Cox regression ii
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Cox Regression II. Kristin Sainani Ph.D. http://www.stanford.edu/~kcobb Stanford University Department of Health Research and Policy. Topics . Stratification Age as time scale Residuals Repeated events Intention-to-treat analysis for RCTs. 1. Stratification .

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Cox Regression II

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Cox regression ii

Cox Regression II

Kristin Sainani Ph.D.http://www.stanford.edu/~kcobbStanford UniversityDepartment of Health Research and Policy


Topics

Topics

  • Stratification

  • Age as time scale

  • Residuals

  • Repeated events

  • Intention-to-treat analysis for RCTs


Cox regression ii

1. Stratification

  • Violations of PH assumption can be resolved by:

    • Adding time*covariate interaction

    • Adding other time-dependent version of the covariate

    • Stratification


Cox regression ii

Stratification

  • Different stratum are allowed to have different baseline hazard functions.

  • Hazard functions do not need to be parallel between different stratum.

  • Essentially results in a “weighted” hazard ratio being estimated: weighted over the different strata.

  • Useful for “nuisance” confounders (where you do not care to estimate the effect).

  • Does not allow you to evaluate interaction or confounding of stratification variable (will miss possible interactions).


Cox regression ii

Example: stratify on gender

  • Males: 1, 3, 4, 10+, 12, 18 (subjects 1-6)

  • Females: 1, 4, 5, 9+ (subjects 7-10)


The pl

The PL


Cox regression ii

2. Using age as the time-scale

in Cox Regression

  • Age is a common confounder in Cox Regression, since age is strongly related to death and disease.

  • You may control for age by adding baseline age as a covariate to the Cox model.

  • A better strategy for large-scale longitudinal surveys, such as NHANES, is to use age as your time-scale (rather than time-in-study).

  • You may additionally stratify on birth cohort to control for cohort effects.


Age as time scale

Age as time-scale

  • The risk set becomes everyone who was at risk at a certain age rather than at a certain event time.

  • The risk set contains everyone who was still event-free at the age of the person who had the event.

  • Requires enough people at risk at all ages (such as in a large-scale, longitudinal survey).


The likelihood with age as time

The likelihood with age as time

Event times: 3, 5, 7+, 12, 13+ (years-in-study)

Baseline ages: 28, 25, 40, 29, 30 (years)

Age at event or censoring: 31, 30, 47+, 41, 43+


3 residuals

3. Residuals

  • Residuals are used to investigate the lack of fit of a model to a given subject.

  • For Cox regression, there’s no easy analog to the usual “observed minus predicted” residual of linear regression


Martingale residual

Martingale residual

  • ci (1 if event, 0 if censored) minus the estimated cumulative hazard to ti (as a function of fitted model) for individual i:

    ci-H(ti,Xi,ßi)

    • E.g., for a subject who was censored at 2 months, and whose predicted cumulative hazard to 2 months was 20%

      • Martingale=0-.20 = -.20

    • E.g., for a subject who had an event at 13 months, and whose predicted cumulative hazard to 13 months was 50%:

      • Martingale=1-.50 = +.50

  • Gives excess failures.

  • Martingale residuals are not symmetrically distributed, even when the fitted model is correctly, so transform to deviance residuals...


  • Deviance residuals

    Deviance Residuals

    • The deviance residual is a normalized transform of the martingale residual. These residuals are much more symmetrically distributed about zero.

    • Observations with large deviance residuals are poorly predicted by the model.


    Deviance residuals1

    Deviance Residuals

    • Behave like residuals from ordinary linear regression

    • Should be symmetrically distributed around 0 and have standard deviation of 1.0.

    • Negative for observations with longer than expected observed survival times.

    • Plot deviance residuals against covariates to look for unusual patterns.


    Deviance residuals2

    Deviance Residuals

    • In SAS, option on the output statement:

      Output out=outdata resdev=Varname

      **Cannot get diagnostics in SAS if time-dependent covariate in the model


    Example uis data

    Out of 628 observations, a few in the range of 3-SD is not unexpected

    Example: uis data

    Pattern looks fairly symmetric around 0.


    Example uis data1

    What do you think this cluster represents?

    Example: uis data


    Example censored only

    Example: censored only


    Example had event only

    Example: had event only


    Schoenfeld residuals

    Schoenfeld residuals

    • Schoenfeld (1982) proposed the first set of residuals for use with Cox regression packages

      • Schoenfeld D. Residuals for the proportional hazards regresssion model. Biometrika, 1982, 69(1):239-241.

    • Instead of a single residual for each individual, there is a separate residual for each individual for each covariate

    • Note: Schoenfeld residuals are not defined for censored individuals.


    Schoenfeld residuals1

    The person who died was 56; based on the fitted model, how likely is it that the person who died was 56 rather than older?

    Schoenfeld residuals

    • The Schoenfeld residual is defined as the covariate value for the individual that failed minus its expected value. (Yields residuals for each individual who failed, for each covariate).

    • Expected value of the covariate at time ti = a weighted-average of the covariate, weighted by the likelihood of failure for each individual in the risk set at ti.


