IMPORTANCE Systematic reviews and meta-analyses of individual participant data (IPD) aim to collect, check, and reanalyze individual-level data from all studies addressing a particular research question and are therefore considered a gold standard approach to evidence synthesis. They are likely to be used with increasing frequency as current initiatives to share clinical trial data gain momentum and may be particularly important in reviewing controversial therapeutic areas. OBJECTIVE To develop PRISMA-IPD as a stand-alone extension to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, tailored to the specific requirements of reporting systematic reviews and meta-analyses of IPD. Although developed primarily for reviews of randomized trials, many items will apply in other contexts, including reviews of diagnosis and prognosis. DESIGN Development of PRISMA-IPD followed the EQUATOR Network framework guidance and used the existing standard PRISMA Statement as a starting point to draft additional relevant material. A web-based survey informed discussion at an international workshop that included researchers, clinicians, methodologists experienced in conducting systematic reviews and meta-analyses of IPD, and journal editors. The statement was drafted and iterative refinements were made by the project, advisory, and development groups. The PRISMA-IPD Development Group reached agreement on the PRISMA-IPD checklist and flow diagram by consensus. FINDINGS Compared with standard PRISMA, the PRISMA-IPD checklist includes 3 new items that address (1) methods of checking the integrity of the IPD (such as pattern of randomization, data consistency, baseline imbalance, and missing data), (2) reporting any important issues that emerge, and (3) exploring variation (such as whether certain types of individual benefit more from the intervention than others). A further additional item was created by reorganization of standard PRISMA items relating to interpreting results. Wording was modified in 23 items to reflect the IPD approach. CONCLUSIONS AND RELEVANCE PRISMA-IPD provides guidelines for reporting systematic reviews and meta-analyses of IPD.

A systematic review and meta-analysis was performed to investigate the ability of simple measures of childhood obesity such as body mass index (BMI) to predict future obesity in adolescence and adulthood. Large cohort studies, which measured obesity both in childhood and in later adolescence or adulthood, using any recognized measure of obesity were sought. Study quality was assessed. Studies were pooled using diagnostic meta-analysis methods. Fifteen prospective cohort studies were included in the meta-analysis. BMI was the only measure of obesity reported in any study, with 200,777 participants followed up. Obese children and adolescents were around five times more likely to be obese in adulthood than those who were not obese. Around 55% of obese children go on to be obese in adolescence, around 80% of obese adolescents will still be obese in adulthood and around 70% will be obese over age 30. Therefore, action to reduce and prevent obesity in these adolescents is needed. However, 70% of obese adults were not obese in childhood or adolescence, so targeting obesity reduction solely at obese or overweight children needs to be considered carefully as this may not substantially reduce the overall burden of adult obesity.

Studies combined in a meta-analysis often have differences in their design and conduct that can lead to heterogeneous results. A random-effects model accounts for these differences in the underlying study effects, which includes a heterogeneity variance parameter. The DerSimonian-Laird method is often used to estimate the heterogeneity variance, but simulation studies have found the method can be biased and other methods are available. This paper compares the properties of nine different heterogeneity variance estimators using simulated meta-analysis data. Simulated scenarios include studies of equal size and of moderate and large differences in size. Results confirm that the DerSimonian-Laird estimator is negatively biased in scenarios with small studies and in scenarios with a rare binary outcome. Results also show the Paule-Mandel method has considerable positive bias in meta-analyses with large differences in study size. We recommend the method of restricted maximum likelihood (REML) to estimate the heterogeneity variance over other methods. However, considering that meta-analyses of health studies typically contain few studies, the heterogeneity variance estimate should not be used as a reliable gauge for the extent of heterogeneity in a meta-analysis. The estimated summary effect of the meta-analysis and its confidence interval derived from the Hartung-Knapp-Sidik-Jonkman method are more robust to changes in the heterogeneity variance estimate and show minimal deviation from the nominal coverage of 95% under most of our simulated scenarios.

Obese children are at higher risk of being obese as adults, and adult obesity is associated with an increased risk of morbidity. This systematic review and meta-analysis investigates the ability of childhood body mass index (BMI) to predict obesity-related morbidities in adulthood. Thirty-seven studies were included. High childhood BMI was associated with an increased incidence of adult diabetes (OR 1.70; 95% CI 1.30-2.22), coronary heart disease (CHD) (OR 1.20; 95% CI 1.10-1.31) and a range of cancers, but not stroke or breast cancer. The accuracy of childhood BMI when predicting any adult morbidity was low. Only 31% of future diabetes and 22% of future hypertension and CHD occurred in children aged 12 or over classified as being overweight or obese. Only 20% of all adult cancers occurred in children classified as being overweight or obese. Childhood obesity is associated with moderately increased risks of adult obesity-related morbidity, but the increase in risk is not large enough for childhood BMI to be a good predictor of the incidence of adult morbidities. This is because the majority of adult obesity-related morbidity occurs in adults who were of healthy weight in childhood. Therefore, targeting obesity reduction solely at obese or overweight children may not substantially reduce the overall burden of obesity-related disease in adulthood.

