scholarly journals When does the use of individual patient data in network meta-analysis make a difference? A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Steve Kanters ◽  
Mohammad Ehsanul Karim ◽  
Kristian Thorlund ◽  
Aslam Anis ◽  
Nick Bansback
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Model Performance ◽  
Patient Data ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

scholarly journals Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e047186
Author(s):  
Roderick P Venekamp ◽  
Jeroen Hoogland ◽  
Maarten van Smeden ◽  
Maroeska M Rovers ◽  
An I De Sutter ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Cochrane Review ◽  
Inappropriate Prescribing ◽  
Model Performance ◽  
Patient Data ◽  
Reference List ◽  
Acute Rhinosinusitis ◽  
Absolute Benefit

IntroductionAcute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials.Methods and analysisThis is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8–15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal–external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk.Ethics and disseminationIn this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals.PROSPERO registration numberCRD42020220108.

scholarly journals Revisiting the use of remission criteria for rheumatoid arthritis by excluding patient global assessment: an individual meta-analysis of 5792 patients

2020 ◽  
pp. annrheumdis-2020-217171 ◽  
Author(s):  
Ricardo J O Ferreira ◽  
Paco M J Welsing ◽  
Johannes W G Jacobs ◽  
Laure Gossec ◽  
Mwidimi Ndosi ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Functional Outcome ◽  
Global Assessment ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Health Assessment ◽  
Patient Data ◽  
Patient Global Assessment ◽  
Remission Criteria ◽  
The Impact

ObjectivesTo determine the impact of excluding patient global assessment (PGA) from the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) Boolean remission criteria, on prediction of radiographic and functional outcome of rheumatoid arthritis (RA).MethodsMeta-analyses using individual patient data from randomised controlled trials testing the efficacy of biological agents on radiographic and functional outcomes at ≥2 years. Remission states were defined by 4 variants of the ACR/EULAR Boolean definition: (i) tender and swollen 28-joint counts (TJC28/SJC28), C reactive protein (CRP, mg/dL) and PGA (0–10=worst) all ≤1 (4V-remission); (ii) the same, except PGA >1 (4V-near-remission); (iii) 3V-remission (i and ii combined; similar to 4V, but without PGA); (iv) non-remission (TJC28 >1 and/or SJC28 >1 and/or CRP >1). The most stringent class achieved at 6 or 12 months was considered. Good radiographic (GRO) and functional outcome (GFO) were defined as no worsening (ie, change in modified total Sharp score (ΔmTSS) ≤0.5 units and ≤0.0 Health Assessment Questionnaire–Disability Index points, respectively, during the second year). The pooled probabilities of GRO and GFO for the different definitions of remission were estimated and compared.ResultsIndividual patient data (n=5792) from 11 trials were analysed. 4V-remission was achieved by 23% of patients and 4V-near-remission by 19%. The probability of GRO in the 4V-near-remission group was numerically, but non-significantly, lower than that in the 4V-remission (78 vs 81%) and significantly higher than that for non-remission (72%; difference=6%, 95% CI 2% to 10%). Applying 3V-remission could have prevented therapy escalation in 19% of all participants, at the cost of an additional 6.1%, 4.0% and 0.7% of patients having ΔmTSS >0.0, >0.5 and >5 units over 2 years, respectively. The probability of GFO (assessed in 8 trials) in 4V-near-remission (67%, 95% CI 63% to 71%) was significantly lower than in 4V-remission (78%, 74% to 81%) and similar to non-remission (69%, 66% to 72%).Conclusion4V-near-remission and 3V-remission have similar validity as the original 4V-remission definition in predicting GRO, despite expected worse prediction of GFO, while potentially reducing the risk of overtreatment. This supports further exploration of 3V-remission as the target for immunosuppressive therapy complemented by patient-oriented targets.

