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Joycelyne E Ewusie,1,2 Lehana Thabane,1 Joseph Beyene,1 Sharon E Straus,3 Jemila S Hamid1,2,4 1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; 2School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; 3Li Ka Shing Knowledge Institute of St Michael’s Hospital, Toronto, Ontario, Canada; 4Children’s Hospital of Eastern Ontario, Ottawa, Ontario, CanadaCorrespondence: Jemila S HamidChildren’s Hospital of Eastern Ontario, Ottawa, Ontario, CanadaTel +1 613 737-7600 x 4194Email jhamid@uottawa.caBackground: Segmented regression (SR) is the most common statistical method used in the analysis of interrupted time series (ITS) data. However, this modeling strategy is indicated to produce spurious results when applied to aggregated data. For multicenter ITS studies, data at a given time point are often aggregated across different participants and settings; thus, conventional segmented regression analysis may not be an optimal approach. Our objective is to provide a robust method for analysis of ITS data, while accounting for two sources of heterogeneity, between participants and across sites.Methods: We present a methodological framework within the segmented regression modeling strategy, where we introduced weights to account for between-participant variation and the differences across multiple sites. We empirically compared the proposed weighted segmented regression (wSR) with the conventional SR as well as with a previously published pooled analysis method using data from the Mobility of Vulnerable Elders in Ontario (MOVE-ON) project, a multisite ITS study.Results: Overall, the wSR produced the most precise estimates, where they had the narrowest 95% CI, while the conventional SR method resulted in the least precise estimates. Our method also resulted in increased power. The pooled analysis method and the wSR had comparable results when there were ≤ 4 sites included in the overall analysis and when there was moderate to high between-site heterogeneity as measured by the I2 statistic.Conclusion: Incorporating participant-level and site-level variability led to estimates that were more precise and accurate in determining the magnitude of the effect of an intervention and led to increased statistical power. This underscores the importance of accounting for the inherent variability in aggregated data. Extensive simulations are required to further assess the methods in a wide range of scenarios and outcome types.Keywords: aggregated data, weighted segmented regression, pooled analysis, interrupted time series, multisite studies

Joycelyne E Ewusie,1 Charlene Soobiah,2,3 Erik Blondal,2,3 Joseph Beyene,1 Lehana Thabane,1,4 Jemila S Hamid1,5 1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; 2Li Ka Shing Knowledge Institute of St Michael’s Hospital, Toronto, Ontario, Canada; 3Institute of Health Policy Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada; 4Biostatistics Unit, Father Sean O’Sullivan Research Centre, St Joseph’s Healthcare, Hamilton, Ontario, Canada; 5Clinical Research Unit, Children’s Hospital of Eastern Ontario, Ottawa, ON, CanadaCorrespondence: Jemila S HamidChildren’s Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON K1H 8L1, CanadaTel +1 (613) 737-7600 x 4194Email jhamid@uottawa.caObjective: Interrupted time series (ITS) designs are robust quasi-experimental designs commonly used to evaluate the impact of interventions and programs implemented in healthcare settings. This scoping review aims to 1) identify and summarize existing methods used in the analysis of ITS studies conducted in health research, 2) elucidate their strengths and limitations, 3) describe their applications in health research and 4) identify any methodological gaps and challenges.Design: Scoping review.Data Sources: Searches were conducted in MEDLINE, JSTOR, PUBMED, EMBASE, CINAHL, Web of Science and the Cochrane Library from inception until September 2017.Study Selection: Studies in health research involving ITS methods or reporting on the application of ITS designs.Data Extraction: Screening of studies was completed independently and in duplicate by two reviewers. One reviewer extracted the data from relevant studies in consultations with a second reviewer. Results of the review were presented with respect to methodological and application areas, and data were summarized using descriptive statistics.Results: A total of 1389 articles were included, of which 98.27% (N=1365) were application papers. Segmented linear regression was the most commonly used method (26%, N=360). A small percentage (1.73%, N=24) were methods papers, of which 11 described either the development of novel methods or improvement of existing methods, 7 adapted methods from other areas of statistics, while 6 provided comparative assessment of conventional ITS methods.Conclusion: A significantly increasing trend in ITS use over time is observed, where its application in health research almost tripled within the last decade. Several statistical methods are available for analyzing ITS data. Researchers should consider the types of data and validate the required assumptions for the various methods. There is a significant methodological gap in ITS analysis involving aggregated data, where analyses involving such data did not account for heterogeneity across patients and hospital settings.Keywords: interrupted time series, segmented linear regression, ARIMA, limitations, methods, scoping review

