hybdes: Hybrid Design MLE and likelihood
In osDesign: Design and analysis of observational studies

Description Usage Arguments Value Author(s) References Examples

hybdes() computes the MLE for a Hybrid Design. hyblik() computes the likelihood for a Hybrid Design at a specified parameter vector

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hybdes(MM, NN, cc, ntrue = 0, aprx='binom', start.mle=NA, group.int=FALSE, betafct =
 function(x){return(x[1] + c(0,x[-1]) )}, print.level = 0, iterlim = 100)
hyblik(beta.matrix, MM, NN, cc, aprx = 'binom', ntrue = 0, group.int=FALSE)

`MM`	MM is a matrix of margin totals. Rows are groups, columns are margin totals
`NN`	NN is a matrix of outcome margin totals. Rows are groups, columns are margin totals. NN is always a K x 2 matrix, where K is the number of groups
`cc`	cc is a list of case-control data. Each element is a table with exposure (rows) and outcome (columns)
`ntrue`	The number of groups that should be calculated using the true hybrid likelihood, rather than an approximation.
`aprx`	Type of approximation to use when calculating the hybrid likelihood. Default is the binomial approximation.
`start.mle`	Starting value for the Newton-Raphson algorithm used to determine Hybrid Design MLE.
`group.int`	A logical indicator of whether or not groups should be treated as having different intercept parameters.
`betafct`	A function used to specify the model of interest by reparamterizing the hybrid likelihood. betafct() takes in group-specific parameters associated with each level of the exposure variable. The default function corresponds to a model with an intercept parameter and log-odds-ratio parameters relating levels of X to the baseline level, X = 0 (i.e. column 1 of MM).
`print.level`	Argument passed into nlm()
`iterlim`	Argument passed into nlm()
`beta.matrix`	Parameter values for likelihood calculation; used only in hyblik(). This should be entered in the form of a matrix, with one row per group and one column per parameter.