The prevalence rate of childhood obesity has increased rapidly worldwide. The childhood obesity epidemic is associated with many adverse health consequences in children as well as a financial burden for a nation’s economy. A meta-analysis was conducted to investigate the effectiveness of school-based childhood obesity prevention programs in preventing childhood obesity. The objectives of this study were to 1) identify the most effective childhood obesity prevention programs through effect size comparison, and 2) identify important program components that affect the effectiveness of the intervention through subgroup analysis. The Comprehensive Meta-Analysis (CMA) program was used for all statistical analyses. Results of the meta-analysis demonstrated that the summary effect size was small (d = 0.039, 95% confidence interval). The school-based program identified in the meta-analysis as the most effective had a d value of 0.368. Subgroup analyses were performed because this meta-analysis study was heterogeneous (Q = 167.774, p = 0.001) with an I2 value of 68.410%. The subgroup moderators were length of program duration, age of participants, nutrition, physical activity, parental involvement, specialist involvement, and theory based versus non-theory based intervention programs. Subgroup analyses demonstrated that significant differences (p < 0.05) occurred among the moderator components. Programs that targeted younger children less than ten years old and programs that were theory based were more effective. The meta-analysis study contained publication bias because the funnel plot was skewed and smaller studies were missing. To further explore the publication bias problem, Classic fail-safe N and Duval and Tweedie’s trim and fill analyses were performed. Classic fail-safe N indicated that two programs were missing from the present study to achieve a non-biased result. The Duval and Tweedie’s trim and fill analysis demonstrated that a small mean effect size difference was detected between the present observed studies and the unbiased effect size. The small mean effect size difference indicated that the results and the reported effect sizes in this meta-analysis study were valid.

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Background: Many randomized controlled trials (RCTs) and meta-analyses include analyses of effect modification (also known as subgroup, interaction, or moderation analyses). Methodologists have widely acknowledged the challenges in deciding whether an apparent effect modification is credible or likely the result of chance or bias. Various sets of credibility criteria are available (Chapter 2 provides an example) but are inconsistent, vague in wording, lack guidance for deciding on overall credibility, and have not been systematically tested. Objective: To systematically develop a formal instrument to assess the credibility of effect modification analyses (ICEMAN) in RCTs and meta-analyses of RCTs. Methods: Key steps in the development process included 1) a systematic survey of the literature to identify available criteria, rationales, and previous instruments, 2) a formal consensus study among 10 leading experts, and 3) a formal user-testing study to refine the instrument based on interviews with trial investigators, systematic reviewer authors, and journal editors who applied drafts of the instrument to published claims of effect modification. Results: The systematic survey identified 150 relevant publications, 36 candidate credibility criteria with associated rationales, and 30 existing checklists (Chapter 3). The consensus study consisted of two main video conferences and multiple rounds of written discussion. The user-testing involved 17 users (including systematic review authors, trial investigators, and journal editors) who suggested substantial improvements based on detailed interviews. The final instrument provides separate versions for RCTs (five core questions) and meta-analyses (eight core questions) with explicit response options, and an overall credibility rating ranging from very low to high credibility. A detailed manual provides rationales, supporting references, examples from the literature, and suggestions for use in combination with other quality appraisal tools and reporting (Chapter 4). Discussion: ICEMAN is a rigorously developed instrument to evaluate claims of effect modification and addresses the main limitations of previous approaches. / Thesis / Doctor of Philosophy (PhD) / Randomized controlled trials and meta-analyses provide the best available evidence to evaluate whether effects of a therapy vary among individual patients. Efforts to decide whether treatment effects differ across patients are important and frequently done but difficult to interpret. The fundamental challenge is to decide whether apparent differences in effect are real or due to chance. To aid this decision, experts have suggested various sets of credibility criteria, all with important limitations. This thesis documents how we systematically addressed the limitations of previous approaches. Key steps were a systematic survey of the available credibility criteria, a consensus study among leading methodologists, and a formal user-testing study. The result is a new instrument for assessing the credibility of effect modification analyses (ICEMAN).