Taddele Kibret,1 Danielle Richer,2 Joseph Beyene1,2 1Department of Mathematics and Statistics, 2Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada Abstract: Network meta-analysis (NMA) has emerged as a useful analytical tool allowing comparison of multiple treatments based on direct and indirect evidence. Commonly, a hierarchical Bayesian NMA model is used, which allows rank probabilities (the probability that each treatment is best, second best, and so on) to be calculated for decision making. However, the statistical properties of rank probabilities are not well understood. This study investigates how rank probabilities are affected by various factors such as unequal number of studies per comparison in the network, the sample size of individual studies, the network configuration, and effect sizes between treatments. In order to explore these factors, a simulation study of four treatments (three equally effective treatments and one less effective reference) was conducted. The simulation illustrated that estimates of rank probabilities are highly sensitive to both the number of studies per comparison and the overall network configuration. An unequal number of studies per comparison resulted in biased estimates of treatment rank probabilities for every network considered. The rank probability for the treatment that was included in the fewest number of studies was biased upward. Conversely, the rank of the treatment included in the most number of studies was consistently underestimated. When the simulation was altered to include three equally effective treatments and one superior treatment, the hierarchical Bayesian NMA model correctly identified the most effective treatment, regardless of all factors varied. The results of this study offer important insight into the ability of NMA models to rank treatments accurately under several scenarios. The authors recommend that health researchers use rank probabilities cautiously in making important decisions. Keywords: multiple treatment meta-analysis, mixed treatment comparison, ranking, network configuration

Chenglin Ye,1,2 Gina Browne,1,3 Joseph Beyene,1 Lehana Thabane1,2 1Department of Clinical Epidemiology and Biostatistics, McMaster University, 2Biostatistcs Unit, St Joseph's Healthcare Hamilton, 3School of Nursing, McMaster University, Hamilton, ON, Canada Background: The value of integrated care through comprehensive, coordinated, and family-centered services has been increasingly recognized for improving health outcomes of children with special health care needs (CSHCN). In a randomized controlled trial (RCT), the integrated care provided through the Children's Treatment Network (CTN) was compared with usual care in improving the psychosocial health of target CSHCN. In this paper, we aimed to estimate the effect of CTN care by conducting multiple analyses to handle noncompliance in the trial. Methods: The trial recruited target children in Simcoe County and York Region, ON, Canada. Children were randomized to receive CTN or usual care and were followed for 2 years. The CTN group received integrated services through multiple providers to address their specific needs while the usual care group continued to receive care directed by their parents. The outcome was change in psychosocial quality of life at 2 years. We conducted intention-to-treat, as-treated, per-protocol, and instrumental variable analyses to analyze the outcome. Results: The trial randomized 445 children, with 229 in the intervention group and 216 in the control group. During follow-up, 52% of children in the intervention group did not receive complete CTN care for various reasons. At 2 years, we did not find a significant improvement in psychosocial quality of life among the children receiving CTN care compared with usual care (intention-to-treat mean difference 1.50, 95% confidence interval −1.49 to 4.50; P = 0.32). Other methods of analysis yielded similar results. Conclusion: Although the effect of CTN care was not significant, there was evidence showing benefits of integrated care for CSHCN. More RCTs are needed to demonstrate the magnitude of such an effect. The CTN study highlights the key challenges in RCTs when assessing interventions involving integrated care, and informs further RCTs including similar evaluations. Keywords: children with special health care needs, chronically ill, family-centered care, randomized controlled trial, noncompliance

Objectives were to describe the reliability and validity of a new paediatric-specific mucositis scale, the Children's International Mucositis Evaluation Scale (ChIMES).In a multi-centre prospective study, children aged 0 to ≤18 years were eligible if they

Objective Malnutrition in children pervades all aspects of their health, growth, cognitive and social development and can lead to irreversible and lifelong effects. We examine the prevalence and determinants of malnutrition in children under 5 in the Ghanaian population. Methods Using data from the latest available Ghana Demographic and Health Survey (GDHS), we estimated and compared prevalence of malnutrition in children among the different subgroups of the population. We used multivariable logistic regression to identify potential factors associated with childhood malnutrition in Ghana. Results Overall, 35.6 % (95 % CI: 33.6, 37.6) of Ghanaian children under 5 years of age suffer from some form of malnutrition. Specifically, 27.5 % (95 % CI: 25.1, 28.7), 13.8 % (95 % CI: 12.5, 15.3), 8.9 % (95 % CI: 7.8, 10.2) were stunted, underweight and wasted, respectively. Results from the logistic regression indicate that gender and age of the child, educational and nutritional status of the mother, and financial status of the household are risk factors associated with childhood malnutrition in Ghana. Conclusions for Practice In view of the observed high rate of malnutrition among Ghanaian children despite the interventions that have been in place since the 1990s, there is a need for increased awareness and improved targeted interventions as well as knowledge translation tools including extensive education on infant and young child feeding practices., Author(s): J. E. Ewusie [sup.1] , J. Beyene [sup.1] [sup.2] , C. Ahiadeke [sup.3] [sup.4] , J. S. Hamid [sup.1] [sup.2] [sup.5] Author Affiliations: (1) grid.25073.33Department of Clinical Epidemiology and [...]

