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<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1249">
<title><![CDATA[Protective families in high- and low-risk environments: Implications for adolescent substance use]]></title>
<dc:creator><![CDATA[Cleveland,M. J.]]></dc:creator>
<dc:creator><![CDATA[ Feinberg,M. E.]]></dc:creator>
<dc:creator><![CDATA[ Greenberg,M. T.]]></dc:creator>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of Youth and Adolescence]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<refworks:created><![CDATA[11/13/2009 9:48:10 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:48:10 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1249</link>
<refworks:do><![CDATA[doi:10.1007/s10964-009-9395-y]]></refworks:do>
<refworks:id><![CDATA[1249]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1248">
<title><![CDATA[Mental health problems and overweight in a nationally representative sample of adolescents: Effects of race and ethnicity]]></title>
<dc:creator><![CDATA[BeLue,R.]]></dc:creator>
<dc:creator><![CDATA[ Francis,L. A.]]></dc:creator>
<dc:creator><![CDATA[ Colaco,B.]]></dc:creator>
<description><![CDATA[OBJECTIVES: In this study we examined the relation between mental health problems and weight in a population-based study of youth aged 12 to 17 years and whether the association between mental health problems and weight is moderated by race and ethnicity. METHODS: We used 2003 National Survey on Children's Health data. Logistic regression was used to arrive at adjusted odds ratios showing the relation between BMI and mental health problems. RESULTS: Compared with their nonoverweight counterparts, both white and Hispanic youth who were overweight were significantly more likely to report depression or anxiety, feelings of worthlessness or inferiority, behavior problems, and bullying of others. Odds ratios relating mental health problems and BMI in black subjects were not statistically significant except for physician diagnosis of depression. CONCLUSIONS: Our results suggest that, when addressing youth overweight status, mental health problems also need to be addressed. Given that the relationship between mental health problems and youth overweight differs according to race/ethnic group, public health programs that target overweight youth should be cognizant of potential comorbid mental health problems and that race/ethnicity may play a role in the relationship between mental health and overweight status.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Pediatrics]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[123]]></prism:volume> 
<prism:startingPage><![CDATA[697]]></prism:startingPage>
<prism:endingPage><![CDATA[702]]></prism:endingPage> 
<refworks:created><![CDATA[11/13/2009 6:51:59 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 6:52:38 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1248</link>
<refworks:FD><![CDATA[Feb]]></refworks:FD>
<refworks:k1><![CDATA[ Adolescent]]></refworks:k1>
<refworks:k1><![CDATA[ African Americans]]></refworks:k1>
<refworks:k1><![CDATA[ Child]]></refworks:k1>
<refworks:k1><![CDATA[ European Continental Ancestry Group]]></refworks:k1>
<refworks:k1><![CDATA[ Female]]></refworks:k1>
<refworks:k1><![CDATA[ Hispanic Americans]]></refworks:k1>
<refworks:k1><![CDATA[ Humans]]></refworks:k1>
<refworks:k1><![CDATA[ Male]]></refworks:k1>
<refworks:k1><![CDATA[ Mental Disorders/complications/ethnology]]></refworks:k1>
<refworks:k1><![CDATA[ Overweight/complications/ethnology]]></refworks:k1>
<refworks:k1><![CDATA[ Questionnaires]]></refworks:k1>
<refworks:no><![CDATA[JID: 0376422; ppublish]]></refworks:no>
<refworks:pp><![CDATA[United States]]></refworks:pp>
<refworks:sn><![CDATA[1098-4275]]></refworks:sn>
<refworks:ad><![CDATA[Departments of Health Policy and Administration, Penn State University, 604 Ford Building, University Park, PA 16802, USA. rzb10@psu.edu]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Comparative Study; Journal Article; AIM; IM]]></refworks:sf>
<refworks:do><![CDATA[10.1542/peds.2008-0687]]></refworks:do>
<refworks:id><![CDATA[1248]]></refworks:id>
<refworks:jo><![CDATA[Pediatrics]]></refworks:jo>
<refworks:an><![CDATA[PMID: 19171640; 123/2/697 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1247">
<title><![CDATA[One size does not fit all: identifying risk profiles for overweight in adolescent population subsets]]></title>
<dc:creator><![CDATA[BeLue,R.]]></dc:creator>
<dc:creator><![CDATA[ Francis,L. A.]]></dc:creator>
<dc:creator><![CDATA[ Rollins,B.]]></dc:creator>
<dc:creator><![CDATA[ Colaco,B.]]></dc:creator>
