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An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven – U.Hasselt [email protected] http://perswww.kuleuven.be/geert verbeke
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An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

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Page 1: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

An introduction to survival analysis

Geert VerbekeInteruniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven – U.Hasselt

[email protected]

http://perswww.kuleuven.be/geert verbeke

Page 2: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

Contents

1 Survival analysis without censoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Survival analysis with censoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Regression for survival data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Bibliography 58

Introduction to survival analysis i

Page 3: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

Chapter 1

Survival analysis without censoring

. Example

. The survival curve

. Estimation of survival curve

Introduction to survival analysis 1

Page 4: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

1.1 Example: Survival times of cancer patients

• Cameron and Pauling [1]; Hand et al. [2] p. 255

• Patients with advanced cancer of the stomach, bronchus, colon, ovary, or breastwere treated (in addition to standard treatment) with ascorbate.

• The outcome of interest is the survival time (days)

• Research question(s):

What is the prognosis for a patient with specific type of cancer ?

Do survival times differ with organ affected ?

Introduction to survival analysis 2

Page 5: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Dataset ‘Cancer’:

Stomach Bronchus Colon Ovary Breast

124 81 248 1234 1235

42 461 377 89 24

25 20 189 201 1581

45 450 1843 356 1166

412 246 180 2970 40

51 166 537 456 727

1112 63 519 3808

46 64 455 791

103 155 406 1804

876 859 365 3460

146 151 942 719

340 166 776

396 37 372

223 163

138 101

72 20

245 283

Average (days) Median (days)

Stomach: 286 124

Bronchus: 211.6 155

Colon: 457.4 372

Ovary: 884.3 406

Breast: 1395.9 1166

Introduction to survival analysis 3

Page 6: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Note the severe differences between averages and medians, due to the skewness ofthe distribution:

• Comparisons between groups is therefore based on parametric tests afterappropriate transformation (e.g., logarithmic), or based on non-parametric tests(e.g., Wilcoxon test).

Introduction to survival analysis 4

Page 7: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

1.2 The survival curve

• Often it is of interest to make a prognosis for specific patients, i.e., it is of interestto estimate the probability of ‘surviving’ a specific amount of time

• In other contexts, the response is not ‘survival’, but still a ‘time to event’:

. Progression free ‘survival’

. How long will a bulb ‘survive’

. Time untill first tooth is affected with caries

. Time a rat needs to find the exit of a maze

. . . .

• Terminology: Survival and Failure

Introduction to survival analysis 5

Page 8: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• In the cancer example, it may be of interest to estimate how likely it is that apatient with colon cancer, treated (in addition to standard treatment) withascorbate, will survive 1 year, 2 years, . . .

• Interest is then in the survival function / curve:

S(t) = P (Outcome > t)

“The probability of surviving time point t”

• Properties of S(t):

. S(0) = 1: There is absolute certainty to ‘survive’ t = 0

. S(+∞) = 0: There is absolute certainty to ‘fail’ eventually

. S(t) is a decreasing function

Introduction to survival analysis 6

Page 9: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Examples of survival curves:

Introduction to survival analysis 7

Page 10: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

1.3 Estimation of survival curve

• As S(t) can be interpreted as a proportion, it can easily be estimated by theobserved proportion of subjects surviving time point t:

S(t) = P (Outcome > t) −→S(t) =

# subjects surviving t

N

• As an example, we estimate the survival curve for ovary cancer patients

• The following 6 event times were recorded:

1234 89 201 356 2970 456

Introduction to survival analysis 8

Page 11: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Calculations:

Time (t) # Surving t S(t)

0 6 6/6 = 1.00

30 6 6/6 = 1.00

89 5 5/6 = 0.83

100 5 5/6 = 0.83

201 4 4/6 = 0.67

356 3 3/6 = 0.50

400 3 3/6 = 0.50

556 2 2/6 = 0.33

1234 1 1/6 = 0.17

2970 0 0/6 = 0.00

Introduction to survival analysis 9

Page 12: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Graphically:

Introduction to survival analysis 10

Page 13: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Remarks:

. S(t) is estimated using a step function

. Steps only at the times where events were observed

. Step size at time point t:

# subjects with event at t

N. The estimate is right-continuous:

Introduction to survival analysis 11

Page 14: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

Chapter 2

Survival analysis with censoring

. The problem of censoring

. Example

. Kaplan-Meier estimate of survival curve

. Comparison of survival curves

. Examples from biomedical literature

Introduction to survival analysis 12

Page 15: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

2.1 The problem of censoring

Event time cannot always be measured !

