R: Flexible parametric mixture models for times to competing...

flexsurvmix {flexsurv} | R Documentation

## Flexible parametric mixture models for times to competing events

### Description

In a mixture model for competing events, an individual can experience one of a set of different events. We specify a model for the probability that they will experience each event before the others, and a model for the time to the event conditionally on that event occurring first.

### Usage

    flexsurvmix(
      formula,
      data,
      event,
      dists,
      pformula = NULL,
      anc = NULL,
      partial_events = NULL,
      initp = NULL,
      inits = NULL,
      fixedpars = NULL,
      dfns = NULL,
      method = "direct",
      em.control = NULL,
      optim.control = NULL,
      aux = NULL,
      sr.control = survreg.control(),
      integ.opts,
      hess.control = NULL,
      ...
    )

### Arguments

`formula` | Survival model formula. The left hand side is a `Surv` object specified as in `flexsurvreg`. This may define various kinds of censoring, as described in `Surv`. Any covariates on the right hand side of this formula will be placed on the location parameter for every component-specific distribution. Covariates on other parameters of the component-specific distributions may be supplied using the `anc` argument. Alternatively, `formula` may be a list of formulae, with one component for each alternative event. This may be used to specify different covariates on the location parameter for different components. A list of formulae may also be used to indicate that for particular individuals, different events may be observed in different ways, with different censoring mechanisms. Each list component specifies the data and censoring scheme for that mixture component. For example, suppose we are studying people admitted to hospital,and the competing states are death in hospital and discharge from hospital. At time t we know that a particular individual is still alive, but we do not know whether they are still in hospital, or have been discharged. In this case, if the individual were to die in hospital, their death time would be right censored at t. If the individual will be (or has been) discharged before death, their discharge time is completely unknown, thus interval-censored on (0,Inf). Therefore, we need to store different event time and status variables in the data for different alternative events. This is specified here as `formula = list("discharge" = Surv(t1di, t2di, type="interval2"), "death" = Surv(t1de, status_de))` where for this individual, `(t1di, t2di) = (0, Inf)` and `(t1de, status_de) = (t, 0)`.
`data` | Data frame containing variables mentioned in `formula`, `event` and `anc`.
`event` | Variable in the data that specifies which of the alternative events is observed for which individual. If the individual's follow-up is right-censored, or if the event is otherwise unknown, this variable must have the value `NA`. Ideally this should be a factor, since the mixture components can then be easily identified in the results with a name instead of a number. If this is not already a factor, it is coerced to one. Then the levels of the factor define the required order for the components of the list arguments `dists`, `anc`, `inits` and `dfns`. Alternatively, if the components of the list arguments are named according to the levels of `event`, then the components can be arranged in any order.
`dists` | Vector specifying the parametric distribution to use for each component. The same distributions are supported as in `flexsurvreg`.
`pformula` | Formula describing covariates to include on the component membership proabilities by multinomial logistic regression. The first component is treated as the baseline.
`anc` | List of component-specific lists, of length equal to the number of components. Each component-specific list is a list of formulae representing covariate effects on parameters of the distribution. If there are covariates for one component but not others, then a list containing one null formula on the location parameter should be supplied for the component with no covariates, e.g `list(rate=~1)` if the location parameter is called `rate`. Covariates on the location parameter may also be supplied here instead of in `formula`. Supplying them in `anc` allows some components but not others to have covariates on their location parameter. If a covariate on the location parameter was provided in `formula`, and there are covariates on other parameters, then a null formula should be included for the location parameter in `anc`, e.g `list(rate=~1)`
`partial_events` | List specifying the factor levels of `event` which indicate knowledge that an individual will not experience particular events, but may experience others. The names of the list indicate codes that indicate partial knowledge for some individuals. The list component is a vector, which must be a subset of `levels(event)` defining the events that a person with the corresponding event code may experience. For example, suppose there are three alternative events called `"disease1"`,`"disease2"` and `"disease3"`, and for some individuals we know that they will not experience `"disease2"`, but they may experience the other two events. In that case we must create a new factor level, called, for example `"disease1or3"`, and set the value of `event` to be `"disease1or3"` for those individuals. Then we use the `"partial_events"` argument to tell `flexsurvmix` what the potential events are for individuals with this new factor level. `partial_events = list("disease1or3" = c("disease1","disease3"))`
`initp` | Initial values for component membership probabilities. By default, these are assumed to be equal for each component.
`inits` | List of component-specific vectors. Each component-specific vector contains the initial values for the parameters of the component-specific model, as would be supplied to `flexsurvreg`. By default, a heuristic is used to obtain initial values, which depends on the parametric distribution being used, but is usually based on the empirical mean and/or variance of the survial times.
`fixedpars` | Indexes of parameters to fix at their initial values and not optimise. Arranged in the order: baseline mixing probabilities, covariates on mixing probabilities, time-to-event parameters by mixing component. Within mixing components, time-to-event parameters are ordered in the same way as in `flexsurvreg`. If `fixedpars=TRUE` then all parameters will be fixed and the function simply calculates the log-likelihood at the initial values. Not currently supported when using the EM algorithm.
`dfns` | List of lists of user-defined distribution functions, one for each mixture component. Each list component is specified as the `dfns` argument of `flexsurvreg`.
`method` | Method for maximising the likelihood. Either `"em"` for the EM algorithm, or `"direct"` for direct maximisation.
`em.control` | List of settings to control EM algorithm fitting. The only options currently available are `trace` set to 1 to print the parameter estimates at each iteration of the EM algorithm `reltol` convergence criterion. The algorithm stops if the log likelihood changes by a relative amount less than `reltol`. The default is the same as in `optim`, that is, `sqrt(.Machine$double.eps)`. `var.method` method to compute the covariance matrix. `"louis"` for the method of Louis (1982), or `"direct"`for direct numerical calculation of the Hessian of the log likelihood. `optim.p.control` A list that is passed as the `control` argument to `optim` in the M step for the component membership probability parameters. The optimisation in the M step for the time-to-event parameters can be controlled by the `optim.control` argument to `flexsurvmix`. For example, `em.control = list(trace=1, reltol=1e-12)`.
`optim.control` | List of options to pass as the `control` argument to `optim`, which is used by `method="direct"` or in the M step for the time-to-event parameters in `method="em"`. By default, this uses `fnscale=10000` and `ndeps=rep(1e-06,p)` where `p` is the number of parameters being estimated, unless the user specifies these options explicitly.
`aux` | A named list of other arguments to pass to custom distribution functions. This is used, for example, by `flexsurvspline` to supply the knot locations and modelling scale (e.g. hazard or odds). This cannot be used to fix parameters of a distribution — use `fixedpars` for that.
`sr.control` | For the models which use `survreg` to find the maximum likelihood estimates (Weibull, exponential, log-normal), this list is passed as the `control` argument to `survreg`.
`integ.opts` | List of named arguments to pass to `integrate`, if a custom density or hazard is provided without its cumulative version. For example, `integ.opts = list(rel.tol=1e-12)`
`hess.control` | List of options to control inversion of the Hessian to obtain a covariance matrix. Available options are `tol.solve`, the tolerance used for `solve` when inverting the Hessian (default `.Machine$double.eps`), and `tol.evalues`, the accepted tolerance for negative eigenvalues in the covariance matrix (default `1e-05`). The Hessian is positive definite, thus invertible, at the maximum likelihood. If the Hessian computed after optimisation convergence can't be inverted, this is either because the converged result is not the maximum likelihood (e.g. it could be a "saddle point"), or because the numerical methods used to obtain the Hessian were inaccurate. If you suspect that the Hessian was computed wrongly enough that it is not invertible, but not wrongly enough that the nearest valid inverse would be an inaccurate estimate of the covariance matrix, then these tolerance values can be modified (reducing `tol.solve` or increasing `tol.evalues`) to allow the inverse to be computed.
`...` | Optional arguments to the general-purpose optimisation routine `optim`. For example, the BFGS optimisation algorithm is the default in `flexsurvreg`, but this can be changed, for example to `method="Nelder-Mead"` which can be more robust to poor initial values. If the optimisation fails to converge, consider normalising the problem using, for example, `control=list(fnscale = 2500)`, for example, replacing 2500 by a number of the order of magnitude of the likelihood. If 'false' convergence is reported with a non-positive-definite Hessian, then consider tightening the tolerance criteria for convergence. If the optimisation takes a long time, intermediate steps can be printed using the `trace` argument of the control list. See `optim` for details.

