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Flexible parametric modelling of time-to-event data using the spline model of Royston and Parmar (2002).


  k = 0,
  knots = NULL,
  bknots = NULL,
  scale = "hazard",
  timescale = "log",
  spline = "rp",



A formula expression in conventional R linear modelling syntax. The response must be a survival object as returned by the Surv function, and any covariates are given on the right-hand side. For example,

Surv(time, dead) ~ age + sex

specifies a model where the log cumulative hazard (by default, see scale) is a linear function of the covariates age and sex.

If there are no covariates, specify 1 on the right hand side, for example Surv(time, dead) ~ 1.

Time-varying covariate effects can be specified using the method described in flexsurvreg for placing covariates on ancillary parameters. The ancillary parameters here are named gamma1, ..., gammar where r is the number of knots k plus one (the ``degrees of freedom'' as defined by Royston and Parmar). So for the default Weibull model, there is just one ancillary parameter gamma1.

Therefore a model with one internal spline knot, where the equivalents of the Weibull shape and scale parameters, but not the higher-order term gamma2, vary with age and sex, can be specified as:

Surv(time, dead) ~ age + sex + gamma1(age) + gamma1(sex)

or alternatively (and more safely, see flexsurvreg)

Surv(time, dead) ~ age + sex, anc=list(gamma1=~age + sex)

Surv objects of type="right","counting", "interval1" or "interval2" are supported, corresponding to right-censored, left-truncated or interval-censored observations.


A data frame in which to find variables supplied in formula. If not given, the variables should be in the working environment.


Optional variable giving case weights.


Optional variable giving expected hazards for relative survival models.


Optional variable giving individual right-truncation times (see flexsurvreg). Note that these models can suffer from weakly identifiable parameters and badly-behaved likelihood functions, and it is advised to compare convergence for different initial values by supplying different inits arguments to flexsurvspline.


Vector of integers or logicals specifying the subset of the observations to be used in the fit.


Number of knots in the spline. The default k=0 gives a Weibull, log-logistic or lognormal model, if "scale" is "hazard", "odds" or "normal" respectively. k is equivalent to df-1 in the notation of stpm for Stata. The knots are then chosen as equally-spaced quantiles of the log uncensored survival times, for example, at the median with one knot, or at the 33% and 67% quantiles of log time (or time, see "timescale") with two knots. To override this default knot placement, specify knots instead.


Locations of knots on the axis of log time (or time, see "timescale"). If not specified, knot locations are chosen as described in k above. Either k or knots must be specified. If both are specified, knots overrides k.


Locations of boundary knots, on the axis of log time (or time, see "timescale"). If not supplied, these are are chosen as the minimum and maximum log death time.


If "hazard", the log cumulative hazard is modelled as a spline function.

If "odds", the log cumulative odds is modelled as a spline function.

If "normal", \(-\Phi^{-1}(S(t))\) is modelled as a spline function, where \(\Phi^{-1}()\) is the inverse normal distribution function qnorm.


If "log" (the default) the log cumulative hazard (or alternative) is modelled as a spline function of log time. If "identity", it is modelled as a spline function of time, however this model would not satisfy the desirable property that the cumulative hazard (or alternative) should approach 0 at time zero.


"rp" to use the natural cubic spline basis described in Royston and Parmar.

"splines2ns" to use the alternative natural cubic spline basis from the splines2 package (Wang and Yan 2021), which may be better behaved due to the basis being orthogonal.


Any other arguments to be passed to or through flexsurvreg, for example, anc, inits, fixedpars, weights, subset, na.action, and any options to control optimisation. See flexsurvreg.


A list of class "flexsurvreg" with the same elements as described in flexsurvreg, and including extra components describing the spline model. See in particular:


Number of knots.


Location of knots on the log time axis.


The scale of the model, hazard, odds or normal.


Matrix of maximum likelihood estimates and confidence limits. Spline coefficients are labelled "gamma...", and covariate effects are labelled with the names of the covariates.

Coefficients gamma1,gamma2,... here are the equivalent of s0,s1,... in Stata streg, and gamma0 is the equivalent of the xb constant term. To reproduce results, use the noorthog option in Stata, since no orthogonalisation is performed on the spline basis here.

