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Cox proportional-hazards regression

Command:Statistics
Next selectSurvival analysis
Next selectCox proportional-hazards regression

Description

Cox regression (or Cox proportional hazards regression) is a statistical method to analyze the effect of several risk factors on survival, or in general on the time it takes for a specific event to happen.

The probability of the endpoint (death, or any other event of interest, e.g. recurrence of disease) is called the hazard. The hazard is modeled as:

Hazard formula (Cox regression)

where X1 ... Xk are a collection of predictor variables and H0(t) is the baseline hazard at time t, representing the hazard for a person with the value 0 for all the predictor variables.

By dividing both sides of the above equation by H0(t) and taking logarithms, we obtain:

Hazard ratio formula (Cox regression)

We call H(t) / H0(t) the hazard ratio. The coefficients bi...bk are estimated by Cox regression, and can be interpreted in a similar manner to that of multiple logistic regression.

Suppose the covariate (risk factor) is dichotomous and is coded 1 if present and 0 if absent. Then the quantity exp(bi) can be interpreted as the instantaneous relative risk of an event, at any time, for an individual with the risk factor present compared with an individual with the risk factor absent, given both individuals are the same on all other covariates.

Suppose the covariate is continuous, then the quantity exp(bi) is the instantaneous relative risk of an event, at any time, for an individual with an increase of 1 in the value of the covariate compared with another individual, given both individuals are the same on all other covariates.

Required input

Dialog box for Cox regression

Survival time: The name of the variable containing the time to reach the event of interest, or the time of follow-up.

Endpoint: The name of a variable containing codes 1 for the cases that reached the endpoint, or code 0 for the cases that have not reached the endpoint, either because they withdrew from the study, or the end of the study was reached. If your data are coded differently, you can use the Define status tool to recode your data.

Predictor variables: Names of variables that you expect to predict survival time.

The Cox proportional regression model assumes that the effects of the predictor variables are constant over time. Furthermore there should be a linear relationship between the endpoint and predictor variables. Predictor variables that have a highly skewed distribution may require logarithmic transformation to reduce the effect of extreme values. Logarithmic transformation of a variable var can be obtained by entering LOG(var) as predictor variable.

Filter: A filter to include only a selected subgroup of cases in the graph.

Options

Graph options

Results

In the example (taken from Bland, 2000), "survival time" is the time to recurrence of gallstones following dissolution (variable Time). Recurrence is coded in the variable Recurrence (1= yes, 0 =No). Predictor variables are Dis (= number of months previous gallstones took to dissolve), Mult (1 in case of multiple previous gallstones, 0 in case of single previous gallstones), and Diam (maximum diameter of previous gallstones).

Cox regression - results
Cox proportional-hazards regression

Survival time

Time

Endpoint

Recurrence

Method

Forward

Enter variable if P<

0.05

Remove variable if P>

0.1

Cases summary

Number of events a

39

27.08%

Number censored b

105

72.92%

Total number of cases

144

100.00%

a Recurrence = 1
b Recurrence = 0

Overall Model Fit

Null model -2 Log Likelihood

339.097

Full model -2 Log Likelihood

326.933

Chi-squared

12.164

DF

2

Significance level

P = 0.0023

Coefficients and Standard Errors

Covariate

b

SE

Wald

P

Exp(b)

95% CI of Exp(b)

Dis

0.04292

0.01657

6.7106

0.0096

1.0439

1.0105 to 1.0783

Mult

0.9635

0.3528

7.4599

0.0063

2.6208

1.3127 to 5.2326

Variables not included in the model

Diam

Baseline cumulative hazard function

 

Baseline

At mean of Covariates

Time

Cumulative Hazard

Cumulative Hazard

Survival

6

0.011

0.030

0.971

7

0.016

0.043

0.958

8

0.025

0.065

0.937

9

0.028

0.073

0.930

10

0.031

0.081

0.922

11

0.040

0.105

0.900

12

0.057

0.150

0.861

13

0.061

0.160

0.852

16

0.069

0.182

0.833

17

0.073

0.194

0.824

18

0.082

0.218

0.804

19

0.087

0.231

0.794

24

0.094

0.247

0.781

25

0.100

0.264

0.768

26

0.106

0.281

0.755

28

0.113

0.299

0.741

29

0.127

0.336

0.714

30

0.142

0.376

0.687

32

0.151

0.400

0.671

38

0.168

0.444

0.641

43

0.201

0.529

0.589

60

0.302

0.796

0.451

Concordance

Harrell's C-index

0.652

95% Confidence interval

0.560 to 0.744

Cases summary

This table shows the number of cases that reached the endpoint (Number of events), the number of cases that did not reach the endpoint (Number censored), and the total number of cases.