### Details

This differs from the more usual "competing risks" models, where we specify "cause-specific hazards" describing the time to each competing event. This time will not be observed for an individual if one of the competing events happens first. The event that happens first is defined by the minimum of the times to the alternative events.

The `flexsurvmix` function fits a mixture model to data consisting of a single time to an event for each individual, and an indicator for what type of event occurs for that individual. The time to event may be observed or censored, just as in `flexsurvreg`, and the type of event may be known or unknown. In a typical application, where we follow up a set of individuals until they experience an event or a maximum follow-up time is reached, the event type is known if the time is observed, and the event type is unknown when follow-up ends and the time is right-censored.

The model is fitted by maximum likelihood, either directly or by using an expectation-maximisation (EM) algorithm, by wrapping `flexsurvreg` to compute the likelihood or to implement the E and M steps.

### Value

List of objects containing information about the fitted model. The important one is `res`, a data frame containing the parameter estimates and associated information.

### References

Larson, M. G., & Dinse, G. E. (1985). A mixture model for the regression analysis of competing risks data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 34(3), 201-211.

Lau, B., Cole, S. R., & Gange, S. J. (2009). Competing risk regression models for epidemiologic data. American Journal of Epidemiology, 170(2), 244-256.

[Package flexsurv version 2.0 Index]