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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 ( 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.
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Adjacency-Clustering and Its Application for Yield Prediction in Integrated Circuit Manufacturing
Accurate yield prediction in integrated circuit manufacturing enables accurate estimation of production cost and early detection of processing problems. It is known that defects tend to be clustered and a chip is likely to be defective if its neighbors are defective. This neighborhood effect is not well captured in traditional yield modeling approaches. We propose a new yield prediction model, called adjacency-clustering which addresses, for the first time, the neighborhood effect, and delivers prediction results that are significantly better than state-of-the-art methods. Adjacency-clustering (AC) model is a form of the Markov random field minimum energy model (MRF) that is primarily known in the context of image segmentation. AC model is a novel use of MRF for identifying defect patterns that enable diagnosis of failure causes in the manufacturing process. In this paper we utilize the defect patterns obtained by the AC model for yield prediction. We compare the performance of the AC model to that of leading yield prediction models including the Poisson, the negative binomial, the Poisson regression, and negative binomial regression models, on real data sets and on simulated data sets. The results demonstrate that the adjacency-clustering model captures the neighborhood effect and delivers superior prediction accuracy. Moreover, the concept and methodology of adjacency-clustering are not limited to integrated circuit manufacturing. Rather, it is applicable in any context where a neighborhood effect is present, such as disease risk mapping and energy consumption prediction. The e-companion is available at https://doi.org/10.1287/opre.2018.1741 .
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- Award ID(s):
- 1760102
- PAR ID:
- 10357247
- Date Published:
- Journal Name:
- Operations Research
- Volume:
- 66
- Issue:
- 6
- ISSN:
- 0030-364X
- Page Range / eLocation ID:
- 1571 to 1585
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/opre.2018.1741
