

not able to be done by applying traditional clustering

techniques.

A Survey of Survival Analysis Techniques

721

Table 1: This is a summary of all methods and algorithms that have been used in literature for clustering based on risk proﬁle

while using censored data.

Survival Clustering Algorithms

Title Application

Domain

Characteristics / Attributes / Features

Partial Cox regres-

sion analysis for

high-dimensional

microarray gene

expression data

Applicable at

every Domain

Reduces the number of features in a high-dimensional dataset

since it aims to identify the principal components and parallel

apply partial least squares method. It has not computational lim-

itations in terms of number of variables in contrast to other sta-

tistical methods. Proportional Hazard assumption must be met

Semi-Supervised

Methods to Predict

Patient Survival from

Gene Expression

Data

HealthCare Restricted to be used for Medical data (clinical and genes ex-

pression). Results may depend heavily on the features chosen.

Proportional Hazard assumption must be met. It is dedicated to

clinical and genes expression data even thought it can be per-

fectly ﬁtted in medical applications

Complementary hier-

archical clustering

HealthCare Uncovers structures arising from the weaker genes. It is an auto-

matic procedure and after performing the initial clustering, there

is no need to decide how many groups should be considered or

where to cut the dendrogram. The backﬁtting algorithm used

somewhat complicates the interpretation of the initial, comple-

mentary clustering

Supervised principal

components (SPC)

Applicable at

every Domain

Reduces the number of features in a high-dimensional dataset.

Proportional Hazard assumption must be met

Using the machine

learning approach

to predict patient

survival from high-

dimensional survival

data

Healthcare It is dedicated to clinical and genes expression data thought it

can be perfectly ﬁtted in medical applications. Restricted to be

used for Medical data (clinical and genes expression). Results

may depend heavily on the features chosen. Proportional Hazard

assumption must be met.

Identifying User Sur-

vival Types via Clus-

tering of Censored

Social Network Data

Applicable at

every Domain

The model is based on decision trees and thus can handle both

categorical and numerical features with ease and without encod-

ing variables.

Identiﬁcation of rel-

evant sub-types via

pre-weighted sparse

clustering

HealthCare It is not essential to choose an “optimal” set of initial weights

since the procedure tends to correct itself. Tends to self-correct

so that relevant features get nonzero weight (even if their initial

weight was zero) and irrelevant features get 1 zero weight (even

if their initial weight was nonzero)

Deep Lifetime Clus-

tering

Applicable at

any Domain

Does not assume Proportional Hazard. It can smoothly handle

the absence of termination signals. There is not a way to ﬁnd the

optimal number of clusters apart from making many different

trials and keep the best results.

Clustering method

for censored and

col-linear survival

data

HealthCare Deals with data collinearity, therefore model can achieve a good

performance without to be necessary strongly correlated covari-

ates to be removed before. The independent response - covari-

ates modelling allows the exploration of the complex relation-

ship between the response and the covariates.

Survival Cluster

Analysis

Applicable at

any Domain

It is not necessary for the user to specify the number of clus-

ters since they are identiﬁed automatically. The model identiﬁes

interpretable populations

HEALTHINF 2021 - 14th International Conference on Health Informatics

722

ACKNOWLEDGEMENTS

This research has been co-ﬁnanced by the European

Union and Greek national funds through the Oper-

ational Program Competitiveness, Entrepreneurship

and Innovation, under the call RESEARCH – CRE-

ATE – INNOVATE (project code: BeHEALTHIER -

T2EDK-04207).

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