Hello StackExchange users,
Background: I am a biostatistician presently wrestling with a dataset of cellular expression rates. The study exposed a host of cells, collected in groups from various donors, to certain peptides. Cells either express certain biomarkers in response, or they don't. The response rates are then recorded for each donor-group. Response rates (expressed as percentages) are the outcome of interest, and peptide exposure is the predictor.
Note that observations are clustered within donors.
Since I only have the summary data, I am treating the donor-wise response rates as continuous data (at least for now).
The complication arises from the fact that I have many zeroes in my data. Far too many to be ignored. I am considering a zero-inflated gamma model to deal with the fact that I have skewed continuous data coupled with an overabundance of zeroes. I have also considered the Tobit model, but this seems inferior since it assumes censoring at a lower bound, as opposed to genuine zeroes (econometricians might say the distinction is moot).
Question: Generally speaking, when is it appropriate to use a zero-inflated gamma model? That is, what are the assumptions? And how does one interpret its inferences? I would be grateful for links to papers that discuss this, if you have any.
I've found a link on SAS-L in which Dale McLerran provides NLMIXED code for a zero-inflated gamma model, so it appears to be possible. Nonetheless, I would hate to charge forth blindly.
Very much appreciated,