Quantifying the advantages of conducting a prospective meta-analysis (PMA): a case study of early childhood obesity prevention

Abstract Background For prospective meta-analyses (PMAs), eligible studies are identified, and the PMA hypotheses, selection criteria, and analysis methods are pre-specified before the results of any of the studies are known. This reduces publication bias and selective outcome reporting and provides a unique opportunity for outcome standardisation/harmonisation. We conducted a world-first PMA of four trials investigating interventions to prevent early childhood obesity. The aims of this study were to quantitatively analyse the effects of prospective planning on variations across trials, outcome harmonisation, and the power to detect intervention effects, and to derive recommendations for future PMA. Methods We examined intervention design, participant characteristics, and outcomes collected across the four trials included in the EPOCH PMA using their registration records, protocol publications, and variable lists. The outcomes that trials planned to collect prior to inclusion in the PMA were compared to the outcomes that trials collected after PMA inclusion. We analysed the proportion of matching outcome definitions across trials, the number of outcomes per trial, and how collaboration increased the statistical power to detect intervention effects. Results The included trials varied in intervention design and participants, this improved external validity and the ability to perform subgroup analyses for the meta-analysis. While individual trials had limited power to detect the main intervention effect (BMI z-score), synthesising data substantially increased statistical power. Prospective planning led to an increase in the number of collected outcome categories (e.g. weight, child’s diet, sleep), and greater outcome harmonisation. Prior to PMA inclusion, only 18% of outcome categories were included in all trials. After PMA inclusion, this increased to 91% of outcome categories. However, while trials mostly collected the same outcome categories after PMA inclusion, some inconsistencies in how the outcomes were measured remained (such as measuring physical activity by hours of outside play versus using an activity monitor). Conclusion Prospective planning led to greater outcome harmonisation and greater power to detect intervention effects, while maintaining acceptable variation in trial designs and populations, which improved external validity. Recommendations for future PMA include more detailed harmonisation of outcome measures and careful pre-specification of analyses to avoid research waste by unnecessary over-collection of data.

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Quantifying the advantages of conducting a prospective meta-analysis (PMA): a case study of early childhood obesity prevention

10.21203/rs.3.rs-36734/v1 ◽

2020 ◽

Author(s):

Anna Lene Seidler ◽

Kylie E Hunter ◽

David Espinoza ◽

Seema Mihrshahi ◽

Lisa M Askie

Keyword(s):

Early Childhood ◽

Childhood Obesity ◽

External Validity ◽

Statistical Power ◽

Meta Analysis ◽

Intervention Design ◽

Intervention Effects ◽

Early Childhood Obesity ◽

Limited Power ◽

Meta Analyses

Abstract Background: For prospective meta-analyses (PMA), eligible studies are identified and the PMA hypotheses, selection criteria and analysis methods are pre-specified before results of any of the studies are known. This reduces publication bias and selective outcome reporting and provides a unique opportunity for outcome standardisation/harmonisation. We conducted a world-first PMA of four trials investigating interventions to prevent early childhood obesity. The aims of this study were to quantitatively analyse the effects of prospective planning on variations across trials, outcome harmonisation, and the power to detect intervention effects, and to derive recommendations for future PMA.Methods: We examined intervention design, participant characteristics and outcomes collected across the four trials included in the EPOCH PMA using their registration records, protocol publications and variable lists. The outcomes trials that planned to collect prior to inclusion in the PMA were compared to the outcomes trials collected after PMA inclusion. We analysed the proportion of matching outcome definitions across trials, the number of outcomes per trial, and how collaboration increased the statistical power to detect intervention effects.Results: The included trials varied in intervention design and participants, which improved external validity and the ability to perform subgroup analyses for the meta-analysis. While individual trials had limited power to detect the main intervention effect (BMI z-score), synthesising data substantially increased statistical power. Prospective planning led to an increase in number of collected outcome categories (e.g. weight, child’s diet, sleep), and greater outcome harmonisation. Prior to PMA inclusion, only 18% of outcome categories were included in all trials. After PMA inclusion this increased to 91% of outcome categories. However, whilst trials mostly collected the same outcome categories after PMA inclusion, some inconsistencies in how the outcomes were measured remained (such as measuring physical activity by hours of outside play versus using an activity monitor).Conclusion: Prospective planning led to greater outcome harmonisation and greater power to detect intervention effects, while maintaining acceptable variation in trial designs and populations, which improved external validity. Recommendations for future PMA include more detailed harmonisation of outcome measures, and careful pre-specification of analyses to avoid research waste by unnecessary over-collection of data.

