Clinical studies investigating treatments for problem gambling or gambling disorder frequently use gambling expenditure, such as gambling losses, as a treatment outcome. Gambling losses frequently vary substantially between participants; some report no losses, and some report substantial losses. In this article, we review how gambling losses are commonly analyzed in treatment studies, and show that frequently used methods, such as a log(y+1) transformation or assuming a normal distribution, often perform poorly for these types of data. We propose that a marginalized longitudinal two-part model is a more attractive option. The models are compared using real data from a trial including 136 persons with gambling disorder. Additionally, different performance metrics are further evaluated in a Monte Carlo simulation study. We conclude that gambling researchers should consider using the longitudinal two-part model as it offers a flexible and powerful way of modeling gambling outcomes. The log(y + 1) transformation can be highly misleading in typical gambling data, as a difference in the number of zeros leads to biased estimates of the treatment effects.