<p>This dissertation is devoted to modeling complex data from the</p><p>Bayesian perspective via constructing priors with latent structures.</p><p>There are three major contexts in which this is done -- strategies for</p><p>the analysis of dynamic longitudinal data, estimating</p><p>shape-constrained functions, and identifying subgroups. The</p><p>methodology is illustrated in three different</p><p>interdisciplinary contexts: (1) adaptive measurement testing in</p><p>education; (2) emulation of computer models for vehicle crashworthiness; and (3) subgroup analyses based on biomarkers.</p><p>Chapter 1 presents an overview of the utilized latent structured</p><p>priors and an overview of the remainder of the thesis. Chapter 2 is</p><p>motivated by the problem of analyzing dichotomous longitudinal data</p><p>observed at variable and irregular time points for adaptive</p><p>measurement testing in education. One of its main contributions lies</p><p>in developing a new class of Dynamic Item Response (DIR) models via</p><p>specifying a novel dynamic structure on the prior of the latent</p><p>trait. The Bayesian inference for DIR models is undertaken, which</p><p>permits borrowing strength from different individuals, allows the</p><p>retrospective analysis of an individual's changing ability, and</p><p>allows for online prediction of one's ability changes. Proof of</p><p>posterior propriety is presented, ensuring that the objective</p><p>Bayesian analysis is rigorous.</p><p>Chapter 3 deals with nonparametric function estimation under</p><p>shape constraints, such as monotonicity, convexity or concavity. A</p><p>motivating illustration is to generate an emulator to approximate a computer</p><p>model for vehicle crashworthiness. Although Gaussian processes are</p><p>very flexible and widely used in function estimation, they are not</p><p>naturally amenable to incorporation of such constraints. Gaussian</p><p>processes with the squared exponential correlation function have the</p><p>interesting property that their derivative processes are also</p><p>Gaussian processes and are jointly Gaussian processes with the</p><p>original Gaussian process. This allows one to impose shape constraints</p><p>through the derivative process. Two alternative ways of incorporating derivative</p><p>information into Gaussian processes priors are proposed, with one</p><p>focusing on scenarios (important in emulation of computer</p><p>models) in which the function may have flat regions.</p><p>Chapter 4 introduces a Bayesian method to control for multiplicity</p><p>in subgroup analyses through tree-based models that limit the</p><p>subgroups under consideration to those that are a priori plausible.</p><p>Once the prior modeling of the tree is accomplished, each tree will</p><p>yield a statistical model; Bayesian model selection analyses then</p><p>complete the statistical computation for any quantity of interest,</p><p>resulting in multiplicity-controlled inferences. This research is</p><p>motivated by a problem of biomarker and subgroup identification to</p><p>develop tailored therapeutics. Chapter 5 presents conclusions and</p><p>some directions for future research.</p> / Dissertation

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Incomplete data often arise in the study of life history processes. Examples include missing responses, missing covariates, and unobservable latent processes in addition to right censoring. This thesis is on the development of statistical models and methods to address these problems as they arise in oncology and chronic disease. Methods of estimation and inference in parametric, weakly parametric and semiparametric settings are investigated. Studies of chronic diseases routinely sample individuals subject to conditions on an event time of interest. In epidemiology, for example, prevalent cohort studies aiming to evaluate risk factors for survival following onset of dementia require subjects to have survived to the point of screening. In clinical trials designed to assess the effect of experimental cancer treatments on survival, patients are required to survive from the time of cancer diagnosis to recruitment. Such conditions yield samples featuring left-truncated event time distributions. Incomplete covariate data often arise in such settings, but standard methods do not deal with the fact that the covariate distribution is also affected by left truncation. We develop a likelihood and algorithm for estimation for dealing with incomplete covariate data in such settings. An expectation-maximization algorithm deals with the left truncation by using the covariate distribution conditional on the selection criterion. An extension to deal with sub-group analyses in clinical trials is described for the case in which the stratification variable is incompletely observed. In studies of affective disorder, individuals are often observed to experience recurrent symptomatic exacerbations of symptoms warranting hospitalization. Interest lies in modeling the occurrence of such exacerbations over time and identifying associated risk factors to better understand the disease process. In some patients, recurrent exacerbations are temporally clustered following disease onset, but cease to occur after a period of time. We develop a dynamic mover-stayer model in which a canonical binary variable associated with each event indicates whether the underlying disease has resolved. An individual whose disease process has not resolved will experience events following a standard point process model governed by a latent intensity. If and when the disease process resolves, the complete data intensity becomes zero and no further events will arise. An expectation-maximization algorithm is developed for parametric and semiparametric model fitting based on a discrete time dynamic mover-stayer model and a latent intensity-based model of the underlying point process. The method is applied to a motivating dataset from a cohort of individuals with affective disorder experiencing recurrent hospitalization for their mental health disorder. Interval-censored recurrent event data arise when the event of interest is not readily observed but the cumulative event count can be recorded at periodic assessment times. Extensions on model fitting techniques for the dynamic mover-stayer model are discussed and incorporate interval censoring. The likelihood and algorithm for estimation are developed for piecewise constant baseline rate functions and are shown to yield estimators with small empirical bias in simulation studies. Data on the cumulative number of damaged joints in patients with psoriatic arthritis are analysed to provide an illustrative application.