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Quantifying the advantages of conducting a prospective meta-analysis (PMA): a case study of early childhood obesity prevention

10.21203/rs.3.rs-36734/v2 ◽

2020 ◽

Author(s):

Anna Lene Seidler ◽

Kylie E Hunter ◽

David Espinoza ◽

Seema Mihrshahi ◽

Lisa M Askie

Keyword(s):

Early Childhood ◽

Childhood Obesity ◽

External Validity ◽

Statistical Power ◽

Meta Analysis ◽

Intervention Design ◽

Intervention Effects ◽

Early Childhood Obesity ◽

Limited Power ◽

Meta Analyses

Abstract Background: For prospective meta-analyses (PMA), eligible studies are identified and the PMA hypotheses, selection criteria and analysis methods are pre-specified before results of any of the studies are known. This reduces publication bias and selective outcome reporting and provides a unique opportunity for outcome standardisation/harmonisation. We conducted a world-first PMA of four trials investigating interventions to prevent early childhood obesity. The aims of this study were to quantitatively analyse the effects of prospective planning on variations across trials, outcome harmonisation and the power to detect intervention effects, and to derive recommendations for future PMA.Methods: We examined intervention design, participant characteristics and outcomes collected across the four trials included in the EPOCH PMA using their registration records, protocol publications and variable lists. The outcomes trials that planned to collect prior to inclusion in the PMA were compared to the outcomes trials collected after PMA inclusion. We analysed the proportion of matching outcome definitions across trials, the number of outcomes per trial, and how collaboration increased the statistical power to detect intervention effects.Results: The included trials varied in intervention design and participants, which improved external validity and the ability to perform subgroup analyses for the meta-analysis. While individual trials had limited power to detect the main intervention effect (BMI z-score), synthesising data substantially increased statistical power. Prospective planning led to an increase in number of collected outcome categories (e.g. weight, child’s diet, sleep), and greater outcome harmonisation. Prior to PMA inclusion, only 18% of outcome categories were included in all trials. After PMA inclusion this increased to 91% of outcome categories. However, whilst trials mostly collected the same outcome categories after PMA inclusion, some inconsistencies in how the outcomes were measured remained (such as measuring physical activity by hours of outside play versus using an activity monitor).Conclusion: Prospective planning led to greater outcome harmonisation and greater power to detect intervention effects, while maintaining acceptable variation in trial designs and populations, which improved external validity. Recommendations for future PMA include more detailed harmonisation of outcome measures, and careful pre-specification of analyses to avoid research waste by unnecessary over-collection of data.

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Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration: protocol for a systematic review with individual participant data meta-analysis of behavioural interventions for the prevention of early childhood obesity

10.1101/2020.12.17.20248441 ◽

2020 ◽

Author(s):

Kylie E Hunter ◽

Brittany J Johnson ◽

Lisa Askie ◽

Rebecca K Golley ◽

Louise A Baur ◽

...

Keyword(s):

