  In many health domains such as substance-use, outcomes are often counts with
an excessive number of zeros (EZ) - count data having zero counts at a rate
significantly higher than that expected of a standard count distribution (e.g.,
Poisson). However, an important gap exists in sample size estimation
methodology for planning sequential multiple assignment randomized trials
(SMARTs) for comparing dynamic treatment regimens (DTRs) using longitudinal
count data. DTRs, also known as treatment algorithms or adaptive interventions,
mimic the individualized and evolving nature of patient care through the
specification of decision rules guiding the type, timing and modality of
delivery, and dosage of treatments to address the unique and changing needs of
individuals. To close this gap, we develop a Monte Carlo-based approach to
sample size estimation. A SMART for engaging alcohol and cocaine-dependent
patients in treatment is used as motivation.
