Research Note: Individual participant data (IPD) meta-analysis

IntroductionMothers with gestational diabetes mellitus (GDM) are at increased risk of pregnancy-related complications and developing type 2 diabetes after delivery. Diet and physical activity-based interventions may prevent GDM, but variations in populations, interventions and outcomes in primary trials have limited the translation of available evidence into practice. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to assess the differential effects and cost-effectiveness of diet and physical activity-based interventions in preventing GDM and its complications.MethodsThe International Weight Management in Pregnancy Collaborative Network database is a living repository of IPD from randomised trials on diet and physical activity in pregnancy identified through a systematic literature search. We shall update our existing search on MEDLINE, Embase, BIOSIS, LILACS, Pascal, Science Citation Index, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects and Health Technology Assessment Database without language restriction to identify relevant trials until March 2021. Primary researchers will be invited to join the Network and share their IPD. Trials including women with GDM at baseline will be excluded. We shall perform a one and two stage random-effect meta-analysis for each intervention type (all interventions, diet-based, physical activity-based and mixed approach) to obtain summary intervention effects on GDM with 95% CIs and summary treatment–covariate interactions. Heterogeneity will be summarised using I2 and tau2 statistics with 95% prediction intervals. Publication and availability bias will be assessed by examining small study effects. Study quality of included trials will be assessed by the Cochrane Risk of Bias tool, and the Grading of Recommendations, Assessment, Development and Evaluations approach will be used to grade the evidence in the results. A model-based economic analysis will be carried out to assess the cost-effectiveness of interventions to prevent GDM and its complications compared with usual care.Ethics and disseminationEthics approval is not required. The study is registered on the International Prospective Register of Systematic Reviews (CRD42020212884). Results will be submitted for publication in peer-reviewed journals.

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A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts

BMC Medicine ◽

10.1186/s12916-020-01894-2 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Yuntao Chen ◽

Adriaan A. Voors ◽

Tiny Jaarsma ◽

Chim C. Lang ◽

Iziah E. Sama ◽

...

Keyword(s):

Heart Failure ◽

Clinical Decision Making ◽

Cox Model ◽

Meta Analysis ◽

Prognostic Index ◽

Individual Participant Data ◽

Mortality Risks ◽

Baseline Mortality ◽

Individual Participant ◽

Stratified Model

Abstract Background Prognostic models developed in general cohorts with a mixture of heart failure (HF) phenotypes, though more widely applicable, are also likely to yield larger prediction errors in settings where the HF phenotypes have substantially different baseline mortality rates or different predictor-outcome associations. This study sought to use individual participant data meta-analysis to develop an HF phenotype stratified model for predicting 1-year mortality in patients admitted with acute HF. Methods Four prospective European cohorts were used to develop an HF phenotype stratified model. Cox model with two rounds of backward elimination was used to derive the prognostic index. Weibull model was used to obtain the baseline hazard functions. The internal-external cross-validation (IECV) approach was used to evaluate the generalizability of the developed model in terms of discrimination and calibration. Results 3577 acute HF patients were included, of which 2368 were classified as having HF with reduced ejection fraction (EF) (HFrEF; EF < 40%), 588 as having HF with midrange EF (HFmrEF; EF 40–49%), and 621 as having HF with preserved EF (HFpEF; EF ≥ 50%). A total of 11 readily available variables built up the prognostic index. For four of these predictor variables, namely systolic blood pressure, serum creatinine, myocardial infarction, and diabetes, the effect differed across the three HF phenotypes. With a weighted IECV-adjusted AUC of 0.79 (0.74–0.83) for HFrEF, 0.74 (0.70–0.79) for HFmrEF, and 0.74 (0.71–0.77) for HFpEF, the model showed excellent discrimination. Moreover, there was a good agreement between the average observed and predicted 1-year mortality risks, especially after recalibration of the baseline mortality risks. Conclusions Our HF phenotype stratified model showed excellent generalizability across four European cohorts and may provide a useful tool in HF phenotype-specific clinical decision-making.

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Smoking and nasopharyngeal cancer: individual data meta-analysis of six prospective studies on 334 935 men

International Journal of Epidemiology ◽

10.1093/ije/dyab060 ◽

2021 ◽

Author(s):

Jia Huang Lin ◽

Chi Pang Wen ◽

Chao Qiang Jiang ◽

Jian-Min Yuan ◽

Chien Jen Chen ◽

...

Keyword(s):

Meta Analysis ◽

Nasopharyngeal Cancer ◽

Population Based ◽

Individual Participant Data ◽

Tobacco Control Policies ◽

Hazard Ratios ◽

Smoking Exposure ◽

Drinking Status ◽

Individual Participant ◽

Never Smokers

Abstract Background The role of smoking in nasopharyngeal carcinoma (NPC) remains uncertain, especially in endemic regions. We conducted an individual participant data (IPD) meta-analysis of prospective cohort studies to investigate the associations between smoking exposure and risk of NPC. Methods We obtained individual participant data of 334 935 male participants from six eligible population-based cohorts in NPC-endemic regions, including two each in Guangzhou and Taiwan, and one each in Hong Kong and Singapore. We used one- and two-stage approaches IPD meta-analysis and Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of NPC for smoking exposure adjusting for age and drinking status. Results During 2 961 315 person-years of follow-up, 399 NPC evens were ascertained. Risks of NPC were higher in ever versus never smokers (HRone-stage = 1.32, 95% CI = 1.07-1.63, P = 0.0088; HRtwo-stage = 1.27, 1.01-1.60, 0.04). These positive associations appeared to be stronger in ever smokers who consumed 16+ cigarettes/day (HRone-stage = 1.67, 95% CI = 1.29-2.16, P = 0.0001), and in those who started smoking at age younger than 16 (2.16, 1.33-3.50, 0.0103), with dose-response relationships (P-values for trend = 0.0028 and 0.0103, respectively). Quitting (versus daily smoking) showed a small reduced risk (stopped for 5+ years: HRone-stage = 0.91, 95% CI = 0.60-1.39, P = 0.66; for former smokers: HRtwo-stage = 0.84, 0.61-1.14, 0.26). Conclusions This first IPD meta-analysis from six prospective cohorts in endemic regions has provided robust observational evidence that smoking increased NPC risk in men. NPC should be added to the 12–16 cancer sites known to be tobacco-related cancers. Strong tobacco control policies, preventing young individuals from smoking, would reduce NPC risk in endemic regions.

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