Model checking and residuals

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Residual diagnostics help you decide whether to trust a fitted model, add covariates, change the regression family (PH vs PO), or simplify the Bernstein degree.

Before you model

library(spsurv)
library(survival)
library(ggplot2)
data(veteran)
veteran$celltype <- factor(veteran$celltype)

eda <- data.frame(
  n = nrow(veteran),
  events = sum(veteran$status),
  censored = sum(1 - veteran$status),
  pct_censored = round(100 * mean(1 - veteran$status), 1)
)
eda
#>     n events censored pct_censored
#> 1 137    128        9          6.6

km_all <- survfit(Surv(time, status) ~ 1, data = veteran)
km_df <- data.frame(
  time = km_all$time,
  surv = km_all$surv
)
ggplot(km_df, aes(x = time, y = surv)) +
  geom_step(linewidth = 0.6) +
  labs(x = "Time (days)", y = "Survival probability", title = "Overall KM") +
  theme_bw()

Kaplan-Meier survival for the veteran data (overall).

Why this is insightful

The overall KM and event rate tell you how much information is available for baseline estimation. Heavy censoring or a short follow-up window limits how far you can extrapolate smooth Bernstein baselines.

What to decide

Low event count relative to Bernstein degree → start with smaller m (see the Bernstein polynomial degree vignette).
Steep early drop in KM → expect a flexible baseline; check diagnostics after fit.

Fit and summarize

fit <- bpph(
  Surv(time, status) ~ karno + celltype,
  degree = 5,
  data = veteran,
  approach = "mle",
  init = 0
)
summary(fit)
#> Call:
#> bpph(formula = Surv(time, status) ~ karno + celltype, degree = 5, 
#>     data = veteran, approach = "mle", init = 0, model = "ph")
#> 
#> Bernstein PH model: 
#> Regression coefficients:
#>                   Estimate    2.5%   97.5% Std. Error z value Pr(>|z|)    
#> karno              -0.0306 -0.0406 -0.0206     0.0051    -6.0    2e-09 ***
#> celltypesmallcell   0.7380  0.2458  1.2303     0.2511     2.9    0.003 ** 
#> celltypeadeno       1.1536  0.5800  1.7272     0.2927     3.9    8e-05 ***
#> celltypelarge       0.3302 -0.2110  0.8714     0.2761     1.2    0.232    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Exponentiated coefficients:
#>                   Estimate 2.5% 97.5%
#> karno                 0.97 0.96   1.0
#> celltypesmallcell     2.09 1.28   3.4
#> celltypeadeno         3.17 1.79   5.6
#> celltypelarge         1.39 0.81   2.4
#> 
#> --- 
#> loglik = -716   AIC = 1450

Use MLE plug-in fits for residual plots (Bayesian fits average over draws; see note at the end).

Visual check — martingale residuals

Martingale residuals should scatter around zero without a strong trend against fitted cumulative hazard.

mart <- residuals(fit, type = "martingale")
csnell <- residuals(fit, type = "cox-snell")
fitted_ch <- csnell

mart_df <- data.frame(
  fitted = fitted_ch,
  martingale = mart,
  status = factor(veteran$status),
  celltype = veteran$celltype
)
ggplot(mart_df, aes(x = fitted, y = martingale, color = status)) +
  geom_point(alpha = 0.6, size = 1.8) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey40") +
  geom_smooth(aes(group = 1), method = "loess", se = FALSE, color = "black", linewidth = 0.5) +
  coord_cartesian(ylim = c(-4, 2)) +
  labs(
    x = "Fitted cumulative hazard (Cox-Snell)",
    y = "Martingale residual",
    color = "Event"
  ) +
  theme_bw() +
  theme(legend.position = "bottom")

Martingale residuals vs fitted cumulative hazard (Cox-Snell).

ggplot(mart_df, aes(x = fitted, y = martingale)) +
  geom_point(alpha = 0.6, size = 1.5) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey40") +
  geom_smooth(method = "loess", se = FALSE, color = "black", linewidth = 0.5) +
  facet_wrap(~ celltype, scales = "free_x") +
  coord_cartesian(ylim = c(-4, 2)) +
  labs(x = "Fitted cumulative hazard", y = "Martingale residual") +
  theme_bw()

Martingale residuals by cell type.