Melphalan and Prednisone (MP) Versus Melphalan, Prednisone and Thalidomide (MPT) as Initial Therapy for Previously Untreated Elderly and/or Transplant Ineligible Patients with Multiple Myeloma: A Meta-Analysis of Randomized Controlled Trials.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 615-615 ◽  
Author(s):  
Prashant Kapoor ◽  
S. Vincent Rajkumar ◽  
Angela Dispenzieri ◽  
Martha Q. Lacy ◽  
David Dingli ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Elderly Patients ◽  
Randomized Controlled Trials ◽  
Research Funding ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
The Impact

Abstract Abstract 615 Background: Trials comparing efficacy of standard melphalan prednisone (MP) therapy with MP plus thalidomide (T) in the transplant ineligible, elderly patients with multiple myeloma have provided conflicting evidence. While there is greater agreement with regard to superior response rates (RR) with the addition of T to MP in elderly patients, the impact on progression free survival (PFS) and overall survival (OS) is less clear with some trials showing an improvement in PFS and/or OS with MPT and others demonstrating no difference in outcomes. We performed a systematic review to integrate the existing outcome data related to the efficacy of MP vs. MPT using a meta-analytic approach. Methods: A comprehensive search of electronic database through July 31st, 2009 was performed for publications, abstracts and presentations to identify randomized controlled trials (RCTs) comparing MP with MPT. A meta-analysis was performed by pooling results on clinical endpoints of RR, PFS and OS reported in all the identified RCTs under a random effects model. We did not have access to individual patient data from these trials. Results: Overall, five prospective RCTs (3 published articles and 2 abstracts) comparing MP with MPT regimen and comprising a total of 1571 patients were identified. For the endpoints of OS and PFS, data were extractable only from 4 RCTs (abstract by Gulbrandsen et al. was excluded). The Bregg and Egger funnel plot for OS demonstrated a symmetric distribution (P = 0.6) indicating no significant publication bias. The test of heterogeneity among all RCTs was statistically significant in the estimate of RR (tau2=0.21; chi2=16.33; p=0.003 (df=4); I2 = 75.5%), but not significant for the estimates of PFS (tau2=0.01; chi2=4.61; p=0.2 (df=3); I2 = 34.9%), and OS (tau2=0.02; chi2=5.53; p=0.14 (df=3); I2 = 45.8%). As expected, the pooled odds ratio of responding to treatment with MP versus MPT was 0.307 (P<0.001) indicating that MP was worse than MPT in achieving at least a partial response. The pooled hazard ratios (HR) for PFS and OS were 1.59 (p<0.001) and 1.34 (p=0.006), respectively (see table for forest plots) in favor of MPT. Conclusion: Our meta-analysis implies that in previously untreated, transplant ineligible elderly patients with multiple myeloma, the addition of thalidomide to melphalan-prednisone demonstrates improved RR, PFS and OS compared with the use of melphalan-prednisone alone. Although the results from a comprehensive individual patient data pooled analysis would give a more precise estimate, our analysis suggests that MPT is superior to MP in terms of response and survival. Disclosures: Dispenzieri: Celgene: Research Funding. Gertz:Celgene: Honoraria. Kumar:celgene, genzyme, millennium, novartis, bayer: Research Funding; genzyme: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis

2020 ◽  
pp. 096228022094855
Author(s):  
Karla Hemming ◽  
James P Hughes ◽  
Joanne E McKenzie ◽  
Andrew B Forbes
Keyword(s):  
Randomized Trial ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Randomized Trials ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Cluster Randomized Trial ◽  
Patient Data ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.

Recombinant Human Erythropoiesis Stimulating Agents in Cancer Patients: Individual Patient Data Meta-Analysis on Behalf of the EPO IPD Meta-Analysis Collaborative Group

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4675-4675 ◽  
Author(s):  
Julia Bohlius ◽  
Corinne Brillant ◽  
Michael Clarke ◽  
Sabine Kluge ◽  
Maryann Napoli ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Red Blood Cell Transfusion ◽  
Collaborative Group ◽  
Study Phase ◽  
Erythropoiesis Stimulating Agents ◽  
Meta Analyses ◽  
The Impact