⇓Censored observations

Various types of censoring:

. Right

. Left

. Interval

. Mixture of the above

Introduction to survival analysis 13

Page 16: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

No censoring

Time/Age

................................................................................................................................................................................................................................................

........................................

Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

Subject 6

Subject 7

Subject 8

: Before event : After event •: True event time ◦: Observations

••

••

••

••

◦◦

◦◦

◦◦

◦◦

Introduction to survival analysis 14

Page 17: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

Right censoring due to study end

Time/Age

................................................................................................................................................................................................................................................

........................................

Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

Subject 6

Subject 7

Subject 8

: Before event : After event •: True event time ◦: Observations

••

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.

End of studyIntroduction to survival analysis 15

Page 18: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

Right censoring due to dropout

Time/Age

................................................................................................................................................................................................................................................

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Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

Subject 6

Subject 7

Subject 8

: Before event : After event •: True event time ◦: Observations

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Introduction to survival analysis 16

Page 19: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

Left censoring due to late study onset

Time/Age

................................................................................................................................................................................................................................................

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Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

Subject 6

Subject 7

Subject 8

: Before event : After event •: True event time ◦: Observations

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Begin of studyIntroduction to survival analysis 17

Page 20: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

Interval censoring due to discrete observation times

Time/Age

................................................................................................................................................................................................................................................

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Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

Subject 6

Subject 7

Subject 8

: Before event : After event •: True event time ◦: Observations

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O B S E R V A T I O N T I M E SIntroduction to survival analysis 18

Page 21: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Our focus will be on right censoring, i.e., either the true event time or a lowerbound of it is observed

• Standard statistical tools for the analysis of censored observations assume randomcensoring:

Event time and censoring time are independent

• Counter examples:

. Patients entering the study later have a better prognosis due to increasedexperience of surgeon=⇒ Negative association between censoring and event time

. Patients leaving the study because they get worse=⇒ Positive association between censoring and event time

Introduction to survival analysis 19

Page 22: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

2.2 Example: Myelomatosis

• Peto et al. [3]; Allison [4] p.26

• Data on 25 patients diagnosed with myelomatosis (Kahler’s disease), multiplemalign tumours in the bone marrow

• Patients randomly assigned to two drug treatments

• Event time is the time from moment of randomization to death

• Some event times are censored due to study termination

• Patients with normal and patients with impaired renal functioning at moment ofrandomization

Introduction to survival analysis 20

Page 23: An introduction to survival analysis - KU Leuven...An introduction to survival analysis Geert Verbeke Interuniversity Institute for Biostatistics and statistical Bioinformatics, K.U.Leuven

• Data:

Treat Duration Status Renal Treat Duration Status Renal

1 8 1 1 2 180 1 0

1 852 0 0 2 632 1 0

1 52 1 1 2 2240 0 0

1 220 1 0 2 195 1 0

1 63 1 1 2 76 1 0

1 8 1 0 2 70 1 0

1 1976 0 0 2 13 1 1

1 1296 0 0 2 1990 0 0

1 1460 0 0 2 18 1 1

1 63 1 1 2 700 1 0

1 1328 0 0 2 210 1 0

1 365 0 0 2 1296 1 0

2 23 1 1

Status:

. 0: Censored

. 1: Death

Renal:

. 0: Normal

. 1: Impaired

• Interest is in estimating and comparing the survival curves for patients withdifferent treatments and for patients with different renal functioning at baseline

Introduction to survival analysis 21

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2.3 Kaplan-Meier estimate of survival curve

• Suppose interest is in estimating the survival curve for patients with treatment 1

• Observed data:

Duration: 8 852 52 220 63 8 1976 1296 1460 63 1328 365

Status: 1 0 1 1 1 1 0 0 0 1 0 0

How to account for the censoring ?