Why this is insightful

A systematic trend in the LOESS smooth (e.g. upward curve at high fitted values) suggests covariate or baseline misspecification. Faceting reveals subgroups where the model fits poorly.

What to decide

Pattern	Suggested action
Clear trend vs fitted CH	Add terms, try PO (`bppo`), or lower/increase degree
One facet behaves differently	Consider interaction or separate analysis
Flat scatter around zero	Proceed; still check Cox-Snell calibration

Visual check — Cox-Snell calibration

Under a well-specified model, the Kaplan-Meier of exp(-CS) should track the Exponential(1) reference (45-degree line on a cumulative-hazard scale).

km_cs <- survfit(Surv(csnell, veteran$status) ~ 1)
cs_df <- data.frame(
  cs = km_cs$time,
  cumhaz = -log(pmax(km_cs$surv, .Machine$double.eps))
)
ggplot(cs_df, aes(x = cs, y = cumhaz)) +
  geom_step(linewidth = 0.6) +
  geom_abline(slope = 1, intercept = 0, color = "steelblue", linetype = "dashed") +
  labs(
    x = "Cox-Snell residual",
    y = "Nelson-Aalen cumulative hazard of exp(-CS)",
    title = "Cox-Snell calibration"
  ) +
  theme_bw()

Cox-Snell calibration: KM of exp(-CS) vs Exponential(1) reference.

Why this is insightful

Curvature above the reference line indicates overall miscalibration of the cumulative hazard (baseline too low or too high on average). This is a global check complementary to martingale trends.

What to decide

Systematic departure from the line → revisit Bernstein degree or model family.
Good calibration but bad martingale trend → local misspecification (covariates).

Visual check — deviance residuals

Large deviance residuals flag observations poorly explained by the fitted model.

dev <- residuals(fit, type = "deviance")
dev_df <- data.frame(
  index = seq_along(dev),
  deviance = dev,
  highlight = abs(dev) >= quantile(abs(dev), 0.95)
)
ggplot(dev_df, aes(x = index, y = deviance, color = highlight)) +
  geom_point(size = 1.8, alpha = 0.7) +
  scale_color_manual(values = c("FALSE" = "grey40", "TRUE" = "firebrick"), guide = "none") +
  labs(x = "Observation index", y = "Deviance residual") +
  theme_bw()

Deviance residuals (largest magnitudes highlighted).

What to decide

Few extreme points → investigate those subjects; consider robustness of conclusions.
Many extremes → global lack of fit; revisit model structure.

Compare with Cox (reference)

cox_fit <- coxph(Surv(time, status) ~ karno + celltype, data = veteran)
mart_cox <- residuals(cox_fit, type = "martingale")
ggplot(data.frame(bp = mart, cox = mart_cox), aes(x = bp, y = cox)) +
  geom_point(alpha = 0.5, size = 1.8) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey40") +
  labs(x = "spsurv martingale", y = "Cox martingale") +
  theme_bw()

spsurv vs Cox martingale residuals.

Disagreement is expected in general (different likelihoods and baselines). Large outliers in the comparison warrant case-level review.

Gamma information stability

v <- vcov(fit, bp.param = TRUE)
stab <- data.frame(
  gamma_information_stable = attr(v, "gamma_information_stable"),
  kappa_gamma = signif(attr(v, "gamma_information_kappa"), 4)
)
stab
#>   gamma_information_stable kappa_gamma
#> 1                     TRUE      217000

What to decide

gamma_information_stable == FALSE → lower Bernstein degree; treat survfit() standard errors and bands as unreliable until stability improves.

Bayesian fits

For approach = "bayes", residuals() uses posterior-mean cumulative hazards. For diagnostic plots comparable to classical methods, refit with MLE or use posterior predictive checks outside this vignette.

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