Analysis of repeated measures data with clumping at zero

Tooze, Janet A.; Grunwald, Gary K.; Jones, Richard H.

doi:10.1191/0962280202sm291ra

Cited by 274 publications

(335 citation statements)

References 15 publications

Supporting

Mentioning

333

Contrasting

Unclassified

Order By: Relevance

“…The interaction term, the test of primary interest, tested whether the change across phases differed between the conditions. The viral RNA level, which displayed both an abundance of values below the level of detection and skew in the detected levels, was tested using a two-part model (Tooze et al, 2002) where one of the parts modeled the presence versus absence of detectable viral levels and the second part modeled the mean of the log-transformed values of those levels above detection. This model was estimated twice, once using all assessment points as an outcome and a second time using the two baseline phase assessments (weeks 0 and 4) as covariates.…”

Section: Discussionmentioning

confidence: 99%

Voucher reinforcement improves medication adherence in HIV-positive methadone patients: A randomized trial

Sorensen

Haug

Delucchi

et al. 2007

Drug and Alcohol Dependence

145

136

View full text Add to dashboard Cite

This clinical trial evaluated a contingency management intervention designed to improve medication adherence among HIV-positive methadone maintenance patients. After a 4-week baseline observation phase, eligible participants (N = 66) were randomly assigned to: (a) medication coaching sessions every other week to assist with adherence strategies (comparison group) or (b) medication coaching plus voucher reinforcement for opening electronic medication caps on time (voucher group). Baseline adherence (percent doses taken/percent total possible doses) was 51% using electronic measurement, 75% using self-report and 75% using pill count. The intervention was provided for 12 weeks, with a 4-week follow-up. The primary outcome results of the clinical trial indicated effectiveness during the intervention, with significant mean adherence differences between voucher and comparison groups using electronic measurement (78% vs. 56%), pill count (86% vs. 75%), and self-report (87% vs. 69%). Differences between groups faded after vouchers were discontinued. Contingency management shows promise as a strategy to promote antiretroviral medication adherence in this population.

show abstract

Section: Discussionmentioning

confidence: 99%

Voucher reinforcement improves medication adherence in HIV-positive methadone patients: A randomized trial

Sorensen

Haug

Delucchi

et al. 2007

Drug and Alcohol Dependence

145

136

View full text Add to dashboard Cite

show abstract

“…46,47 Linkage disequilibrium was expressed as r 2 and D 0 . 48,49 To study the association between ADH1B and ADH1C genotypes and amount of alcohol intake, the correlated mixed distribution model was applied (Mixcorr macro 50 ). This model handles data with clumping at zero and a lognormal distribution for nonzero values, and contains components to model the probability of a nonzero value and the mean of nonzero values, allowing for repeated measurements using random effects.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…This model handles data with clumping at zero and a lognormal distribution for nonzero values, and contains components to model the probability of a nonzero value and the mean of nonzero values, allowing for repeated measurements using random effects. 50 This means that if a variable affects the mean amount by affecting both the probability of occurrence of a nonzero value and also the mean of a nonzero value, these effects can be separated and quantified. Hence, two estimates are produced from this model: the OR for having a nonzero alcohol intake (that is, for not being a non-drinker) and the mean amount of alcohol intake among those with a nonzero intake.…”

Section: Statistical Analysesmentioning

confidence: 99%

Alcoholism and alcohol drinking habits predicted from alcohol dehydrogenase genes

et al. 2007

View full text Add to dashboard Cite

Alcohol drinking habits and alcoholism are partly genetically determined. Alcohol is degraded primarily by alcohol dehydrogenase (ADH) wherein genetic variation that affects the rate of alcohol degradation is found in ADH1B and ADH1C. It is biologically plausible that these variations may be associated with alcohol drinking habits and alcoholism. By genotyping 9080 white men and women from the general population, we found that men and women with ADH1B slow vs fast alcohol degradation drank more alcohol and had a higher risk of everyday drinking, heavy drinking, excessive drinking and of alcoholism. For example, the weekly alcohol intake was 9.8 drinks (95% confidence interval (CI): 9.1-11) among men with the ADH1B Á 1/1 genotype compared to 7.5 drinks (95% CI: 6.4-8.7) among men with the ADH1B Á 1/2 genotype, and the odds ratio (OR) for heavy drinking was 3.1 (95% CI: 1.7-5.7) among men with the ADH1B Á 1/1 genotype compared to men with the ADH1B Á 1/2 genotype. Furthermore, individuals with ADH1C slow vs fast alcohol degradation had a higher risk of heavy and excessive drinking. For example, the OR for heavy drinking was 1.4 (95% CI: 1.1-1.8) among men with the ADH1C Á 1/2 genotype and 1.4 (95% CI: 1.0-1.9) among men with the ADH1B Á 2/2 genotype, compared with men with the ADH1C Á 1/1 genotype. Results for ADH1B and ADH1C genotypes among men and women were similar. Finally, because slow ADH1B alcohol degradation is found in more than 90% of the white population compared to less than 10% of East Asians, the population attributable risk of heavy drinking and alcoholism by ADH1B Á 1/1 genotype was 67 and 62% among the white population compared with 9 and 24% among the East Asian population.