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• Simple ‘naive’ solutions:

. Ignoring the censored observations: Over-optimistic

. Treating censored observations as event times: Over-pessimistic

• Let N (t) denote the number of subjects surviving time t

• Terminology: N (t) is the number at risk

• A correct account of the censoring is based on the fact that the number ofsubjects surviving t is the proportion of people that survived t − ∆ and do not diein interval ]t − ∆; t]:

................................................................................................................

........................................] ]

N (t − ∆) N (t)

t − ∆ tTime:

# at risk:

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• Formally, in terms of numbers at risk:

N (t) = N (t − ∆) × P (Not dying in ]t − ∆; t])

= N (t − ∆) × {1 − P (Dying in ]t − ∆; t])}

• Formally, in terms of survival probabilities:

S(t) = S(t − ∆) × {1 − P (Dying in ]t − ∆; t])}

• Hence, the survival probability S(t) at time t can be calculated based on thesurvival probability at an earlier time point, t − ∆, and the probability of dying inbetween the two time points.

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S(t) = S(t − ∆) × {1 − P (Dying in ]t − ∆; t])}

• Starting from S(0) = 1, the above expression will allow calculation of S(t) for anylater time point t.

• We only need to estimate P (Dying in ]t − ∆; t])

• An obvious estimate is

# subjects dying in ]t − ∆; t])

N (t − ∆)

• However, this assumes that there are no censored observations between t− ∆ andt since, otherwise, the number of subjects dying between t − ∆ and t is unknown.

• This can be accomplished by taking ∆ sufficiently small

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• The estimate S(t) will then be obtained from

S(t) =

S(t − ∆) ×

1 −

# subjects dying in ]t − ∆; t])N(t − ∆)

• Note that S(t) = S(t − ∆) if no events were observed between t − ∆ and t

• Hence, as in the case without censoring, the estimate S(t) will be constant inintervals where no events were observed.

• As in the case without censoring, the estimate S(t) takes steps only at those timepoints where events were observed.

• We now apply this to estimate S(t) for the Myelomatosis data for patients giventhe first treatment

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S(t) = S(t − ∆) ×

1 −

# subjects dying in ]t − ∆; t])N(t − ∆)

Time t # at risk N(t) #Failures #Censored Proportion dying S(t)

0 12 0 0 0 1.00

5 12 0 0 0/12 1.00 ×(1 − 0/12) = 1.00

8 12 2 0 2/12 1.00 ×(1 − 2/12) = 0.83

10 10 0 0 0/10 0.83 ×(1 − 0/10) = 0.83

52 10 1 0 1/10 0.83 ×(1 − 1/10) = 0.75

63 9 2 0 2/9 0.75 ×(1 − 2/9) = 0.58

220 7 1 0 1/7 0.58 ×(1 − 1/7) = 0.50

300 6 0 0 0/6 0.50 ×(1 − 0/6) = 0.50

365 6 0 1 0/6 0.50 ×(1 − 0/6) = 0.50

500 5 0 0 0/5 0.50 ×(1 − 0/5) = 0.50

852 5 0 1 0/5 0.50 ×(1 − 0/5) = 0.50

1296 4 0 1 0/4 0.50 ×(1 − 0/4) = 0.50

1460 3 0 1 0/3 0.50 ×(1 − 0/3) = 0.50

1976 2 0 1 0/2 0.50 ×(1 − 0/2) = 0.50

1328 1 0 1 0/1 0.50 ×(1 − 0/1) = 0.50

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• The resulting estimate S(t) is the so-called Kaplan-Meier estimate:

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2.4 Comparison of survival curves

• Often, interest is in the comparison of survival curves of different groups

• For the Myelomatosis data, interest may be to compare survival between the twotreatment goups

• Also of interest is the comparison of survival for patients with impaired renalfunctioning with survival for patients with normal renal functioning.

• We will focuss on the comparison of two groups, but extensions are available forthe comparison of multiple groups

• For each group separately, the Kaplan-Meier estimate for the survival curve can becalculated.

