Zero-Inflated and Hurdle Models of Count Data with Extra Zeros: Examples from an HIV-Risk Reduction Intervention Trial

Hu, Mei‐Chen; Pavlicová, Martina; Nunes, Edward V.

doi:10.3109/00952990.2011.597280

Cited by 270 publications

(209 citation statements)

References 14 publications

Supporting

Mentioning

203

Contrasting

Unclassified

Order By: Relevance

“…To analyse these outcomes, we used a hurdle model (Hu, Pavlicova, & Nunes, 2011; Ridout, Demétrio, & Hinde, 1998) fitted to the dependent variable of the number of symptoms attributed to the tablet. This consists of a joint logistic and truncated negative binomial regression, providing us with information on the odds of participants attributing one or more symptoms to the tablet, but also allowing us to identify the effect of the predictors on the number of symptoms participants go on to attribute to the tablet.…”

Section: Methodsmentioning

confidence: 99%

Medicine‐related beliefs predict attribution of symptoms to a sham medicine: A prospective study

Webster

Weinman

Rubin

2018

British J Health Psychol

View full text Add to dashboard Cite

ObjectivesTo investigate a range of possible predictors of nocebo responses to medicines.DesignProspective cohort study.MethodsIn total, 203 healthy adult volunteers completed measures concerning demographics, psychological factors, medicine‐related beliefs, baseline symptoms, and symptom expectations before taking a sham pill, described as ‘a well‐known tablet available without prescription’ that was known to be associated with several side effects. Associations between these measures and subsequent attribution of symptoms to the tablet were assessed using a hurdle model consisting of a joint logistic and truncated negative binomial regression.ResultsMen had an increased odds of attributing symptoms to the tablet OR = 1.52, and older participants had decreased odds, OR = 0.97. Medicine‐related beliefs were important, with modern health worries, belief that medicines cause harm and perceived sensitivity to medicines associated with increased odds of symptom attribution, OR = 1.02, 1.10, 1.09, respectively. Trust in medicines and pharmaceutical companies decreased the odds of symptom attribution, OR = 0.91, 0.88, respectively. The number of symptoms at baseline and the expected likelihood of symptoms were associated with an increased odds of attributing symptoms to the tablet, OR = 1.07, 1.06, respectively. Anxiety, previous symptom experience, symptom expectations, and modern health worries were also important in predicting the number of symptoms participants attributed to the tablet.ConclusionIt is hard to predict who is at risk of developing nocebo responses to medicines from demographic or personality characteristics. Context‐specific factors such as beliefs about and trust in medicines, current symptoms and symptom expectations are more useful as predictors. More work is needed to investigate this in a patient sample. Statement of contribution What is already known on this subject? Many patients report non‐specific side effects to their medication which may arise through a nocebo effect.Whether some people are particularly predisposed to experience nocebo effects remains unclear. What does this study add? Demographic and personality characteristics are poor predictors of symptom attribution to a sham medicine.Instead, context‐specific factors that concern people's beliefs surrounding medicines, their current symptoms, and symptom expectations are more useful as predictors of symptom attribution.

show abstract

Section: Methodsmentioning

confidence: 99%

Medicine‐related beliefs predict attribution of symptoms to a sham medicine: A prospective study

Webster

Weinman

Rubin

2018

British J Health Psychol

View full text Add to dashboard Cite

show abstract

“…In vehicle crash data, counts frequently depart from the Poisson distribution due to larger frequencies of extreme observations resulting in a greater variance compared to the mean, resulting in over-dispersion [25], which was evident in our analysis. Although under dispersion can occasionally occur when analyzing motor vehicle crash data, it was not present according to our results.…”

Section: Discussionmentioning

confidence: 73%

“…

AbstractThe purpose of this article was to develop an accident prediction model for motor vehicle crashes occurring within Miami-Dade County, Florida during 2008-2011 Motor vehicle crash data were extracted from the Florida Department of Motor Vehicle and Highway Safety dataset for 40 intersections within Miami-Dade County, Florida for development of an accident prediction model. Each intersection was matched at least one of 20 red light camera (RLC) sites using selected geometric variables.

…”

mentioning

confidence: 99%

Untitled

2017

BBIJ

View full text Add to dashboard Cite

The purpose of this article was to develop an accident prediction model for motor vehicle crashes occurring within Miami-Dade County, Florida during 2008-2011 Motor vehicle crash data were extracted from the Florida Department of Motor Vehicle and Highway Safety dataset for 40 intersections within Miami-Dade County, Florida for development of an accident prediction model. Each intersection was matched at least one of 20 red light camera (RLC) sites using selected geometric variables. In addition, each intersection examined was at least 2 miles away from any RLC site. The dependent variable examined was the number of injury crashes occurring at each intersection between 2008 and 2011. Poisson, negative binomial, and gamma model distributions were compared using the Pearson's chi square ( ) 2 χ , scaled deviance (G 2 ), and Akaike Information Criterion (AIC) goodness of fit tests. Our analysis indicated that the negative binomial distribution was the best fit among the three models. Inspection of the observed data also suggested that the outcome variable's distribution was over dispersed. This study provided guidance on the use of goodness of fit testing (GOF) statistics for Poisson, negative binomial, and gamma models which will allow other researchers to evaluate different models.

show abstract

“…In general, the zeros from the first state are called structural zeros, and those from the second state are called sampling zeros [24,33]. The probability mass function for the ZIP models is as follows:…”

Section: The Zero-inflated Poisson Modelmentioning

confidence: 99%

“…The zero-inflated Poisson model is a combination of the logit model and the standard Poisson model. There are two sets of results: the first set shows the chances that a given independent variable affected the "structural" zeros; and the second set shows the results for the "sampling" counts [33]. Column (1) and column (2) in Table 6 show the first and second sets of results for male farmers, and column (3) and column (4) show the corresponding results for female farmers.…”

Section: The Determinants Of Farmers' Attendance In Farmer Field Schoolmentioning

confidence: 99%

An Impact Analysis of Farmer Field School in China

Cai

Shi

2016

Sustainability

View full text Add to dashboard Cite

Abstract:In this paper, we investigate the impact of the Farmer Field School (FFS) intervention among small-scale tomato farmers in Beijing. Using data collected by face-to face-interview from 358 households on 426 planting plots in 2009, we evaluate the yield effect and find evidence of positive impact. We then examine the determining factors of farmers' FFS attendance using the zero-inflated Poisson model. We find evidence of the positive impact of the FFS program on male participants but no impact on female participants. We find that some factors, such as being the household head, wealth level and land size affect both FFS participation decisions and attendance decisions, whereas other factors may affect only one decision but not the other. The results suggest that FFS is a useful way to increase production of farmers in Beijing and that the approach is especially effective for male and wealthy producers with smaller farm sizes and higher literacy.

show abstract

Zero-Inflated and Hurdle Models of Count Data with Extra Zeros: Examples from an HIV-Risk Reduction Intervention Trial

Cited by 270 publications

References 14 publications

Medicine‐related beliefs predict attribution of symptoms to a sham medicine: A prospective study

Medicine‐related beliefs predict attribution of symptoms to a sham medicine: A prospective study

Untitled

An Impact Analysis of Farmer Field School in China

Contact Info

Product

Resources

About