Proportional hazards tests and diagnostics based on weighted residuals

PM Grambsch, TM Therneau - Biometrika, 1994 - academic.oup.com
PM Grambsch, TM Therneau
Biometrika, 1994academic.oup.com
Nonproportional hazards can often be expressed by extending the Cox model to include
time varying coefficients; eg, for a single covariate, the hazard function for subject i is
modelled as exp {β (t) Zi (t)}. A common example is a treatment effect that decreases with
time. We show that the function βi (t) can be directly visualized by smoothing an appropriate
residual plot. Also, many tests of proportional hazards, including those of Cox (1972), Gill &
Schumacher (1987), Harrell (1986), Lin (1991), Moreau, O'Quigley & Mesbah (1985) …
SUMMARY
Nonproportional hazards can often be expressed by extending the Cox model to include time varying coefficients; e.g., for a single covariate, the hazard function for subject i is modelled as exp {β(t)Zi(t)}. A common example is a treatment effect that decreases with time. We show that the function βi(t) can be directly visualized by smoothing an appropriate residual plot. Also, many tests of proportional hazards, including those of Cox (1972), Gill & Schumacher (1987), Harrell (1986), Lin (1991), Moreau, O'Quigley & Mesbah (1985), Nagelkerke, Oosting & Hart (1984), O'Quigley & Pessione (1989), Schoenfeld (1980) and Wei (1984) are related to time-weighted score tests of the proportional hazards hypothesis, and can be visualized as a weighted least-squares line fitted to the residual plot.
Oxford University Press