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• Kaplan-Meier estimates for both treatment groups:

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• Kaplan-Meier estimates for patients with normal and impaired renal functioning,respectively:

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• Due to the censoring, classical tests such as t-test and Wilcoxon test cannot beused for the comparison of the survival times

• Various tests have been designed for the comparison of survival curves, whencensoring is present

• The most popular ones are:

. Logrank test

. Wilcoxon (Gehan) test

• The Logrank test has more power than Wilcoxon for detecting late differences

• The Logrank test has less power than Wilcoxon for detecting early differences

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• Test results:

Effect of treatment Effect of renal functioning

Logrank: p=0.2468

Wilcoxon: p=0.6260

Logrank: p=0.0029

Wilcoxon: p=0.0005

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2.5 Examples from biomedical literature

• Shatari et al. [5]:

. Methods, p.439:

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. Figure 1, p.440:

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• Blanchon et al. [6]:

. Statistical Methods, p.831:

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. Figure 2, p.834:

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Chapter 3

Regression for survival data

. Example

. Cox regression

. Application

. Model diagnostics

. Examples from biomedical literature

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3.1 Example: Pneumonia data

• Klein and Moeschberger [7] p.14

• Data on 3470 children, to study risk factors for time to hospitalized pneumonia

• Censoring after the first year of life, if not earlier

• Overall, 73 (2.10%) of the children were reported to be hospitalized forpneumonia within the first year of life.

• We want to study the association between the time to hospitalized pneumonia andsome child- and/or mother-specific characteristics

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• Note that the estimated probability of not experiencing hospitalized pneumonia is97.7%, somewhat smaller than the observed proportion 100 − 2.10 = 97.8%,obtained without correction for the early censoring.

• The relation with the following potential risk factors is to be studied:

. Age of mother (Years)

. Presence of siblings (Yes: 48%, No: 52%)

. Smoking status mother (Yes: 34%, No: 66%)

. Urban environment (Yes: 76%, No: 24%)

. Alcohol use mother (Yes: 36%, No: 64%)

. Poverty status mother (Yes: 36%, No: 74%)

. Normal birthweight child (≥ 5.5 pounds ≈ 2.5 kg. Yes: 92%, No: 8%)

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• The relation with factors can be studied using group-specific Kaplan-Meierestimates, together with Logrank and/or Wilcoxon tests

• Investigating the relation with covariates, requires a regression-type model

• Relating the outcome to several factors and/or covariates simultaneously requiresmultiple regression, ANOVA, or ANCOVA models

• The most frequently used model is the Cox (proportional hazards) model

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3.2 Cox regression

• Suppose interest is in studying the relation between the survival probality S(t) andsome covariate X

• Examples:

X = Age mother

X =

1 if mother smokes

0 if mother does not smoke

• Let S0(t) denote the survival function in case X = 0, and Sx(t) is the survivalfunction in case X = x, for a specific value x

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• The Cox regression model assumes that:

Sx(t) = {S0(t)}exp(βx)

• In case β > 0:

x ↗ =⇒ exp(βx) ↗ =⇒ Sx(t) < S0(t)

Higher X-values associated with increased risk for event

• In case β < 0:

x ↗ =⇒ exp(βx) ↘ =⇒ Sx(t) > S0(t)

Higher X-values associated with reduced risk for event

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• In case β > 0:

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• In case β < 0:

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3.3 Application: Pneumonia data

• Effects of all risk factors separately, assessed using simple Cox regression models:

Effect β p-value

Age of mother −0.0985 0.0275

Urban environment −0.4523 0.0695

Alcohol use −0.0535 0.8282

Normal birthweight child −0.2412 0.5439

Smoking of mother 0.7958 0.0007

Poverty of mother 0.5963 0.0109

Presence of siblings 0.6436 0.0079

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• Some evidence for positive effect (later event) of:

. Older mother

. Urban environment

• Some evidence for negative effect (earlier event) of:

. Smoking of mother

. Poverty of the mother

. Presence of siblings

• The positive effect of an urban environment is somewhat surprising and may beexplained by other characteristics to which it is related.