Although IPD meta-analyses have many advantages in assessing the effects of health care, there are several aspects that could be further developed to make fuller use of the potential of these time-consuming projects. In particular, IPD could be used to more fully investigate the influence of covariates on heterogeneity of treatment effects, both within and between trials. The impact of heterogeneity, or use of random effects, are seldom discussed. There is thus considerable scope for enhancing the methods of analysis and presentation of IPD meta-analysis.

Systematic reviews are difficult to keep up to date, but failure to do so leads to a decay in review currency, accuracy, and utility. We are developing a novel approach to systematic review updating termed "Living systematic review" (LSR): systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs may be particularly important in fields where research evidence is emerging rapidly, current evidence is uncertain, and new research may change policy or practice decisions. We hypothesize that a continual approach to updating will achieve greater currency and validity, and increase the benefits to end users, with feasible resource requirements over time.

BackgroundIt is uncertain which simple measures of childhood obesity are best for predicting future obesity-related health problems and the persistence of obesity into adolescence and adulthood.ObjectivesTo investigate the ability of simple measures, such as body mass index (BMI), to predict the persistence of obesity from childhood into adulthood and to predict obesity-related adult morbidities. To investigate how accurately simple measures diagnose obesity in children, and how acceptable these measures are to children, carers and health professionals.Data sourcesMultiple sources including MEDLINE, EMBASE and The Cochrane Library were searched from 2008 to 2013.MethodsSystematic reviews and a meta-analysis were carried out of large cohort studies on the association between childhood obesity and adult obesity; the association between childhood obesity and obesity-related morbidities in adulthood; and the diagnostic accuracy of simple childhood obesity measures. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and a modified version of the Quality in Prognosis Studies (QUIPS) tool. A systematic review and an elicitation exercise were conducted on the acceptability of the simple measures.ResultsThirty-seven studies (22 cohorts) were included in the review of prediction of adult morbidities. Twenty-three studies (16 cohorts) were included in the tracking review. All studies included BMI. There were very few studies of other measures. There was a strong positive association between high childhood BMI and adult obesity [odds ratio 5.21, 95% confidence interval (CI) 4.50 to 6.02]. A positive association was found between high childhood BMI and adult coronary heart disease, diabetes and a range of cancers, but not stroke or breast cancer. The predictive accuracy of childhood BMI to predict any adult morbidity was very low, with most morbidities occurring in adults who were of healthy weight in childhood. Predictive accuracy of childhood obesity was moderate for predicting adult obesity, with a sensitivity of 30% and a specificity of 98%. Persistence of obesity from adolescence to adulthood was high. Thirty-four studies were included in the diagnostic accuracy review. Most of the studies used the least reliable reference standard (dual-energy X-ray absorptiometry); only 24% of studies were of high quality. The sensitivity of BMI for diagnosing obesity and overweight varied considerably; specificity was less variable. Pooled sensitivity of BMI was 74% (95% CI 64.2% to 81.8%) and pooled specificity was 95% (95% CI 92.2% to 96.4%). The acceptability to children and their carers of BMI or other common simple measures was generally good.LimitationsLittle evidence was available regarding childhood measures other than BMI. No individual-level analysis could be performed.ConclusionsChildhood BMI is not a good predictor of adult obesity or adult disease; the majority of obese adults were not obese as children and most obesity-related adult morbidity occurs in adults who had a healthy childhood weight. However, obesity (as measured using BMI) was found to persist from childhood to adulthood, with most obese adolescents also being obese in adulthood. BMI was found to be reasonably good for diagnosing obesity during childhood. There is no convincing evidence suggesting that any simple measure is better than BMI for diagnosing obesity in childhood or predicting adult obesity and morbidity. Further research on obesity measures other than BMI is needed to determine which is the best tool for diagnosing childhood obesity, and new cohort studies are needed to investigate the impact of contemporary childhood obesity on adult obesity and obesity-related morbidities.Study registrationThis study is registered as PROSPERO CRD42013005711.FundingThe National Institute for Health Research Health Technology Assessment programme.

Meta-analyses of clinical trials are increasingly seeking to go beyond estimating the effect of a treatment and may also aim to investigate the effect of other covariates and how they alter treatment effectiveness. This requires the estimation of treatment-covariate interactions. Meta-regression can be used to estimate such interactions using published data, but it is known to lack statistical power, and is prone to bias.The use of individual patient data can improve estimation of such interactions, among other benefits, but it can be difficult and time-consuming to collect and analyse. This paper derives, under certain conditions, the power of meta-regression and IPD methods to detect treatment-covariate interactions. These power formulae are shown to depend on heterogeneity in the covariate distributions across studies. This allows the derivation of simple tests, based on heterogeneity statistics, for comparing the statistical power of the analysis methods.