Overall Model Fit

The Chi-squared statistic tests the relationship between time and all the covariates in the model.

Coefficients and Standard Errors

Using the Forward selection method, the two covariates Dis and Mult were entered in the model which significantly (0.0096 for Dis and 0.0063 for Mult) contribute to the prediction of time.

MedCalc lists the regression coefficient b, its standard error, Wald statistic (b/SE)2, P value, Exp(b) and the 95% confidence interval for Exp(b).

Exp(b) and Hazard ratio

  • For a continuous covariate, Exp(b) is the increase of the hazard ratio for 1 unit change of the continuous variable.

    Note that when b is negative, then Exp(b) is less than 1 and Exp(b) is the decrease of the hazard ratio for 1 unit change of the continuous variable.

  • For a dichotomous covariate, Exp(b) is the hazard ratio.

The coefficient for months for dissolution (continuous variable Dis) is 0.0429. Exp(b) = Exp(0.0429) is 1.0439 (with 95% Confidence Interval 1.0107 to 1.0781), meaning that for an increase of 1 month to dissolution of previous gallstones, the hazard ratio for recurrence increases by a factor 1.04. For 2 months the hazard ratio increases by a factor 1.042.

The coefficient for multiple gallstones (dichotomous variable Mult) is 0.9335. Exp(b) = Exp(0.9635) is 2.6208, meaning that a case with previous gallstones is 2.6208 (with 95% Confidence Interval 1.3173 to 5.2141) more likely to have a recurrence than a case with a single stone.

Variables not included in the model

The variable Diam was found not to significantly contribute to the prediction of time, and was not included in the model.

Baseline cumulative hazard function

Finally, the program lists the baseline cumulative hazard H0(t), with the cumulative hazard and survival at mean of all covariates in the model.

The baseline cumulative hazard can be used to calculate the survival probability S(t) for any case at time t:

Baseline cumulative hazard (Cox regression)

where PI is a prognostic index:

Prognostic index (Cox regression)

Concordance

Harrell's C-index (Harrell et al., 1996), also known as the concordance index, is a goodness of fit measure for models which produce risk scores. See Park et al. (2021) for a detailed description.

Values of C near 1 indicate that the cox regression model is good at predicting which of 2 patients will take longer to present the event of interest. Values of C near 0.5 indicate that the model is no better than a coin flip in determining which patient will present the event of interest first. Values near 0 means that the model performs worse than a coin flip.

The confidence interval of Harrell's C-index is calculated using the modified τ method according to Pencina (2004).

Graph

The graph displays the survival curves for all categories of the categorical variable Mult (1 in case of multiple previous gallstones, 0 in case of single previous gallstones), and for mean values for all other covariates in the model.

Cox regression - survival curves

If no covariate was selected for Graph - Subgroups, or if the selected variable was not included in the model, then the graph displays a single survival curve at mean of all covariates in the model.

Sample size considerations

Based on the work of Peduzzi et al. (1995) the following guideline for a minimum number of cases to include in a study can be suggested.

Let p be the smallest of the proportions of positive cases (cases that reached the endpoint) and negative cases (cases that did not reach the endpoint) in the population and k the number of predictor variables, then the minimum number of cases to include is:

N = 10 k / p

For example: you have 3 predictor variables to include in the model and the proportion of positive cases in the population is 0.20 (20%). The minimum number of cases required is

N = 10 x 3 / 0.20 = 150

If the resulting number is less than 100 you should increase it to 100 as suggested by Long (1997).

Literature

See also