<description><![CDATA[PURPOSE: The purpose of this study is to identify population subgroups of adolescents who are homogenous with respect to sociodemographic factors and potentially modifiable risk and protective factors related to overweight status in a nationally representative sample of adolescents ages 12-17. METHODS: The data used for this study are from the Centers for Disease Control and National Center for Health Statistics' National Survey of Children's Health, 2003 (NSCH). Classification and Regression Trees (CART) were used to identify population segments of adolescents based on risk and protective factors for obesity. RESULTS: In the final CART model, 12 variables remained, including: poverty level, race, gender, participation in sports, number of family meals, family educational attainment, child physical activity, participation in free lunch programs, neighborhood safety and connectedness, TV viewing time, and child age in years. Poverty level was determined to be the most variable related to weight status in this sample of adolescents. Adolescents living in households below approximately the 300% poverty level were subject to a different constellation of predictors than adolescents living in homes above the 300% poverty level. CONCLUSIONS: Our results demonstrate how risk and protective factors related to obesity emerge differently among sociodemographic subgroups and the relative importance of these risk and protective factors in relation to adolescent overweight status. Interventions that work for one population subgroup may not work for another.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[The Journal of Adolescent Health]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[5]]></prism:number>
<prism:volume><![CDATA[45]]></prism:volume> 
<prism:startingPage><![CDATA[517]]></prism:startingPage>
<prism:endingPage><![CDATA[524]]></prism:endingPage> 
<refworks:created><![CDATA[11/13/2009 6:50:25 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 6:51:06 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1247</link>
<refworks:FD><![CDATA[Nov]]></refworks:FD>
<refworks:no><![CDATA[JID: 9102136; 2008/07/02 [received]; 2009/02/11 [revised]; 2009/03/17 [accepted]; 2009/05/28 [aheadofprint]; ppublish]]></refworks:no>
<refworks:pp><![CDATA[United States]]></refworks:pp>
<refworks:sn><![CDATA[1879-1972]]></refworks:sn>
<refworks:ad><![CDATA[Department of Health Policy and Administration, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. rzb10@psu.edu]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Journal Article; IM]]></refworks:sf>
<refworks:do><![CDATA[10.1016/j.jadohealth.2009.03.010]]></refworks:do>
<refworks:id><![CDATA[1247]]></refworks:id>
<refworks:wp><![CDATA[20090528]]></refworks:wp>
<refworks:an><![CDATA[PMID: 19837359; S1054-139X(09)00120-7 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1244">
<title><![CDATA[Identification and prediction of latent classes of weight-loss strategies among women]]></title>
<dc:creator><![CDATA[Lanza,S. T.]]></dc:creator>
<dc:creator><![CDATA[ Savage,J. S.]]></dc:creator>
<dc:creator><![CDATA[ Birch,L. L.]]></dc:creator>
<description><![CDATA[We apply latent class analysis (LCA) to quantify multidimensional patterns of weight-loss strategies in a sample of 197 women, and explore the degree to which dietary restraint, disinhibition, and other individual characteristics predict membership in latent classes of weight-loss strategies. Latent class models were fit to a set of 14 healthy and unhealthy weight-loss strategies. BMI, weight concern, body satisfaction, depression, dietary disinhibition and restraint, and the interaction of disinhibition and restraint were included as predictors of latent class membership. All analyses were conducted with PROC LCA, a recently developed SAS procedure available for download. Results revealed four subgroups of women based on their history of weight-loss strategies: No Weight Loss Strategy (10.0%), Dietary Guidelines (26.5%), Guidelines+Macronutrients (39.4%), and Guidelines+Macronutrients+Restrictive (24.2%). BMI, weight concerns, the desire to be thinner, disinhibition, and dietary restraint were all significantly related to weight-control strategy latent class. Among women with low dietary restraint, disinhibition increases the odds of engaging in any set of weight-loss strategies vs. none, whereas among medium- and high-restraint women disinhibition increases the odds of use of unhealthy vs. healthy strategies. LCA was an effective tool for organizing multiple weight-loss strategies in order to identify subgroups of individuals who have engaged in particular sets of strategies over time. This person-centered approach provides a measure weight-control status, where the different statuses are characterized by particular combinations of healthy and unhealthy weight-loss strategies.Obesity (2009) doi:10.1038/oby.2009.275.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Obesity]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<refworks:created><![CDATA[11/13/2009 6:17:37 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:30:34 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1244</link>