show abstract

“…The first step consists of fitting a two-part statistical model that describes the relationship between usual intake and covariates and estimates the variability of intake both within and between individuals. We adapted a two-part model with correlated person-specific random effects, developed by Tooze et al (3), for this purpose. Similar to the ISUF method, the statistical model represents usual intake as the product of the probability to consume a food on a given day and the usual consumption-day amount.…”

Section: Overview Of the Nci Methodsmentioning

confidence: 99%

A New Statistical Method for Estimating the Usual Intake of Episodically Consumed Foods with Application to Their Distribution

Tooze¹,

Midthune²,

Dodd³

et al. 2006

Journal of the American Dietetic Association

550

575

View full text Add to dashboard Cite

Objective-We propose a new statistical method that uses information from two 24-hour recalls (24HRs) to estimate usual intake of episodically-consumed foods.Statistical Analyses Performed-The method developed at the National Cancer Institute (NCI) accommodates the large number of non-consumption days that arise with foods by separating the probability of consumption from the consumption-day amount, using a two-part model. Covariates, such as sex, age, race, or information from a food frequency questionnaire (FFQ), may supplement the information from two or more 24HRs using correlated mixed model regression. The model allows for correlation between the probability of consuming a food on a single day and the consumptionday amount. Percentiles of the distribution of usual intake are computed from the estimated model parameters. Results-TheEating at America's Table Study (EATS) data are used to illustrate the method to estimate the distribution of usual intake for whole grains and dark green vegetables for men and women and the distribution of usual intakes of whole grains by educational level among men. A simulation study indicates that the NCI method leads to substantial improvement over existing methods for estimating the distribution of usual intake of foods.Applications/Conclusions-The NCI method provides distinct advantages over previously proposed methods by accounting for the correlation between probability of consumption and amount consumed and by incorporating covariate information. Researchers interested in estimating the distribution of usual intakes of foods for a population or subpopulation are advised to work with a statistician and incorporate the NCI method in analyses. KeywordsUsual intake; Episodically-consumed foods; statistical methods When using dietary assessment among populations or individuals, investigators are often interested in capturing usual intakes -that is, long-term averages. The 24-hour dietary recall (24HR) provides rich details about dietary intake for a given day, but collecting more than two 24HRs per individual is impractical in large surveys such as the National Health and Nutrition Examination Survey (NHANES). Therefore, it is necessary to employ statistical methods to estimate usual dietary intake.Researchers are interested in estimating the usual intake of foods to assess compliance with food-based dietary recommendations and to relate food intake to health parameters. Unlike most nutrients, which are consumed daily, estimating usual intake of episodically-consumed foods presents the following unique challenges for statistical modeling (see the Glossary for a definition of "statistical modeling" and related terms):A. accounting for days without consumption of a particular food or food group; B. allowing for consumption-day amount data that are generally positively skewed and have extreme values in the upper tail of the intake distribution;C. distinguishing within-person variability, which consists of day-to-day variation in intake and random reporting errors, from between-...

show abstract

Analysis of repeated measures data with clumping at zero

Cited by 274 publications

References 15 publications

Voucher reinforcement improves medication adherence in HIV-positive methadone patients: A randomized trial

Voucher reinforcement improves medication adherence in HIV-positive methadone patients: A randomized trial

Alcoholism and alcohol drinking habits predicted from alcohol dehydrogenase genes

A New Statistical Method for Estimating the Usual Intake of Episodically Consumed Foods with Application to Their Distribution

Contact Info

Product

Resources

About