Albert Burgess-Hull, Ph.D.

@article{Burgess-Hull:2020fk,

title = {Finite Mixture Models with Student t Distributions: an Applied Example.},

author = {Albert J Burgess-Hull},

url = {https://pubmed.ncbi.nlm.nih.gov/32306224/},

doi = {10.1007/s11121-020-01109-3},

issn = {1573-6695 (Electronic); 1389-4986 (Linking)},

year  = {2020},

date = {2020-04-18},

journal = {Prev Sci},

address = {Clinical Pharmacology and Therapeutics Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, 21224, USA. albert.burgess-hull@nih.gov.},

abstract = {The use of finite mixture modeling (FMM) to identify unobservable or latent groupings of individuals within a population has increased rapidly in applied prevention research. However, many prevention scientists are still unaware of the statistical assumptions underlying FMM. In particular, finite mixture models (FMMs) typically assume that the observed indicator variables are normally distributed within each latent subgroup (i.e., within-class normality). These assumptions are rarely met in applied psychological and prevention research, and violating these assumptions when fitting a FMM can lead to the identification of spurious subgroups and/or biased parameter estimates. Although new methods have been developed that relax the within-class normality assumption when fitting a FMM, prevention scientists continue to rely on FMM methods that assume within-class normality. The purpose of the current article is to introduce prevention researchers to a FMM method for heavy-tailed data: FMM with Student t distributions. We begin by reviewing the distributional assumptions that underlie FMM and the limitations of FMM with normal distributions. Next, we introduce FMM with Student t distributions, and show, step by step, the analytic and substantive results of fitting a FMM with normal and Student t distributions to data from a smoking-cessation trial. Finally, we extend the results of the applied example to draw conclusions about the use of FMM with Student t distributions in applied settings and to provide guidelines for researchers who wish to use these methods in their own research.},

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pubstate = {published},

tppubtype = {article}

}

@article{Epstein:2020aa,

title = {Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data},

author = {David H Epstein and Matthew Tyburski and William J Kowalczyk and Albert J Burgess-Hull and Karran A Phillips and Brenda L Curtis and Kenzie L Preston},

doi = {10.1038/s41746-020-0234-6},

isbn = {2398-6352},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {npj Digital Medicine},

volume = {3},

number = {1},

pages = {26},

abstract = {Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to ``push''content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy---as high as 0.93 by the end of 16 weeks of tailoring---but this was driven mostly by correct predictions of absence. For predictions of presence, ``believability''(positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based ``digital phenotyping''inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.},

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pubstate = {published},

tppubtype = {article}

}

@article{Burgess-Hull:2018aa,

title = {The social networks of smokers attempting to quit: An empirically derived and validated classification.},

author = {Albert J Burgess-Hull and Linda J Roberts and Megan E Piper and Timothy B Baker},

url = {https://www.ncbi.nlm.nih.gov/pubmed/29251951},

doi = {10.1037/adb0000336},

issn = {1939-1501 (Electronic); 0893-164X (Linking)},

year  = {2018},

date = {2018-02-01},

journal = {Psychol Addict Behav},

volume = {32},

number = {1},

pages = {64--75},

address = {Human Development and Family Studies, School of Human Ecology, University of Wisconsin-Madison.},

abstract = {Social relationships play an important role in the uptake, maintenance, and cessation of smoking behavior. However, little is known about the natural co-occurrence of social network features in adult smokers' networks and how multidimensional features of the network may connect to abstinence outcomes. The current investigation examined whether qualitatively distinct subgroups defined by multiple characteristics of the social network could be empirically identified within a sample of smokers initiating a quit attempt. Egocentric social network data were collected from 1571 smokers (58% female, 83% white) engaged in a 3-year smoking cessation clinical trial. Using nine indicator variables reflecting both risk and protective network features, finite mixture models identified five social network subgroups: High Stress/High Contact, Large and Supportive, Socially Disconnected, Risky Friends and Low Contact, and High Contact with Smokers and Light Drinkers. External variables supported the validity of the identified subgroups and the subgroups were meaningfully associated with baseline demographic, psychiatric, and tobacco measures. The Socially Disconnected subgroup was characterized by little social interaction, low levels of stress, and low exposure to social environmental smoking cues, and had the highest probability of successful cessation at 1 week compared with all other social network subgroups. At 6 months posttreatment its members had higher abstinence rates than members of the High Stress/High Contact subgroup and the Risky Friends and Low Contact subgroup. The present study highlights the heterogeneity of smokers' social milieus and suggests that network features, especially those entailing exposure to smoking cues and contexts, heighten risk for smoking cessation failure. (PsycINFO Database Record},

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pubstate = {published},

tppubtype = {article}

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@article{Manhart:2011aa,

title = {HPV vaccination among a community sample of young adult women.},

author = {Lisa E Manhart and Albert J Burgess-Hull and Charles B Fleming and Jennifer A Bailey and Kevin P Haggerty and Richard F Catalano},

url = {https://www.ncbi.nlm.nih.gov/pubmed/21640775},

doi = {10.1016/j.vaccine.2011.05.024},

issn = {1873-2518 (Electronic); 0264-410X (Linking)},

year  = {2011},

date = {2011-07-18},

journal = {Vaccine},

volume = {29},

number = {32},

pages = {5238--5244},

address = {Department of Epidemiology and Center for AIDS Research, University of Washington, Seattle, WA 98104, USA. lmanhart@u.washington.edu},

abstract = {OBJECTIVES: Despite the high efficacy of the human papillomavirus (HPV) vaccine, uptake has been slow and little data on psychosocial barriers to vaccination exist. METHODS: A community sample of 428 women enrolled in a longitudinal study of social development in the Seattle WA metropolitan area were interviewed about HPV vaccine status, attitudes, and barriers to HPV vaccination in spring 2008 or 2009 at approximately age 22. RESULTS: Nineteen percent of women had initiated vaccination, 10% had completed the series, and approximately 40% of unvaccinated women intended to get vaccinated. Peer approval was associated with vaccine initiation (adjusted prevalence ratio (APR) 2.1; 95% confidence interval 1.4-3.2) and intention to vaccinate (APR 1.4; 1.1-1.9). Belief the vaccine is <75% effective was associated with less initiation (APR 0.6; 0.4-0.9) or intention to vaccinate (APR 0.5; 0.4-0.7). Vaccine initiation was also less likely among cigarette smokers and illegal drug users, whereas intention to vaccinate was more common among women currently attending school or with >5 lifetime sex partners, but less common among women perceiving low susceptibility to HPV (APR 0.6; 0.5-0.9). CONCLUSIONS: HPV vaccination uptake was low in this community sample of young adult women. Increasing awareness of susceptibility to HPV and the high efficacy of the vaccine, along with peer interventions to increase acceptability, may be most effective.},

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pubstate = {published},

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}