<refworks:FD><![CDATA[Aug 20]]></refworks:FD>
<refworks:no><![CDATA[JID: 101264860; 2009/08/20 [aheadofprint]; aheadofprint]]></refworks:no>
<refworks:sn><![CDATA[1930-7381]]></refworks:sn>
<refworks:ad><![CDATA[The Methodology Center, The Pennsylvania State University, University Park, Pennsylvania, USA.]]></refworks:ad>
<refworks:la><![CDATA[ENG]]></refworks:la>
<refworks:sf><![CDATA[JOURNAL ARTICLE]]></refworks:sf>
<refworks:do><![CDATA[10.1038/oby.2009.275]]></refworks:do>
<refworks:id><![CDATA[1244]]></refworks:id>
<refworks:wp><![CDATA[20090820]]></refworks:wp>
<refworks:jo><![CDATA[Obesity]]></refworks:jo>
<refworks:an><![CDATA[PMID: 19696754; oby2009275 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1241">
<title><![CDATA[Structural nested mean models for assessing time-varying effect moderation]]></title>
<dc:creator><![CDATA[Almirall,D.]]></dc:creator>
<dc:creator><![CDATA[ Ten Have,T.]]></dc:creator>
<dc:creator><![CDATA[ Murphy,S. A.]]></dc:creator>
<description><![CDATA[This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time varying and so are the covariates said to moderate its effect. Intermediate causal effects that describe time-varying causal effects of treatment conditional on past covariate history are introduced and considered as part of Robins' structural nested mean model. Two estimators of the intermediate causal effects, and their standard errors, are presented and discussed: The first is a proposed two-stage regression estimator. The second is Robins' G-estimator. The results of a small simulation study that begins to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented. The methodology is illustrated using longitudinal data from a depression study.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Biometrics]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<refworks:created><![CDATA[10/16/2009 3:07:31 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/16/2009 3:08:19 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1241</link>
<refworks:FD><![CDATA[Apr 13]]></refworks:FD>
<refworks:no><![CDATA[JID: 0370625; aheadofprint]]></refworks:no>
<refworks:sn><![CDATA[1541-0420]]></refworks:sn>
<refworks:ad><![CDATA[Center for Health Services Research in Primary Care, VA Medical Center, Durham, North Carolina 27705, U.S.A.]]></refworks:ad>
<refworks:la><![CDATA[ENG]]></refworks:la>
<refworks:sf><![CDATA[JOURNAL ARTICLE]]></refworks:sf>
<refworks:do><![CDATA[10.1111/j.1541-0420.2009.01238.x]]></refworks:do>
<refworks:id><![CDATA[1241]]></refworks:id>
<refworks:wp><![CDATA[20090413]]></refworks:wp>
<refworks:jo><![CDATA[Biometrics]]></refworks:jo>
<refworks:an><![CDATA[PMID: 19397586; BIOM1238 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1240">
<title><![CDATA[Lifestyle therapy changes and hypercholesterolemia: Identifying risk groups in a community sample of blacks and whites]]></title>
<dc:creator><![CDATA[BeLue,R.]]></dc:creator>
<dc:creator><![CDATA[ Lanza,S. T.]]></dc:creator>
<dc:creator><![CDATA[ Figaro,M. K.]]></dc:creator>
<description><![CDATA[OBJECTIVE: To examine diet and exercise lifestyle therapy change (LTC), behaviors and their relation to hypercholesterolemia in a community sample of Blacks and Whites. DESIGN: Latent class analysis (LCA) was employed to identify homogeneous subgroups of community dwelling Blacks and Whites related to LTC for hypercholesterolemia. LCA is a statistical technique used to identify subgroups of individuals who share a similar pattern of responses to a set of observations. The relation between hypercholesterolemia and latent class membership was assessed. PARTICIPANTS: Adults age 18 and over who participated in a county-level adaptation of the Behavioral Risk Factor Surveillance System. MAIN OUTCOME MEASURE: Hypercholesterolemia (absence or presence). RESULTS: Eleven unique latent classes of LTC behavior emerged from LCA models. Exercisers and Fat Reducers represented between 19% and 29% of each race-sex group. Latent class membership probabilities varied substantially across race and sex. Only Black women had a class of Contemplators (21.5%). Overall, men and Blacks with self reported hypercholesterolemia were more likely to engage only in fat reduction but not increase in vegetable consumption, reduction of fat or regular exercise (odds ratios range from 1.8-3.5). CONCLUSIONS: The distribution of diet and exercise related LTC behaviors in relation to self-reported hypercholesterolemia can help to identify, understand and tailor culturally and sex specific interventions based on the proportions of men and women in different latent classes.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Ethnicity & Disease]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[19]]></prism:volume> 
