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Booil Jo

Bio: Booil Jo is an academic researcher from Stanford University. The author has contributed to research in topics: Randomized controlled trial & Medicine. The author has an hindex of 45, co-authored 120 publications receiving 6336 citations. Previous affiliations of Booil Jo include University of California, Los Angeles & Palo Alto University.


Papers
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Journal ArticleDOI
TL;DR: Although both treatments led to considerable improvement and were similarly effective in producing full remission at EOT, FBT was more effective in facilitating full remission in both follow-up points.
Abstract: means. Secondary outcome measures included partial remission rates (85% of expected weight for height plus those who were in full remission) and changes in body mass index percentile and eating-related psychopathology. Results: There were no differences in full remission between treatments at EOT. However, at both the 6- and 12month follow-up, FBT was significantly superior to AFT on this measure. Family-based treatment was significantly superior for partial remission at EOT but not at follow-up. In addition, body mass index percentile at EOT was significantly superior for FBT, but this effect was not found at follow-up. Participants in FBT also had greater changes in Eating Disorder Examination score at EOT than those in AFT, but there were no differences at follow-up. Conclusion: Although both treatments led to considerable improvement and were similarly effective in producing full remission at EOT, FBT was more effective in facilitating full remission at both follow-up points. Trial Registration: clinicaltrials.gov Identifier: NCT00149786.

677 citations

Journal ArticleDOI
TL;DR: The authors proposed growth mixture modeling to assess intervention effects in longitudinal randomized trials, which allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories.
Abstract: SUMMARY This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finitemixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as non-normative growth. The analysis identifies subgroup membership and allows theory-based modeling of intervention effects in the different subgroups. An example is presented concerning a randomized ∗This paper was presented at the 2000 Joint Statistical Meetings in Indianapolis, Indiana, August 13–17, 2000. It is a revised version of the 1997 paper General Growth Mixture Modeling of Latent Trajectory Classes: Perspectives and Prospects, an earlier version of which was presented at the Prevention Science Methodology Group meeting in Tempe, Arizona, May 22–24, 1996. † To whom correspondence should be addressed

386 citations

25 Oct 2001
TL;DR: This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials and presents an example of a randomized intervention in Baltimore public schools aimed at reducing aggressive classroom behavior, where only students who were initially more aggressive showed benefits from the intervention.
Abstract: This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved hetero-geneity among the subjects using a finite mixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as non-normative growth. The analysis identi?es subgroup membership and allows theory-based modeling of intervention effects in the different subgroups. An example is presented concerning a randomized intervention in Baltimore public schools aimed at reducing aggressive classroom behavior, where only students who were initially more aggressive showed benefits from the intervention.

382 citations

Journal ArticleDOI
TL;DR: This longitudinal study of adolescents with 22q11.2 deletion syndrome identified the catechol-O-methyltransferase low-activity allele (COMTL) as a risk factor for decline in prefrontal cortical volume and cognition, as well as for the consequent development of psychotic symptoms during adolescence.
Abstract: Although schizophrenia is strongly hereditary, there are limited data regarding biological risk factors and pathophysiological processes. In this longitudinal study of adolescents with 22q11.2 deletion syndrome, we identified the catechol-O-methyltransferase low-activity allele (COMT(L)) as a risk factor for decline in prefrontal cortical volume and cognition, as well as for the consequent development of psychotic symptoms during adolescence. The 22q11.2 deletion syndrome is a promising model for identifying biomarkers related to the development of schizophrenia.

302 citations

Journal ArticleDOI
TL;DR: Although both DBT-BED and ACGT reduced binge eating, DBT -BED showed significantly fewer dropouts and greater initial efficacy than ACGT, and the lack of differential findings over follow-up suggests that the hypothesized specific effects of D BTED do not show long-term impact beyond those attributable to nonspecific common therapeutic factors.

273 citations


Cited by
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24 Mar 2010-BMJ
TL;DR: This update of the CONSORT statement improves the wording and clarity of the previous checklist and incorporates recommendations related to topics that have only recently received recognition, such as selective outcome reporting bias.
Abstract: Overwhelming evidence shows the quality of reporting of randomised controlled trials (RCTs) is not optimal. Without transparent reporting, readers cannot judge the reliability and validity of trial findings nor extract information for systematic reviews. Recent methodological analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which are considered the gold standard for evaluating interventions because of their ability to minimise or avoid bias. A group of scientists and editors developed the CONSORT (Consolidated Standards of Reporting Trials) statement to improve the quality of reporting of RCTs. It was first published in 1996 and updated in 2001. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have endorsed the CONSORT statement. The statement facilitates critical appraisal and interpretation of RCTs. During the 2001 CONSORT revision, it became clear that explanation and elaboration of the principles underlying the CONSORT statement would help investigators and others to write or appraise trial reports. A CONSORT explanation and elaboration article was published in 2001 alongside the 2001 version of the CONSORT statement. After an expert meeting in January 2007, the CONSORT statement has been further revised and is published as the CONSORT 2010 Statement. This update improves the wording and clarity of the previous checklist and incorporates recommendations related to topics that have only recently received recognition, such as selective outcome reporting bias. This explanatory and elaboration document-intended to enhance the use, understanding, and dissemination of the CONSORT statement-has also been extensively revised. It presents the meaning and rationale for each new and updated checklist item providing examples of good reporting and, where possible, references to relevant empirical studies. Several examples of flow diagrams are included. The CONSORT 2010 Statement, this revised explanatory and elaboration document, and the associated website (www.consort-statement.org) should be helpful resources to improve reporting of randomised trials.

5,957 citations

Journal ArticleDOI
TL;DR: This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposefully sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.
Abstract: Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be used most commonly in implementation research. However, combining sampling strategies may be more appropriate to the aims of implementation research and more consistent with recent developments in quantitative methods. This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposeful sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.

5,601 citations

Journal Article

4,293 citations

Posted Content
TL;DR: In this paper, the authors investigated conditions sufficient for identification of average treatment effects using instrumental variables and showed that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect.
Abstract: We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

3,154 citations