Evidence is limited regarding whether periodontal treatment improves hemoglobin A1c (HbA1c) among people with prediabetes and periodontal disease, and it is unknown whether improvement of metabolic status persists >3 mo. In an exploratory post hoc analysis of the multicenter randomized controlled trial “Antibiotika und Parodontitis” (Antibiotics and Periodontitis)—a prospective, stratified, double-blind study—we assessed whether nonsurgical periodontal treatment with or without an adjunctive systemic antibiotic treatment affects HbA1c and high-sensitivity C-reactive protein (hsCRP) levels among periodontitis patients with normal HbA1c (≤5.7%, n = 218), prediabetes (5.7% < HbA1c < 6.5%, n = 101), or unknown diabetes (HbA1c ≥ 6.5%, n = 8) over a period of 27.5 mo. Nonsurgical periodontal treatment reduced mean pocket probing depth by >1 mm in both groups. In the normal HbA1c group, HbA1c values remained unchanged at 5.0% (95% CI, 4.9% to 6.1%) during the observation period. Among periodontitis patients with prediabetes, HbA1c decreased from 5.9% (95% CI, 5.9% to 6.0%) to 5.4% (95% CI, 5.3% to 5.5%) at 15.5 mo and increased to 5.6% (95% CI, 5.4% to 5.7%) after 27.5 mo. At 27.5 mo, 46% of periodontitis patients with prediabetes had normal HbA1c levels, whereas 47.9% remained unchanged and 6.3% progressed to diabetes. Median hsCRP values were reduced in the normal HbA1c and prediabetes groups from 1.2 and 1.4 mg/L to 0.7 and 0.7 mg/L, respectively. Nonsurgical periodontal treatment may improve blood glucose values among periodontitis patients with prediabetes (ClinicalTrials.gov NCT00707369).

Les habitudes de consommation de substances psychoactives, le stress, l’obésité et les traits cardiovasculaires associés seraient en partie reliés aux mêmes facteurs génétiques. Afin d’explorer cette hypothèse, nous avons effectué, chez 119 familles multi-générationnelles québécoises de la région du Saguenay-Lac-St-Jean, des études d’association et de liaison pangénomiques pour les composantes génétiques : de la consommation usuelle d’alcool, de tabac et de café, de la réponse au stress physique et psychologique, des traits anthropométriques reliés à l’obésité, ainsi que des mesures du rythme cardiaque (RC) et de la pression artérielle (PA). 58000 SNPs et 437 marqueurs microsatellites ont été utilisés et l’annotation fonctionnelle des gènes candidats identifiés a ensuite été réalisée. Nous avons détecté des corrélations phénotypiques significatives entre les substances psychoactives, le stress, l’obésité et les traits hémodynamiques. Par exemple, les consommateurs d’alcool et de tabac ont montré un RC significativement diminué en réponse au stress psychologique. De plus, les consommateurs de tabac avaient des PA plus basses que les non-consommateurs. Aussi, les hypertendus présentaient des RC et PA systoliques accrus en réponse au stress psychologique et un indice de masse corporelle (IMC) élevé, comparativement aux normotendus. D’autre part, l’utilisation de tabac augmenterait les taux corporels d’épinéphrine, et des niveaux élevés d’épinéphrine ont été associés à des IMC diminués. Ainsi, en accord avec les corrélations inter-phénotypiques, nous avons identifié plusieurs gènes associés/liés à la consommation de substances psychoactives, à la réponse au stress physique et psychologique, aux traits reliés à l’obésité et aux traits hémodynamiques incluant CAMK4, CNTN4, DLG2, DAG1, FHIT, GRID2, ITPR2, NOVA1, NRG3 et PRKCE. Ces gènes codent pour des protéines constituant un réseau d’interactions, impliquées dans la plasticité synaptique, et hautement exprimées dans le cerveau et ses tissus associés. De plus, l’analyse des sentiers de signalisation pour les gènes identifiés (P = 0,03) a révélé une induction de mécanismes de Potentialisation à Long Terme. Les variations des traits étudiés seraient en grande partie liées au sexe et au statut d’hypertension. Pour la consommation de tabac, nous avons noté que le degré et le sens des corrélations avec l’obésité, les traits hémodynamiques et le stress sont spécifiques au sexe et à la pression artérielle. Par exemple, si des variations ont été détectées entre les hommes fumeurs et non-fumeurs (anciens et jamais), aucune différence n’a été observée chez les femmes. Nous avons aussi identifié de nombreux traits reliés à l’obésité dont la corrélation avec la consommation de tabac apparaît essentiellement plus liée à des facteurs génétiques qu’au fait de fumer en lui-même. Pour le sexe et l’hypertension, des différences dans l’héritabilité de nombreux traits ont également été observées. En effet, des analyses génétiques sur des sous-groupes spécifiques ont