<prism:startingPage><![CDATA[142]]></prism:startingPage>
<prism:endingPage><![CDATA[147]]></prism:endingPage> 
<refworks:created><![CDATA[10/16/2009 3:02:47 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:25:09 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1240</link>
<refworks:FD><![CDATA[Spring]]></refworks:FD>
<refworks:k1><![CDATA[ Adolescent]]></refworks:k1>
<refworks:k1><![CDATA[ Adult]]></refworks:k1>
<refworks:k1><![CDATA[ African Americans/psychology]]></refworks:k1>
<refworks:k1><![CDATA[ Aged]]></refworks:k1>
<refworks:k1><![CDATA[ Aged, 80 and over]]></refworks:k1>
<refworks:k1><![CDATA[ Behavioral Risk Factor Surveillance System]]></refworks:k1>
<refworks:k1><![CDATA[ Diet, Fat-Restricted]]></refworks:k1>
<refworks:k1><![CDATA[ European Continental Ancestry Group/psychology]]></refworks:k1>
<refworks:k1><![CDATA[ Exercise]]></refworks:k1>
<refworks:k1><![CDATA[ Female]]></refworks:k1>
<refworks:k1><![CDATA[ Health Behavior/ethnology]]></refworks:k1>
<refworks:k1><![CDATA[ Humans]]></refworks:k1>
<refworks:k1><![CDATA[ Hypercholesterolemia/ethnology/psychology/therapy]]></refworks:k1>
<refworks:k1><![CDATA[ Life Style/ethnology]]></refworks:k1>
<refworks:k1><![CDATA[ Male]]></refworks:k1>
<refworks:k1><![CDATA[ Middle Aged]]></refworks:k1>
<refworks:k1><![CDATA[ Residence Characteristics]]></refworks:k1>
<refworks:k1><![CDATA[ Socioeconomic Factors]]></refworks:k1>
<refworks:k1><![CDATA[ Young Adult]]></refworks:k1>
<refworks:no><![CDATA[GR: P50DA010075/DA/NIDA NIH HHS/United States; GR: R03DA023032/DA/NIDA NIH HHS/United States; JID: 9109034; ppublish]]></refworks:no>
<refworks:pp><![CDATA[United States]]></refworks:pp>
<refworks:sn><![CDATA[1049-510X]]></refworks:sn>
<refworks:ad><![CDATA[Department of Health Policy and Administration, Methodology Center, Pennsylvania State University, University Park, PA 16803, USA. Rzb10@psu.edu]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Journal Article; Research Support, N.I.H., Extramural; IM]]></refworks:sf>
<refworks:id><![CDATA[1240]]></refworks:id>
<refworks:an><![CDATA[PMID: 19537224]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1202">
<title><![CDATA[A person-centered approach to individualizing a school-based universal prevention intervention]]></title>
<dc:creator><![CDATA[Caldwell,L. L.]]></dc:creator>
<dc:creator><![CDATA[ Bradley,S.]]></dc:creator>
<dc:creator><![CDATA[ Coffman,D. L.]]></dc:creator>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[American Journal of Drug and Alcohol Abuse]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[4]]></prism:number>
<prism:volume><![CDATA[35]]></prism:volume> 
<prism:startingPage><![CDATA[214]]></prism:startingPage>
<prism:endingPage><![CDATA[219]]></prism:endingPage> 
<refworks:created><![CDATA[10/2/2009 7:08:55 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/2/2009 7:08:55 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1202</link>
<refworks:k1><![CDATA[ Adolescent motivation]]></refworks:k1>
<refworks:k1><![CDATA[ latent class analysis]]></refworks:k1>
<refworks:k1><![CDATA[ leisure]]></refworks:k1>
<refworks:k1><![CDATA[ ontogenetic approach]]></refworks:k1>
<refworks:k1><![CDATA[ substance use]]></refworks:k1>
<refworks:id><![CDATA[1202]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1201">
<title><![CDATA[Using item response theory to detect differential item functioning in health disparities research]]></title>
<dc:creator><![CDATA[Coffman,D. L.]]></dc:creator>
<dc:creator><![CDATA[ BeLue,R.]]></dc:creator>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of Community Psychology]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[5]]></prism:number>
<prism:volume><![CDATA[37]]></prism:volume> 
<prism:startingPage><![CDATA[1]]></prism:startingPage>
<prism:endingPage><![CDATA[12]]></prism:endingPage> 
<refworks:created><![CDATA[10/2/2009 7:05:34 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/12/2009 4:23:16 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1201</link>