BACKGROUND/OBJECTIVES: In the absence of consistent clinical evidence, there are concerns that fructose contributes to non-alcoholic fatty liver disease (NAFLD). To determine the effect of fructose on markers of NAFLD, we conducted a systematic review and meta-analysis of controlled feeding trials. SUBJECTS/METHODS: We searched MEDLINE, EMBASE, CINAHL and the Cochrane Library (through 3 September 2013). We included relevant trials that involved a follow-up of ≥7 days. Two reviewers independently extracted relevant data. Data were pooled by the generic inverse variance method using random effects models and expressed as standardized mean difference (SMD) for intrahepatocellular lipids (IHCL) and mean difference (MD) for alanine aminotransferase (ALT). Inter-study heterogeneity was assessed (Cochran Q statistic) and quantified ([/.sup.2] statistic). RESULTS: Eligibility criteria were met by eight reports containing 13 trials in 260 healthy participants: seven isocaloric trials, in which fructose was exchanged isocalorically for other carbohydrates, and six hypercaloric trials, in which the diet was supplemented with excess energy (+ 21-35% energy) from high-dose fructose (+ 104-220 g/day). Although there was no effect of fructose in isocaloric trials, fructose in hypercaloric trials increased both IHCL (SMD = 0.45 (95% confidence interval (CI): 0.18, 0.72)) and ALT (MD = 4.94 U/l (95% CI: 0.03, 9.85)). LIMITATIONS: Few trials were available for inclusion, most of which were small, short (≤4 weeks), and of poor quality. CONCLUSIONS: Isocaloric exchange of fructose for other carbohydrates does not induce NAFLD changes in healthy participants. Fructose providing excess energy at extreme doses, however, does raise IHCL and ALT, an effect that may be more attributable to excess energy than fructose. Larger, longer and higher-quality trials of the effect of fructose on histopathological NAFLD changes are required. doi:10.1038/ejcn.2014.8; published online 26 February 2014 Keywords: sugars;non-alcoholic fatty liver disease;clinical trial;meta-analysis, INTRODUCTION Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease and a cause of raised liver enzymes in developed countries, (1) affecting 10-30% of people in developed [...]

Background: Recent progesterone trials call for an update of previous syntheses of interventions to prevent preterm birth.Objectives: To compare the relative effects of different types and routes of administration of progesterone, cerclage, and pessary at preventing preterm birth in at-risk women overall and in specific populations.Search Strategy: We searched Medline, EMBASE, CINAHL, Cochrane CENTRAL, and Web of Science up to 1 January 2018.Selection Criteria: We included randomised trials of progesterone, cerclage or pessary for preventing preterm birth in at-risk singleton pregnancies.Data Collection and Analysis: We used a piloted data extraction form and performed Bayesian random-effects network meta-analyses with 95% credibility intervals (CrI), as well as pairwise meta-analyses, rating the quality of the evidence using GRADE.Main Results: We included 40 trials (11 311 women). In at-risk women overall, vaginal progesterone reduced preterm birth <34 (OR 0.43, 95% CrI 0.20-0.81) and <37 weeks (OR 0.51, 95% CrI 0.34-0.74), and neonatal death (OR 0.41, 95% CrI 0.20-0.83). In women with a previous preterm birth, vaginal progesterone reduced preterm birth <34 (OR 0.29, 95% CI 0.12-0.68) and <37 weeks (OR 0.43, 95% CrI 0.23-0.74), and 17α-hydroxyprogesterone caproate reduced preterm birth <37 weeks (OR 0.53, 95% CrI 0.27-0.95) and neonatal death (OR 0.39, 95% CI 0.16-0.95). In women with a short cervix (≤25 mm), vaginal progesterone reduced preterm birth <34 weeks (OR 0.45, 95% CI 0.24-0.84).Conclusions: Vaginal progesterone was the only intervention with consistent effectiveness for preventing preterm birth in singleton at-risk pregnancies overall and in those with a previous preterm birth.Tweetable Abstract: In updated NMA, vaginal progesterone consistently reduced PTB in overall at-risk pregnancies and in women with previous PTB. [ABSTRACT FROM AUTHOR]