    Example

    Example

    • 5 people left in our risk set at event time=7 months:

      • Female 55-year old smoker

      • Male 45-year old non-smoker

      • Female 67-year old smoker

      • Male 58-year old smoker

      • Male 70-year old non-smoker

        The 55-year old female smoker is the one who has the event…


    Example1

    Example

    Based on our model, we can calculate a predicted probability of death by time 7 for each person (call it “p-hat”):

    • Female 55-year old smoker: p-hat=.10

    • Male 45-year old non-smoker : p-hat=.05

    • Female 67-year old smoker : p-hat=.30

    • Male 58-year old smoker : p-hat=.20

    • Male 70-year old non-smoker : p-hat=.30

      Thus, the expected value for the AGE of the person who failed is:

      55(.10) + 45 (.05) + 67(.30) + 58 (.20) + 70 (.30)= 60

      And, the Schoenfeld residual is: 55-60 = -5


    Example2

    Example

    Based on our model, we can calculate a predicted probability of death by time 7 for each person (call it “p-hat”):

    • Female 55-year old smoker: p-hat=.10

    • Male 45-year old non-smoker : p-hat=.05

    • Female 67-year old smoker : p-hat=.30

    • Male 58-year old smoker : p-hat=.20

    • Male 70-year old non-smoker : p-hat=.30

      The expected value for the GENDER of the person who failed is:

      0(.10) + 1(.05) + 0(.30) + 1 (.20) + 1 (.30)= .55

      And, the Schoenfeld residual is: 0-.55 = -.55


    Schoenfeld residuals2

    Schoenfeld residuals

    • Since the Schoenfeld residuals are, in principle, independent of time, a plot that shows a non-random pattern against time is evidence of violation of the PH assumption.

      • Plot Schoenfeld residuals against time to evaluate PH assumption

      • Regress Schoenfeld residuals against time to test for independence between residuals and time.


    Example no pattern with time

    Example: no pattern with time


    Example violation of ph

    Example: violation of PH


    Schoenfeld residuals3

    Schoenfeld residuals

    In SAS:

    option on the output statement:

    Output out=outdata ressch= Covariate1 Covariate2 Covariate3


    Summary of the many ways to evaluate ph assumption

    Summary of the many ways to evaluate PH assumption…

    1. Examine log(-log(S(t)) plots

    PH assumption is supported by parallel lines and refuted by lines that cross or nearly cross

    Must use categorical predictors or categories of a continuous predictor

    2. Include interaction with time in the model

    PH assumption is supported by non-significant interaction coefficient and refuted by significant interaction coefficient

    Retaining the interaction term in the model corrects for the violation of PH

    Don’t complicate your model in this way unless it’s absolutely necessary!

    3. Plot Schoenfeld residuals

    PH assumption is supported by a random pattern with time and refuted by a non-random pattern

    4. Regress Schoenfeld residuals against time to test for independence between residuals and time.

    PH assumption is supported by a non-significant relationship between residuals and time, and refuted by a significant relationship


    Cox regression ii

    4. Repeated events

    • Death (presumably) can only happen once, but many outcomes could happen twice…

      • Fractures

      • Heart attacks

      • Pregnancy

        Etc…


    Cox regression ii

    Repeated events: 1

    • Strategy 1: run a second Cox regression (among those who had a first event) starting with first event time as the origin

    • Repeat for third, fourth, fifth, events, etc.

      • Problems: increasingly smaller and smaller sample sizes.


    Cox regression ii

    Repeated events: Strategy 2

    • Treat each interval as a distinct observation, such that someone who had 3 events, for example, gives 3 observations to the dataset

      • Major problem: dependence between the same individual


    Cox regression ii

    Strategy 3

    • Stratify by individual (“fixed effects partial likelihood”)

    • In PROC PHREG: strata id;

      • Problems:

      • does not work well with RCT data

      • requires that most individuals have at least 2 events

      • Can only estimate coefficients for those covariates that vary across successive spells for each individual; this excludes constant personal characteristics such as age, education, gender, ethnicity, genotype


    5 considerations when analyzing data from an rct

    5. Considerations when analyzing data from an RCT…


    Intention to treat analysis

    Intention-to-Treat Analysis

    Intention-to-treat analysis: compare outcomes according to the groups to which subjects were initially assigned, regardless of which intervention they actually received.

    Evaluates treatment effectiveness rather than treatment efficacy


    Why intention to treat

    Why intention to treat?

    • Non-intention-to-treat analyses lose the benefits of randomization, as the groups may no longer be balanced with regards to factors that influence the outcome.

    • Intention-to-treat analysis simulates “real life,” where patients often don’t adhere perfectly to treatment or may discontinue treatment altogether.


    Drop ins and drop outs example whi

    Both women on placebo and women on active treatment discontinued study medications.

    Women on placebo “dropped in” to treatment because their regular doctors put them on hormones (dogma= “hormones are good”).

    Women on treatment “dropped in” to treatment because their doctors took them off study drugs and put them on hormones to insure they were on hormones and not placebo.

    Drop-ins and Drop-outs: example, WHI

    Women’s Health Initiative Writing Group. JAMA. 2002;288:321-333.


    Effect of intention to treat on the statistical analysis

    Effect of Intention to treat on the statistical analysis

    • Intention-to-treat analyses tend to underestimate treatment effects; increased variability “waters down” results.


    Example3

    Example

    Take the following hypothetical RCT:

    Treated subjects have a 25% chance of dying during the 2-year study vs. placebo subjects have a 50% chance of dying.

    TRUE RR= 25%/50% = .50 (treated have 50% less chance of dying)

    You do a 2-yr RCT of 100 treated and 100 placebo subjects.

    If nobody switched, you would see about 25 deaths in the treated group and about 50 deaths in the placebo group (give or take a few due to random chance).

    Observed RR .50


    Example continued

    Example, continued

    BUT, if early in the study, 25 treated subjects switch to placebo and 25 placebo subjects switch to control.

    You would see about

    25*.25 + 75*.50 = 43-44 deaths in the placebo group

    And about

    25*.50 + 75*.25 = 31 deaths in the treated group

    Observed RR = 31/44  .70

    Diluted effect!


    References

    References

    Paul Allison. Survival Analysis Using SAS. SAS Institute Inc., Cary, NC: 2003.


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