<refworks:id><![CDATA[1201]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1199">
<title><![CDATA[Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs]]></title>
<dc:creator><![CDATA[Collins,L. M.]]></dc:creator>
<dc:creator><![CDATA[ Dziak,J. J.]]></dc:creator>
<dc:creator><![CDATA[ Li,R.]]></dc:creator>
<description><![CDATA[An investigator who plans to conduct an experiment with multiple independent variables must decide whether to use a complete or reduced factorial design. This article advocates a resource management perspective on making this decision, in which the investigator seeks a strategic balance between service to scientific objectives and economy. Considerations in making design decisions include whether research questions are framed as main effects or simple effects; whether and which effects are aliased (confounded) in a particular design; the number of experimental conditions that must be implemented in a particular design and the number of experimental subjects the design requires to maintain the desired level of statistical power; and the costs associated with implementing experimental conditions and obtaining experimental subjects. In this article 4 design options are compared: complete factorial, individual experiments, single factor, and fractional factorial. Complete and fractional factorial designs and single-factor designs are generally more economical than conducting individual experiments on each factor. Although relatively unfamiliar to behavioral scientists, fractional factorial designs merit serious consideration because of their economy and versatility.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Psychological Methods]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[3]]></prism:number>
<prism:volume><![CDATA[14]]></prism:volume> 
<prism:startingPage><![CDATA[202]]></prism:startingPage>
<prism:endingPage><![CDATA[224]]></prism:endingPage> 
<refworks:created><![CDATA[9/30/2009 8:45:22 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:39:57 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1199</link>
<refworks:FD><![CDATA[Sep]]></refworks:FD>
<refworks:no><![CDATA[GR: K05 DA018206/DA/NIDA NIH HHS/United States; GR: P50 DA10075/DA/NIDA NIH HHS/United States; JID: 9606928; ppublish]]></refworks:no>
<refworks:pp><![CDATA[United States]]></refworks:pp>
<refworks:sn><![CDATA[1082-989X]]></refworks:sn>
<refworks:ad><![CDATA[The Methodology Center, Department of Human Development and Family Studies, Pennsylvania State University, PA 16801, USA. lmcollins@psu.edu]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Journal Article; Research Support, N.I.H., Extramural; IM]]></refworks:sf>
<refworks:do><![CDATA[10.1037/a0015826]]></refworks:do>
<refworks:id><![CDATA[1199]]></refworks:id>
<refworks:jo><![CDATA[Psychological Methods]]></refworks:jo>
<refworks:an><![CDATA[PMID: 19719358; 2009-12975-002 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1164">
<title><![CDATA[Sensitivities and specificities of diagnostic tests and infection prevalence of Schistosoma Haematobium estimated from data on adults in villages northwest of Accra, Ghana]]></title>
<dc:creator><![CDATA[Koukounari,A.]]></dc:creator>
<dc:creator><![CDATA[ Webster,J. P.]]></dc:creator>
<dc:creator><![CDATA[ Donnelly,C. A.]]></dc:creator>
<dc:creator><![CDATA[ Bray,B. C.]]></dc:creator>
<dc:creator><![CDATA[ Naples,J.]]></dc:creator>
<dc:creator><![CDATA[ Bosompem,K.]]></dc:creator>
<dc:creator><![CDATA[ Shiff,C.]]></dc:creator>
<description><![CDATA[Substantial uncertainties surround the sensitivities and specificities of diagnostic techniques for urinary schistosomiasis. We used latent class (LC) modeling to address this problem. In this study, 220 adults in three villages northwest of Accra, Ghana were examined using five Schistosoma haematobium diagnostic measures: microscopic examination of urine for detection of S. haematobium eggs, dipsticks for detection of hematuria, tests for circulating antigens, antibody tests, and ultrasound scans of the urinary system. Testing of the LC model indicated non-invariance of the performance of the diagnostic tests across different age groups, and measurement invariance held for males and females and for the three villages. We therefore recommend the use of LC models for comparison between and the identification of the most accurate schistosomiasis diagnostic tests. Furthermore, microscopy and hematuria dipsticks were indicated through these models as the most appropriate techniques for detection of S. haematobium infection.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[The American Journal of Tropical Medicine and Hygiene]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[3]]></prism:number>
