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Background: Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol-related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero-inflated, particularly compared with recently developed marginalized count regression approaches for such data., Methods: The current simulation study examined five commonly used approaches for analyzing count outcomes, including two linear models (with outcomes on raw and log-transformed scales, respectively) and three prevailing count distribution-based models (ie, Poisson, negative binomial, and zero-inflated Poisson (ZIP) models). We also considered the marginalized zero-inflated Poisson (MZIP) model, a novel alternative that estimates the overall effects on the population mean while adjusting for zero-inflation. Motivated by alcohol misuse prevention trials, extensive simulations were conducted to evaluate and compare the statistical power and Type I error rate of the statistical models and approaches across data conditions that varied in sample size ( N = 100 $$ N=100 $$ to 500), zero rate (0.2 to 0.8), and intervention effect sizes., Results: Under zero-inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non-zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on the raw scale, negative binomial model, and ZIP model. The performance of the linear model with a log-transformed outcome variable was unsatisfactory., Conclusions: The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero-inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Objective: American Indian (AI) individuals are at increased risk for present-day trauma exposure and associated negative outcomes, as well as ongoing effects of intergenerational trauma exposure and adversity. However, few empirically supported treatments exist that are specifically tailored and/or tested with AI communities. This study describes the process of selecting, adapting, and implementing narrative exposure therapy (NET) with an AI community., Methods: A community and academic partnership was formed and worked together to make culturally mindful changes to NET to best fit the needs of the community. The partnership incorporated community leaders/Elders (n = 7), providers (n = 11), and participants seeking treatment (n = 50) to implement an iterative process of adapting and implementing the adapted form of NET., Results: Key adaptions included addressing historical and intergenerational trauma, greater protections for confidentiality in a small community, and incorporation of cultural customs and traditions. Overall, the adapted form of NET was favorably received by the participants, and the implementation appeared to be feasible, with improved retention over past trials of adapted trauma-focused treatments with this community and with highly positive satisfaction ratings and feedback., Conclusions: NET was shown to be an appropriate approach for this AI community and should be considered as a treatment option for other AI communities. Future work should consider strategies outlined in this adaption as well as following a similar process for working with AI communities to implement culturally appropriate interventions for trauma-related symptoms., (© 2022 Wiley Periodicals LLC.)

Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study-level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta-analysis. The current study proposes a novel statistical approach, the Zero-inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero-inflated Poisson regression model, thus it is attractive for meta-analysis when individual participant-level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero-inflation bias in most situations., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)