mreg: To perform regression when discrete outcome variables are missing

Description

This software was created for the paper referred to below. If a longitudinal data base has regularly updated explanatory variables, but whose outcome variable is only intermittently collected then we can still perform exact maximum likelihood estimation of a regression model if the outcome variable is discrete.

Usage

mreg(
  formula,
  data,
  patid,
  start.theta = NULL,
  modify = unity,
  modify.p = 0,
  mod.formula = ~1,
  density.name = "negbin",
  link = "log",
  iterlim = 100,
  gradtol = 1e-06,
  steptol = 1e-06,
  na.action = NULL,
  print.level = 2,
  zero.start = FALSE
)

Value

It returns an object of class mreg which is similar to a lm object. It has print and summary methods to display the fitted parameters and standard errors.

Arguments

formula: This is a formula object e.g. Y~A+B to describe the location parameter
data: This is a data frame in which the variables are recorded
patid: In a longitudinal context this indexes the individuals. Note that the observations within each patient is assumed to be ordered according the timing of the observations.
start.theta: Optional vector of starting values for location and nuisance parameters
modify: We may wish to let the location depend on functions of the previous outcomes. Since these may be missing, we have to provide a function that can cope with all the potential values the outcome may have taken. See paper
modify.p: This is the dimension of the parameters associated with the modify function.
mod.formula: If we require other variables to interact with the previous observation we must create a set of variables to use. This is a one-sided formula e.g. ~X+Z, if we wanted to use those variables.
density.name: This is the density the increment in outcome is assumed to follow. It can be one of three values: negbin, poisson, geometric.
link: This is the link function \(g(\mu)=\eta\). Where \(\eta\) is a linear combination of covariates, and \(\mu\) is the expected value of the outcome. The link function can be one of four values: identity, log, logit, hyper.
iterlim: The maximum number of iterations allowed for the nlm function.
gradtol: The parameter gradtol for the nlm function.
steptol: The parameter steptol for the nlm function
na.action: Parameter is not used: If any covariates are missing the function will return an error.
print.level: The parameter print.level for the nlm function. Set to the maximum, verbose level.
zero.start: It may be the case that it is known that the first value of the outcome was zero for all individuals, in which case invoke this TRUE/FALSE option.

References

Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics

Examples

Run this code

	data(public)
if (FALSE) {
mod1 <- mreg( damaged~offset(log(intervisit.time))+esr.init,
data=public,patid=ptno,print.level=2, iterlim=1000 )
mod.ncar <-mreg(damaged ~ offset(log(intervisit.time)) + esr.init +
         tender + effused + clinic.time, data = public, patid = ptno,
         modify = paper, modify.p = 5, mod.formula = ~art.dur.init,
       density.name = "negbin.ncar", iterlim = 1000, print.level = 2)
}