<prism:volume><![CDATA[80]]></prism:volume> 
<prism:startingPage><![CDATA[435]]></prism:startingPage>
<prism:endingPage><![CDATA[441]]></prism:endingPage> 
<refworks:created><![CDATA[7/16/2009 8:06:35 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:29:49 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1164</link>
<refworks:FD><![CDATA[Mar]]></refworks:FD>
<refworks:k1><![CDATA[ Adult]]></refworks:k1>
<refworks:k1><![CDATA[ Animals]]></refworks:k1>
<refworks:k1><![CDATA[ Antibodies, Protozoan/blood]]></refworks:k1>
<refworks:k1><![CDATA[ Antigens, Protozoan/urine]]></refworks:k1>
<refworks:k1><![CDATA[ Female]]></refworks:k1>
<refworks:k1><![CDATA[ Ghana/epidemiology]]></refworks:k1>
<refworks:k1><![CDATA[ Hematuria]]></refworks:k1>
<refworks:k1><![CDATA[ Humans]]></refworks:k1>
<refworks:k1><![CDATA[ Immunoglobulin G/blood]]></refworks:k1>
<refworks:k1><![CDATA[ Likelihood Functions]]></refworks:k1>
<refworks:k1><![CDATA[ Male]]></refworks:k1>
<refworks:k1><![CDATA[ Microscopy]]></refworks:k1>
<refworks:k1><![CDATA[ Middle Aged]]></refworks:k1>
<refworks:k1><![CDATA[ Predictive Value of Tests]]></refworks:k1>
<refworks:k1><![CDATA[ Schistosoma haematobium/immunology/isolation & purification]]></refworks:k1>
<refworks:k1><![CDATA[ Schistosomiasis haematobia/blood/diagnosis/epidemiology/ultrasonography]]></refworks:k1>
<refworks:k1><![CDATA[ Sensitivity and Specificity]]></refworks:k1>
<refworks:k1><![CDATA[ Young Adult]]></refworks:k1>
<refworks:no><![CDATA[GR: 1R03CA103497-01/CA/NCI NIH HHS/United States; GR: P50-DA-010075/DA/NIDA NIH HHS/United States; GR: Medical Research Council/United Kingdom; JID: 0370507; 0 (Antibodies, Protozoan); 0 (Antigens, Protozoan); 0 (Immunoglobulin G); ppublish]]></refworks:no>
<refworks:pp><![CDATA[United States]]></refworks:pp>
<refworks:sn><![CDATA[1476-1645]]></refworks:sn>
<refworks:ad><![CDATA[Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College, London, United Kingdom. artemis.koukounari@imperial.ac.uk]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; AIM; IM]]></refworks:sf>
<refworks:id><![CDATA[1164]]></refworks:id>
<refworks:an><![CDATA[PMID: 19270295; 80/3/435 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1163">
<title><![CDATA[Transitions into and out of light and intermittent smoking during emerging adulthood]]></title>
<dc:creator><![CDATA[White,H. R.]]></dc:creator>
<dc:creator><![CDATA[ Bray,B. C.]]></dc:creator>
<dc:creator><![CDATA[ Fleming,C. B.]]></dc:creator>
<dc:creator><![CDATA[ Catalano,R. F.]]></dc:creator>
<description><![CDATA[INTRODUCTION: The purpose of this study was to examine transitions in smoking from adolescence into emerging adulthood and to identify factors that might influence these transitions, specifically, movement into and out of light and intermittent smoking. METHODS: This study used Markov models to examine movement across three stages of smoking (nonsmoking, light and intermittent smoking, and heavy smoking) from adolescence into emerging adulthood. Biannual data were collected from 990 young men and women from the 12th grade until 2 years after high school. RESULTS: At each timepoint, most youth were nonsmokers. Those who were heavy smokers in 12th grade had a 79% chance of also being heavy smokers 2 years after high school. Between 17% and 21% of participants were light and intermittent smokers at each timepoint, and the likelihood of remaining so at the next timepoint ranged from 56% to 72%. Less than one-half of the 12th-grade light and intermittent smokers were light and intermittent smokers 2 years later, and 3% of the sample were light and intermittent smokers across all assessments. Prevalence and transition rates did not differ by gender. College attendees reported less smoking than nonattendees before and after their transition to college, and attendees compared with nonattendees who smoked were less likely to transition from light and intermittent to heavy smoking and remain heavy smokers. Binge drinking was significantly related to 12th-grade smoking stage and to transitions from nonsmoking to smoking. Overall, few emerging adults maintained light and intermittent smoking consistently over time. DISCUSSION: Light and intermittent smoking during emerging adulthood may not be the same phenomenon as light and intermittent smoking in adulthood.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Nicotine and Tobacco Research]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[11]]></prism:volume> 
<prism:startingPage><![CDATA[211]]></prism:startingPage>
