hybdes {osDesign} | R Documentation |
hybdes() computes the MLE for a Hybrid Design. hyblik() computes the likelihood for a Hybrid Design at a specified parameter vector
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. |
mle |
MLE of the hybrid design |
start.mle |
Result of clogit function (stratified case-control MLE) |
E. Smoot
Smoot, E., and S. Haneuse. "On the Analysis of Hybrid Designs that Combine Group- and Individual-Level Data." Biometrics (in press, 2014).
#hybdes(MM, NN, cc, approx='NA')