• Also the effect of poverty of the mother might be explained by other factors,related to poverty.

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• We therefore fit a multiple Cox model, assessing the effect of all covariates/factorssimultaneously:

Simple models Multiple model

Effect β p-value β p-value

Age of mother −0.0985 0.0275 −0.1287 0.0107

Urban environment −0.4523 0.0695 −0.3509 0.1616

Alcohol use −0.0535 0.8282 −0.1213 0.6374

Normal birthweight child −0.2412 0.5439 −0.0152 0.9697

Smoking of mother 0.7958 0.0007 0.7289 0.0028

Poverty of mother 0.5963 0.0109 0.2778 0.2586

Presence of siblings 0.6436 0.0079 0.7557 0.0042

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• There is no evidence anymore for an effect of living in an urban environment. Thiscan be explained from:

. There are significantly less smoking mothers (32.74%) in urban environmentscompared to rural environments (38.63%): Chi-squared test, p = 0.0018.

. There are significantly less siblings (46.95%) in urban environments comparedto rural environments (51.74%): Chi-squared test, p = 0.0158.

• There is no evidence anymore for an effect of poverty of the mother. This can beexplained from:

. There are significantly more smoking mothers (40.30%) in poor circumstancescompared to non-poor circumstances (30.69%): Chi-squared test, p < 0.0001.

. There are significantly more siblings (58.41%) with poor mothers compared tonon-poor mothers (42.30%): Chi-squared test, p < 0.0001.

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• To assess the effect of the risk factors, we compare survival when the risk factorsare present to survival when the risk factors are absent, keeping the othersconstant (rural environment, no alcohol use, normal birthweight, no poverty):

. Present: Mother 20yrs old, smoking, with other children

. Absent: Mother 30yrs old, not smoking, without other children

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3.4 Model diagnostics

• The Cox regression model assumes that:

Sx(t) = {S0(t)}exp(βx)

• Checking the above assumption is difficult since:

. The observations are not always observed (censoring)

. The ‘baseline’ survival curve S0(t) is left unspecified

• Most software packages do not include tools to check the assumption.

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3.5 Examples from biomedical literature

• Nawrot et al. [8]:

. Statistical analyses, p.122:

∗ Power analyses basedon logrank

∗ Cox regression to adjustfor potentially importantcovariates

∗ Sensitivity analyses to checkwhether results depend oncovariates included.

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. Figure 2, p.122:

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. Results, p.124:

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• Hutchins et al. [9]:

. End Point Definitions and Statistical Analysis, p.8315:

∗ OS: Overall survival

∗ DFS: Disease free survival

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. Results (p.8316), Figures 4 and 5 (p.8318):

Fig 5. Overall survival (OS) by hormone receptor (HR) status with and

without tamoxifen (TAM). HR�, HR positive; HR�, HR negative.

Fig 4. Disease-free survival (DFS) by hormone receptor (HR) status with

and without tamoxifen (TAM). HR�, HR positive; HR�, HR negative.

HR: postmenopausal hromone receptor positive; TAM: Tamoxifen group

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[2] D.J. Hand, F. Daly, A.D. Lunn, K.J. McConway, and E. Ostrowski. A handbook of small datasets. Chapman & Hall, first edition, 1989.

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[5] T. Shatari, M.A. Clark, T. Yamamoto, A. Menon, C. Keh, J.Alexander-Williams, and M. Keighley. Long strictureplasty is as safe andeffective as short strictureplasty in small-bowel crohn’s disease. Colorectal Disease, 6:438–441, 2004.

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[9] L.F. Hutchins, S.J. Green, P.M. Ravdin, D. Lew, S. Martino, M. Abeloff, A.P. Lyss, C. Allred, S.E. Rivkin, and C.K. Osborne. Randomized,controlled trial of Cyclophosphamide, Methotrexate, and Fluorouracil versus Cyclophosphamide, Doxorubicin, and Fluorouracil with andwithout Tamoxifen for high-risk, node-negative breast cancer: Treatment results of intergroup protocol int-0102. Journal of Clinical

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