*Once a month we discuss a particular research method that may be of interest to people working in health economics. We’ll consider widely used key methodologies, as well as more novel approaches. Our reviews are not designed to be comprehensive but provide an introduction to the method, its underlying principles, some applied examples, and where to find out more. If you’d like to write a post for this series, get in touch. This month’s method is semiparametric models with penalised splines.*

## Principles

A common assumption of regression models is that effects are linear and additive. However, nothing is ever really that simple. One might respond that all models are wrong, but some are useful, as George Box once said. And the linear, additive regression model has coefficients that can be interpreted as average treatment effects under the right assumptions. Sometimes though we are interested in conditional average treatment effects and how the impact of an intervention varies according to the value of some variable of interest. Often this relationship is not linear and we don’t know its functional form. Splines provide a way of estimating curves (or surfaces) of unknown functional form and are a widely used tool for semiparametric regression models. The term ‘spline’ was derived from the tool shipbuilders and drafters used to construct smooth edges: a bendable piece of material that when fixed at a number of points would relax into the desired shape.

## Implementation

Our interest lies in estimating the unknown function *m:*

A ‘spline’ in the mathematical sense is a function constructed piece-wise from polynomial functions. The places where the functions meet are known as knots and the spline has order equal to one more than the degree of the underlying polynomial terms. Basis-splines or B-splines are the typical starting point for spline functions. These are curves that are defined recursively as a sum of ‘basis functions’, which depend only on the polynomial degree and the knots. A spline function can be represented as a linear combination of B-splines, the parameters dictating this combination can be estimated using standard regression model estimation techniques. If we have B-splines then our regression function can be estimated as:

by minimising . Where the are the B-splines and the are coefficients to be estimated.

Useful technical explainers of splines and B-splines can be found here [PDF] and here [PDF].

One issue with fitting splines to data is that we run the risk of ‘overfitting’. Outliers might distort the curve we fit, damaging the external validity of conclusions we might make. To deal with this, we can enforce a certain level of smoothness using so-called penalty functions. The smoothness (or conversely the ‘roughness’) of a curve is often defined by the integral of the square of the second derivative of the curve function. Penalised-splines, or P-splines, were therefore proposed which added on this smoothness term multiplied by a smoothing parameter . In this case, we look to minimising:

to estimate our parameters. Many other different variations on this penalty have been proposed. This article provides a good explanation of P-splines.

An attractive type of spline has become the ‘low rank thin plate spline‘. This type of spline is defined by its penalty, which has a physical analogy with the resistance that a thin sheet of metal puts up when it is bent. This type of spline removes the problem associated with thin plate splines of having too many parameters to estimate by taking a ‘low rank’ approximation, and it is generally insensitive to the choice of knots, which other penalised spline regression models are not.

Crainiceanu and colleagues show how the low rank thin plate smooth splines can be represented as a generalised linear mixed model. In particular, our model can be represented as:

where , , are the knots. The parameters, , can be estimated by minimising

.

This is shown to give the mixed model

where each random coefficient in the vector is distributed as and and are given in the paper cited above.

As a final note, we have discussed splines in one dimension, but they can be extended to more dimensions. A two-dimensional spline can be generated by taking the tensor product of the two one dimensional spline functions. I leave this as an exercise for the reader.

### Software

#### R

- The package
*gamm4*provides the tools necessary for a frequentist analysis along the lines described in this post. It uses restricted maximum likelihood estimation with the package*lme4*to estimate the parameters of the thin plate spline model. - A Bayesian version of this functionality is implemented in the package
*rstanarm*, which uses*gamm4*to produce the matrices for thin plate spline models and Stan for the estimation through the*stan_gamm4*function.

If you wanted to implement these models for yourself from scratch, Crainiceanu and colleagues provide the R code to generate the matrices necessary to estimate the spline function:

n<-length(covariate) X<-cbind(rep(1,n),covariate) knots<-quantile(unique(covariate), seq(0,1,length=(num.knots+2))[-c(1,(num.knots+2))]) Z_K<-(abs(outer(covariate,knots,"-")))^3 OMEGA_all<-(abs(outer(knots,knots,"-")))^3 svd.OMEGA_all<-svd(OMEGA_all) sqrt.OMEGA_all<-t(svd.OMEGA_all$v %*% (t(svd.OMEGA_all$u)*sqrt(svd.OMEGA_all$d))) Z<-t(solve(sqrt.OMEGA_all,t(Z_K)))

#### Stata

I will temper this advice by cautioning that I have never estimated a spline-based semi-parametric model in Stata, so what follows may be hopelessly incorrect. The only implementation of penalised splines in Stata is the package and associated function *pspline. *However, I cannot find any information about the penalty function used, so I would advise some caution when implementing. An alternative is to program the model yourself, through conversion of the above R code in Mata to generate the matrix Z and then the parameters could be estimated with *xtmixed. *

## Applications

Applications of these semi-parametric models in the world of health economics have tended to appear more in technical or statistical journals than health economics journals or economics more generally. For example, recent examples include Li et al who use penalised splines to estimate the relationship between disease duration and health care costs. Wunder and co look at how reported well-being varies over the course of the lifespan. And finally, we have Stollenwerk and colleagues who use splines to estimate flexible predictive models for cost-of-illness studies with ‘big data’.

**Credit**