<prism:endingPage><![CDATA[219]]></prism:endingPage> 
<refworks:created><![CDATA[7/16/2009 8:01:42 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:31:56 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1163</link>
<refworks:FD><![CDATA[Feb]]></refworks:FD>
<refworks:no><![CDATA[GR: DA 08093-15/DA/NIDA NIH HHS/United States; GR: DA 10075-12/DA/NIDA NIH HHS/United States; GR: DA 17552-05/DA/NIDA NIH HHS/United States; JID: 9815751; OID: NLM: PMC2658905 [Available on 02/01/10]; 2009/02/20 [aheadofprint]; ppublish]]></refworks:no>
<refworks:pp><![CDATA[England]]></refworks:pp>
<refworks:sn><![CDATA[1469-994X]]></refworks:sn>
<refworks:ad><![CDATA[Rutgers Center of Alcohol Studies, Rutgers University, 607 Allison Road, Piscataway, NJ 08854-8001, USA. hewhite@rci.rutgers.edu]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Journal Article; Research Support, N.I.H., Extramural; IM]]></refworks:sf>
<refworks:do><![CDATA[10.1093/ntr/ntn017]]></refworks:do>
<refworks:id><![CDATA[1163]]></refworks:id>
<refworks:wp><![CDATA[20090220]]></refworks:wp>
<refworks:an><![CDATA[PMID: 19246434; ntn017 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1159">
<title><![CDATA[Variable selection for partially linear models with measurement errors]]></title>
<dc:creator><![CDATA[Liang,H.]]></dc:creator>
<dc:creator><![CDATA[ Li,R.]]></dc:creator>
<description><![CDATA[This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures. penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-for-attenuation approach. whereas the second procedure corrects the bias by using orthogonal regression. The sampling properties for the two procedures are investigated. The rate of convergence and the asymptotic normality of the resulting estimates are established. We further demonstrate that, with proper choices of the penalty functions and the regularization parameter. the resulting estimates perform asymptotically as well as an oracle property. Choice of smoothing parameters is also discussed. Finite sample performance of the proposed variable selection procedures is assessed by Monte Carlo simulation studies. We further illustrate the proposed procedures by an application.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of the American Statistical Association]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[485]]></prism:number>
<prism:volume><![CDATA[104]]></prism:volume> 
<prism:startingPage><![CDATA[234]]></prism:startingPage>
<refworks:created><![CDATA[7/7/2009 6:37:17 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:30:59 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1159</link>
<refworks:FD><![CDATA[Mar]]></refworks:FD>
<refworks:k1><![CDATA[ Asymptotic methods]]></refworks:k1>
<refworks:k1><![CDATA[ Regression analysis]]></refworks:k1>
<refworks:k1><![CDATA[ Bias]]></refworks:k1>
<refworks:sn><![CDATA[01621459]]></refworks:sn>
<refworks:lk><![CDATA[http://ezaccess.libraries.psu.edu/login?url=http://proquest.umi.com/pqdweb?did=1681064171&Fmt=7&clientId=9874&RQT=309&VName=PQD]]></refworks:lk>
<refworks:id><![CDATA[1159]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1155">
<title><![CDATA[A prospective longitudinal model of substance use onset among South African adolescents]]></title>
<dc:creator><![CDATA[Patrick,M. E.]]></dc:creator>
<dc:creator><![CDATA[ Collins,L. M.]]></dc:creator>
<dc:creator><![CDATA[ Smith,E.]]></dc:creator>
<dc:creator><![CDATA[ Caldwell,L. L.]]></dc:creator>
<dc:creator><![CDATA[ Flisher,A. J.]]></dc:creator>
<dc:creator><![CDATA[ Wegner,L.]]></dc:creator>
<description><![CDATA[Substance use onset among Colored adolescents between eighth and ninth grades in an urban area of Cape Town, South Africa was examined using latent transition analysis. Longitudinal self-report data regarding substance use (N= 1118, 50.9% female) were collected in 2004 and 2005. Results indicated that the pattern of onset was similar across genders; adolescents first tried either alcohol or cigarettes, followed by both, then dagga (cannabis), and then inhalants. The prevalence of lifetime cigarette use was slightly greater for females; dagga (cannabis) and inhalant use were greater for males. The similarity of developmental onset in the current sample to previous international work supports the promise of adapting prevention programs across contexts. The study's limitations are noted.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Substance Use and Misuse]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[5]]></prism:number>
<prism:volume><![CDATA[44]]></prism:volume> 
<prism:startingPage><![CDATA[647]]></prism:startingPage>