In the Weibull model, for example, gamma0,gamma1 are -shape*log(scale), shape respectively in dweibull or flexsurvreg notation, or (-Intercept/scale, 1/scale) in survreg notation.

In the log-logistic model with shape a and scale b (as in eha::dllogis from the eha package), 1/b^a is equivalent to exp(gamma0), and a is equivalent to gamma1.

In the log-normal model with log-scale mean mu and standard deviation sigma, -mu/sigma is equivalent to gamma0 and 1/sigma is equivalent to gamma1.


The maximised log-likelihood. This will differ from Stata, where the sum of the log uncensored survival times is added to the log-likelihood in survival models, to remove dependency on the time scale.


This function works as a wrapper around flexsurvreg by dynamically constructing a custom distribution using dsurvspline, psurvspline and unroll.function.

In the spline-based survival model of Royston and Parmar (2002), a transformation \(g(S(t,z))\) of the survival function is modelled as a natural cubic spline function of log time \(x = \log(t)\) plus linear effects of covariates \(z\).

$$g(S(t,z)) = s(x, \bm{\gamma}) + \bm{\beta}^T \mathbf{z}$$

The proportional hazards model (scale="hazard") defines \(g(S(t,\mathbf{z})) = \log(-\log(S(t,\mathbf{z}))) = \log(H(t,\mathbf{z}))\), the log cumulative hazard.

The proportional odds model (scale="odds") defines \(g(S(t,\mathbf{z})) \)\( = \log(S(t,\mathbf{z})^{-1} - 1)\), the log cumulative odds.

The probit model (scale="normal") defines \(g(S(t,\mathbf{z})) = \)\( -\Phi^{-1}(S(t,\mathbf{z}))\), where \(\Phi^{-1}()\) is the inverse normal distribution function qnorm.

With no knots, the spline reduces to a linear function, and these models are equivalent to Weibull, log-logistic and lognormal models respectively.

The spline coefficients \(\gamma_j: j=1, 2 \ldots \), which are called the "ancillary parameters" above, may also be modelled as linear functions of covariates \(\mathbf{z}\), as

$$\gamma_j(\mathbf{z}) = \gamma_{j0} + \gamma_{j1}z_1 + \gamma_{j2}z_2 + ... $$

giving a model where the effects of covariates are arbitrarily flexible functions of time: a non-proportional hazards or odds model.

Natural cubic splines are cubic splines constrained to be linear beyond boundary knots \(k_{min},k_{max}\). The spline function is defined as

$$s(x,\bm{\gamma}) = \gamma_0 + \gamma_1 x + \gamma_2 v_1(x) + \ldots + $$$$ \gamma_{m+1} v_m(x)$$

where \(v_j(x)\) is the \(j\)th basis function

$$v_j(x) = (x - k_j)^3_+ - \lambda_j(x - k_{min})^3_+ - (1 - $$$$ \lambda_j) (x - k_{max})^3_+$$

$$\lambda_j = \frac{k_{max} - k_j}{k_{max} - k_{min}}$$

and \((x - a)_+ = max(0, x - a)\).


Royston, P. and Parmar, M. (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine 21(1):2175-2197.

Wang W, Yan J (2021). Shape-Restricted Regression Splines with R Package splines2. Journal of Data Science, 19(3), 498-517.

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

See also

flexsurvreg for flexible survival modelling using general parametric distributions.

plot.flexsurvreg and lines.flexsurvreg to plot fitted survival, hazards and cumulative hazards from models fitted by flexsurvspline and flexsurvreg.


Christopher Jackson <>


## Best-fitting model to breast cancer data from Royston and Parmar (2002)
## One internal knot (2 df) and cumulative odds scale

spl <- flexsurvspline(Surv(recyrs, censrec) ~ group, data=bc, k=1, scale="odds")

## Fitted survival

plot(spl, lwd=3, ci=FALSE)

## Simple Weibull model fits much less well

splw <- flexsurvspline(Surv(recyrs, censrec) ~ group, data=bc, k=0, scale="hazard")
lines(splw, col="blue", ci=FALSE)

## Alternative way of fitting the Weibull

if (FALSE) {
splw2 <- flexsurvreg(Surv(recyrs, censrec) ~ group, data=bc, dist="weibull")