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I have created a Cox model for lung adenocarcinoma patients. Several variables make up the model and I have assessed whether or not the proportional hazards assumption holds.

Using the cox.zph function, I have found that one of my variables (a biomarker) returns a p-value <0.05. The p-value for the global model also violates the proportional hazards assumption.

Checking a plot of the Schoenfeld residuals, I can see that my biomarker violates the assumption most significantly in the first 24 months of the observation period. I plan to include an interaction with time.

Could somebody please explain to me, however, what exactly I am looking at when plotting Schoenfeld residuals? I can't seem to find a clear, novice explanation anywhere. enter image description here

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1 Answer 1

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What's plotted starts with a variance-weighted transformation of the Schoenfeld residuals for a covariate, into what are called "scaled Schoenfeld residuals." Those are then added to the corresponding time-invariant coefficient estimate from the Cox model under the proportional hazards (PH) assumption and smoothed. The result is a plot of an estimate of the regression coefficient for the covariate over time. If the plot is reasonably flat, the PH assumption holds. Take this one step at a time.

Risk-weighted covariate averages and covariances

You start by determining, for each event time, the risk-weighted averages of covariate values and the corresponding risk-weighted covariance among covariate values over all individuals at risk at that time. That's essentially a part of the model-fitting process, anyway. The risks used for the weighting are simply the corresponding hazard ratios for the individuals at that time, the exponentiated linear-predictor values from the model.

Schoenfeld residuals

The Schoenfeld residuals are calculated for all covariates for each individual experiencing an event at a given time. Those are the differences between that individual's covariate values at the event time and the corresponding risk-weighted average of covariate values among all those then at risk. The word "residual" thus makes sense, as it's the difference between an observed covariate value and what you might have expected based on all those at risk at that time.

Scaled Schoenfeld residuals

The Schoenfeld residuals are then scaled inversely with respect to their (co)variances. The scaled values at an event time for an individual come from pre-multiplying the vector of original Schoenfeld residuals by the inverse of the corresponding risk-weighted covariate covariance matrix at that time.* You can think of this as down-weighting Schoenfeld residuals whose values are uncertain because of high variance.

The "plot of scaled Schoenfeld residuals"

Although the plot you show is generally called a "plot of scaled Schoenfeld residuals," that's not quite right.

The importance of the scaled Schoenfeld residuals comes from their associations with the time-dependence of a Cox regression coefficient. If $s_{k,j}^*$ is a scaled Schoenfeld residual for covariate $j$ at time $t_k$ and the estimated time-fixed Cox regression coefficient under PH is $\hat \beta_j$, then the expected value of $s_{k,j}^*$ is approximately the deviation of the actual coefficient value at time $t_k$, $\beta_j(t_k)$, from the PH-based estimate:

$$E(s_{k,j}^*) + \hat \beta_j \approx \beta_j(t_k).$$

That was shown by Grambsch and Therneau in 1994. The y-axis values of the plot for covariate $j$ are the sums of the scaled Schoenfeld residuals with the corresponding PH estimate $\hat \beta_j$.

Simple answer to the question

The smoothed plot is thus an estimate of the time dependence of the coefficient for the covariate $j$, $\beta_j(t_k)$. In your case, the plot indicates that your biomarker is most strongly associated with outcome at early times, dropping off to almost no association beyond a time value of 50-60.

The above is pretty much based on Therneau and Grambsch. Section 6.2 presents the plotting of scaled Schoenfeld residuals, with ordinary Schoenfeld residuals described in Section 4.6 and the formulas for risk-weighted covariate means and covariances in Section 3.1 (equations 3.5 and 3.7, respectively).


*The code in residuals.coxph() makes a simplifying assumption that the risk-weighted covariate covariance matrix is constant in time, and uses the average value. The inverse of that average risk-weighted covariate covariance matrix is then (conveniently) just the product of the coefficient covariance matrix times the number of events.

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  • $\begingroup$ thank you so much for such a thorough explanation. Although some of the answer went over my head, it gives me something to get my teeth into! :) $\endgroup$
    – user333336
    Commented Oct 8, 2021 at 17:48
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    $\begingroup$ @user333336 I'm not sure that I completely "got it" until I tried to answer this question. For most purposes, the "simple answer" should suffice, the rest just says why it's true. $\endgroup$
    – EdM
    Commented Oct 8, 2021 at 18:09
  • $\begingroup$ Hi @EdM you wrote this "You start by determining, for each event time, the risk-weighted averages of covariate values and the corresponding risk-weighted covariance among covariate values over all individuals at risk at that time". When calculating the risk-weighted averages for time 10 fx and let us assume two got the event at time 10, and five did not. Does this mean that the total risk are the sum of the RS for the five individuals who did not get the event at time 10? or is it the total risk = five+two=7? I want to understand who is part of the "over all individuals at risk at that time" $\endgroup$
    – Devi Sita
    Commented Jun 12 at 11:42
  • $\begingroup$ RS = risk scores $\endgroup$
    – Devi Sita
    Commented Jun 12 at 11:44
  • $\begingroup$ @DeviSita you can think of the risk set for an event time as being all those still under observation immediately prior to that event time. Thus it includes both those who have the event at that time and those who don't. In your example, that's all 7 individuals. $\endgroup$
    – EdM
    Commented Jun 12 at 15:56

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