<prism:endingPage><![CDATA[662]]></prism:endingPage> 
<refworks:created><![CDATA[4/29/2009 9:05:32 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/18/2009 3:07:19 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1155</link>
<refworks:no><![CDATA[GR: DA 017491/DA/NIDA NIH HHS/United States; GR: DA 017629/DA/NIDA NIH HHS/United States; GR: DA 10075/DA/NIDA NIH HHS/United States; JID: 9602153; ppublish]]></refworks:no>
<refworks:pp><![CDATA[United States]]></refworks:pp>
<refworks:sn><![CDATA[1532-2491]]></refworks:sn>
<refworks:ad><![CDATA[Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48106-1248, USA. meganpat@isr.umich.edu]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Journal Article; Research Support, N.I.H., Extramural; IM]]></refworks:sf>
<refworks:do><![CDATA[10.1080/10826080902810244]]></refworks:do>
<refworks:id><![CDATA[1155]]></refworks:id>
<refworks:an><![CDATA[PMID: 19360538; 910365752 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1154">
<title><![CDATA[Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioral interventions]]></title>
<dc:creator><![CDATA[Collins,L. M.]]></dc:creator>
<dc:creator><![CDATA[ Chakraborty,B.]]></dc:creator>
<dc:creator><![CDATA[ Murphy,S. A.]]></dc:creator>
<dc:creator><![CDATA[ Strecher,V. J.]]></dc:creator>
<description><![CDATA[BACKGROUND: Many interventions in today's health sciences are multicomponent, and often one or more of the components are behavioral. Two approaches to building behavioral interventions empirically can be identified. The more typically used approach, labeled here the classical approach, consists of constructing a likely best intervention a priori, and then evaluating the intervention in a standard randomized controlled trial (RCT). By contrast, the emergent phased experimental approach involves programmatic phases of empirical research and discovery aimed at identifying individual intervention component effects and the best combination of components and levels. PURPOSE: The purpose of this article is to provide a head-to-head comparison between the classical and phased experimental approaches and thereby highlight the relative advantages and disadvantages of these approaches when they are used to select program components and levels so as to arrive at the most potent intervention. METHODS: A computer simulation was performed in which the classical and phased experimental approaches to intervention development were applied to the same randomly generated data. RESULTS: The phased experimental approach resulted in better mean intervention outcomes when the intervention effect size was medium or large, whereas the classical approach resulted in better mean intervention outcomes when the effect size was small. The phased experimental approach led to identification of the correct set of intervention components and levels at a higher rate than the classical approach across all conditions. LIMITATIONS: Some potentially important factors were not varied in the simulation, for example the underlying structural model and the number of intervention components. CONCLUSIONS: The phased experimental approach merits serious consideration, because it has the potential to enable intervention scientists to develop more efficacious behavioral interventions.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Clinical Trials]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[1]]></prism:number>
<prism:volume><![CDATA[6]]></prism:volume> 
<prism:startingPage><![CDATA[5]]></prism:startingPage>
<prism:endingPage><![CDATA[15]]></prism:endingPage> 
<refworks:created><![CDATA[3/31/2009 2:09:16 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/13/2009 9:28:55 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=047091193814000000/RWWS3A1312351/Newest%20Articles&amp;rn=1154</link>
<refworks:FD><![CDATA[Feb]]></refworks:FD>
<refworks:no><![CDATA[GR: K05 DA018206/DA/NIDA NIH HHS/United States; GR: P50 DA10075/DA/NIDA NIH HHS/United States; JID: 101197451; ppublish]]></refworks:no>
<refworks:sn><![CDATA[1740-7745]]></refworks:sn>
<refworks:ad><![CDATA[The Methodology Center and Department of Human Development and Family Studies, Penn State, University Park, PA 16801, USA. lmcollins@psu.edu.]]></refworks:ad>
<refworks:la><![CDATA[eng]]></refworks:la>
<refworks:sf><![CDATA[Journal Article; Research Support, N.I.H., Extramural; IM]]></refworks:sf>
<refworks:do><![CDATA[10.1177/1740774508100973]]></refworks:do>
<refworks:id><![CDATA[1154]]></refworks:id>
<refworks:jo><![CDATA[Clinical Trials]]></refworks:jo>
<refworks:an><![CDATA[PMID: 19254929; 6/1/5 [pii]]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>

</rdf:RDF>