Early Childhood ◽

Childhood Obesity ◽

Obesity Prevention ◽

Meta Analysis ◽

Individual Participant Data ◽

Behavioural Interventions ◽

Analysis Methodology ◽

Individual Participant ◽

Early Childhood Obesity ◽

Overall Effectiveness

ABSTRACTIntroductionBehavioural interventions in early life appear to show some effect in reducing childhood overweight and obesity. However, uncertainty remains regarding their overall effectiveness, and whether effectiveness differs among key subgroups. These evidence gaps have prompted an increase in very early childhood obesity prevention trials worldwide. Combining the individual participant data (IPD) from these trials will enhance statistical power to determine overall effectiveness and enable examination of intervention-covariate interactions. We present a protocol for a systematic review with IPD meta-analysis to evaluate the effectiveness of obesity prevention interventions commencing antenatally or in the first year after birth, and to explore whether there are differential effects among key subgroups.Methods and analysisSystematic searches of Medline, Embase, CENTRAL, CINAHL, PsycInfo, and trial registries for all ongoing and completed randomised controlled trials evaluating behavioural interventions for the prevention of early childhood obesity have been completed up to March 2020 and will be updated annually to include additional trials. Eligible trialists will be asked to share their IPD; if unavailable, aggregate data will be used where possible. An IPD meta-analysis and a nested prospective meta-analysis (PMA) will be performed using methodologies recommended by the Cochrane Collaboration. The primary outcome will be body mass index (BMI) z-score at age 24 +/- 6 months using World Health Organisation Growth Standards, and effect differences will be explored among pre-specified individual and trial-level subgroups. Secondary outcomes include other child weight-related measures, infant feeding, dietary intake, physical activity, sedentary behaviours, sleep, parenting measures and adverse events.Ethics and disseminationApproved by The University of Sydney Human Research Ethics Committee (2020/273) and Flinders University Social and Behavioural Research Ethics Committee (project no. HREC CIA2133-1). Results will be relevant to clinicians, child health services, researchers, policy-makers and families, and will be disseminated via publications, presentations, and media releases.RegistrationProspectively registered on PROSPERO: CRD42020177408STRENGTHS AND LIMITATIONS OF THIS STUDYThis will be the largest individual participant data (IPD) meta-analysis evaluating behavioural interventions for the prevention of early childhood obesity to date, and will provide the most reliable and precise estimates of early intervention effects to inform future decision-making.IPD meta-analysis methodology will enable unprecedented exploration of important individual and trial-level characteristics that may be associated with childhood obesity or that may be effect modifiers.The proposed innovative methodologies are feasible and have been successfully piloted by members of our group.It may not be possible to obtain IPD from all eligible trials; in this instance, aggregate data will be used where available, and sensitivity analyses will be conducted to assess inclusion bias.Outcome measures may be collected and reported differently across included trials, potentially increasing imprecision; however, we will harmonise available data where possible, and encourage those planning or conducting ongoing trials to collect common core outcomes following prospective meta-analysis methodology.

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A Meta-Analysis of the Association Between Breastfeeding and Early Childhood Obesity

Journal of Pediatric Nursing ◽

10.1016/j.pedn.2020.04.024 ◽

2020 ◽

Vol 53 ◽

pp. 57-66 ◽

Cited By ~ 1

Author(s):

Jia Qiao ◽

Li-Jing Dai ◽

Qing Zhang ◽

Yan-Qiong Ouyang

Keyword(s):

Early Childhood ◽

Childhood Obesity ◽

Meta Analysis ◽

Early Childhood Obesity

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Power-Enhanced Funnel Plots for Meta-Analysis

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000392 ◽

2020 ◽

Vol 228 (1) ◽

pp. 43-49 ◽

Cited By ~ 1

Author(s):

Michael Kossmeier ◽

Ulrich S. Tran ◽

Martin Voracek

Keyword(s):

Statistical Power ◽

Meta Analysis ◽

Relevant Information ◽

Selective Reporting ◽

Graphical Display ◽

Funnel Plot ◽

Evidential Value ◽

Graphical Displays ◽

Funnel Plots ◽

Meta Analyses

Abstract. Currently, dedicated graphical displays to depict study-level statistical power in the context of meta-analysis are unavailable. Here, we introduce the sunset (power-enhanced) funnel plot to visualize this relevant information for assessing the credibility, or evidential value, of a set of studies. The sunset funnel plot highlights the statistical power of primary studies to detect an underlying true effect of interest in the well-known funnel display with color-coded power regions and a second power axis. This graphical display allows meta-analysts to incorporate power considerations into classic funnel plot assessments of small-study effects. Nominally significant, but low-powered, studies might be seen as less credible and as more likely being affected by selective reporting. We exemplify the application of the sunset funnel plot with two published meta-analyses from medicine and psychology. Software to create this variation of the funnel plot is provided via a tailored R function. In conclusion, the sunset (power-enhanced) funnel plot is a novel and useful graphical display to critically examine and to present study-level power in the context of meta-analysis.

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