I keep reading that sparsity (the number of cells with 0 observations in a cross tabulation of all variables in a model) is a problem for running logistic regression models because it biases odds ratios and wald tests upwards etc.. I think most datasets will have varying degrees of sparsity, but are there rules of thumb or formal tests for determining when sparsity is likely to lead to biased results for logistic regression models?
My source for this claim regarding sparsity:
Cohen, J., Cohen, P., West, S.G., Aiken, L.S. (2003) Applied Multiple Regression/Correlation Analysis for the Behavioural Sciences. New Jersey: Lawrence Erlbaum and Associates https://books.google.com.au/books?id=fAnSOgbdFXIC&pg=PT657&dq=cohen+regression+sparse&hl=en&sa=X&ved=0ahUKEwjVo--iicPZAhWDupQKHbwaAq8Q6AEIKTAA#v=onepage&q=cohen%20regression%20sparse&f=false
Greenland, S., & Altman, D.G. (2016). Sparse data bias a problem hiding in plain sight. BMJ, 352.