révélé des gènes additionnels partageant des fonctions synaptiques : CAMK4, CNTN5, DNM3, KCNAB1 (spécifique à l’hypertension), CNTN4, DNM3, FHIT, ITPR1 and NRXN3 (spécifique au sexe). Ces gènes codent pour des protéines interagissant avec les protéines de gènes détectés dans l’analyse générale. De plus, pour les gènes des sous-groupes, les résultats des analyses des sentiers de signalisation et des profils d’expression des gènes ont montré des caractéristiques similaires à celles de l’analyse générale. La convergence substantielle entre les déterminants génétiques des substances psychoactives, du stress, de l’obésité et des traits hémodynamiques soutiennent la notion selon laquelle les variations génétiques des voies de plasticité synaptique constitueraient une interface commune avec les différences génétiques liées au sexe et à l’hypertension. Nous pensons, également, que la plasticité synaptique interviendrait dans de nombreux phénotypes complexes influencés par le mode de vie. En définitive, ces résultats indiquent que des approches basées sur des sous-groupes et des réseaux amélioreraient la compréhension de la nature polygénique des phénotypes complexes, et des processus moléculaires communs qui les définissent. / Links among substance use, obesity, stress and related cardiovascular outcomes may be in part due to shared genetic factors. To investigate this hypothesis, we performed genome-wide linkage and association scans for genetic components of habitual alcohol, tobacco and coffee use, response to mental and physical stress, obesity related anthropometric traits and heart rate (HR) and blood pressure (BP) measurements in 119 multigenerational French Canadian families from founder population of Saguenay-Lac-St-Jean region using 58000 SNPs and 437 microsatellite markers and followed with functional annotation on resulted genes. We found significant phenotypic correlations among substance use, obesity, stress and hemodynamic traits. For instance, alcohol and tobacco users had attenuated HR response to mental stress; moreover, tobacco users had lower BP compared to non users; Hypertensives had stronger HR and systolic blood pressure (SBP) response to mental stress and higher body mass index (BMI), compared to normotensives; Use of tobacco seemed to increase the epinephrine level in body and higher epinephrine level was correlated with lower BMI. Consistent with phenotypic relatedness, we found numerous shared genes associated / linked to substance use, obesity-related traits, response to mental and physical stress and hemodynamic traits including CAMK4, CNTN4, DLG2, DAG1, FHIT, GRID2, ITPR2, NOVA1, NRG3 and PRKCE forming protein interaction network, involved in synaptic plasticity and highly expressed in brain related tissues; moreover, pathway analysis on identified genes pointed (P = 0.03) to Long-Term Potentiation pathway. Large portions of variation of studied traits were explained by sex and hypertension status, focusing on tobacco use we noted that degree and the direction of correlations of obesity, hemodynamic and stress related traits with tobacco use vary according to sex and hypertension status; for instance, while in males, current tobacco users were slender compared to never or former tobacco users, there were no such differences in females; moreover, we found several obesity related traits that their correlations with smoking behavior seemingly root in genetic factors rather than smoking effect itself. Sex- and hypertension differences in heritabilities of many of these traits were also observed; meanwhile, specific subgroup genetic analyses uncovered additional shared synaptic genes among these traits including CAMK4, CNTN5, DNM3, KCNAB1 (Hypertension-specific), CNTN4, DNM3, FHIT, ITPR1 and NRXN3 (Sex-specific) having protein interactions with genes driven from general analysis; moreover, the results of pathway analysis and reported gene expression profiles of resulted genes from subgroup analyses revealed similar characteristics to those from general analysis. The substantial overlap among genomic determinants of substance use, stress, obesity and hemodynamic traits supports the notion that the genetic variations in pathways of synaptic plasticity may be a common interface behind them as well as observed sex and hypertension genetic differences, we also think synaptic plasticity may underlie many complex phenotypes in which life style is a contributing factor; moreover, our findings indicate considering subgroup and network-based approaches enhance understanding of polygenic nature of complex phenotypes as well as shared molecular underpinnings among them.