Abstract Background: Erythropoiesis stimulating agents (ESAs) consistently have been shown to decrease transfusions in anemic oncology patients. However, whether they increase mortality in cancer patients is under debate. Results from individual studies conflict, and results from literature based meta-analyses are inconclusive. We conducted a meta-analysis based on individual patient data (IPD) from all available randomized controlled trials (RCTs). Meta-analyses with individual patient data offer several advantages over study-level analysis, including the ability to gain statistical power and increase validity using time-to-event analyses, to adjust for prognostic variables that may have confounded the original treatment comparisons and to investigate subgroups in which treatment may be either more or less effective or harmful. Methods: An international collaboration conducted an individual patent data meta-analysis of ESA effects on mortality in cancer patients. With guidance from an independent steering committee of international experts in hematology, oncology, radiotherapy, epidemiology, medical statistics and a consumer representative, we developed a detailed protocol and statistical analysis plan. Independent RCT investigators and representatives from pharmaceutical companies who submitted data provided additional input through the project’s advisory board. IPD from RCTs of ESA plus red blood cell transfusion (RBCT) (as needed) versus placebo or no ESA plus RBCT (as needed), for prophylaxis or treatment of anemia in cancer patients with or without concurrent antineoplastic therapy, were included. Hazard ratios and 95% confidence intervals (CIs) were calculated per study and meta-analyzed with fixed-effects and random-effects models. Primary endpoints were overall survival (during active study phase and for the longest follow-up available) for patients receiving chemotherapy, and for all cancer patients regardless of anticancer treatment. Stratified multivariable Cox-regression analyses were conducted to assess the impact of baseline imbalances and to identify potential effect modifiers. Duplicate main statistical analyses were conducted independently at two academic statistical departments. Results: Data on 13933 patients enrolled in 53 studies were included in the analysis. Data were provided by the companies Amgen Inc., Johnson & Johnson Pharmaceutical Research & Development, L.L.C., and F. Hoffmann-La Roche Ltd.; and by five independent investigators. Results are currently undergoing internal verification and final evaluation and will be presented at the meeting. Conclusion: Final conclusions will be presented at the meeting. Future analyses using IPD will be conducted to estimate the risks (clots, tumor progression) and potential benefits (transfusion needs and quality of life/fatigue) from other outcomes.

scholarly journals Metformin in Pregnancy Study (MiPS): protocol for a systematic review with individual patient data meta-analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. e036981
Author(s):  
Aya Mousa ◽  
Tone Løvvik ◽  
Ijäs Hilkka ◽  
Sven M Carlsen ◽  
Laure Morin-Papunen ◽  
...  
Keyword(s):  
Individual Patient Data ◽  
Data Extraction ◽  
Meta Analysis ◽  
A Priori ◽  
Patient Data ◽  
Sensitivity Analyses ◽  
Efficacy And Safety ◽  
Adverse Pregnancy ◽  
The Impact ◽  
In Pregnancy

IntroductionGestational diabetes mellitus (GDM) is a common disorder of pregnancy and contributes to adverse pregnancy outcomes. Metformin is often used for the prevention and management of GDM; however, its use in pregnancy continues to be debated. The Metformin in Pregnancy Study aims to use individual patient data (IPD) meta-analysis to clarify the efficacy and safety of metformin use in pregnancy and to identify relevant knowledge gaps.Methods and analysisMEDLINE, EMBASE and all Evidence-Based Medicine will be systematically searched for randomised controlled trials (RCT) testing the efficacy of metformin compared with placebo, usual care or other interventions in pregnant women. Two independent reviewers will assess eligibility using prespecified criteria and will conduct data extraction and quality appraisal of eligible studies. Authors of included trials will be contacted and asked to contribute IPD. Primary outcomes include maternal glycaemic parameters and GDM, as well as neonatal hypoglycaemia, anthropometry and gestational age at delivery. Other adverse maternal, birth and neonatal outcomes will be assessed as secondary outcomes. IPD from these RCTs will be harmonised and a two-step meta-analytic approach will be used to determine the efficacy and safety of metformin in pregnancy, with a priori adjustment for covariates and subgroups to examine effect moderators of treatment outcomes. Sensitivity analyses will assess heterogeneity, risk of bias and the impact of trials which have not provided IPD.Ethics and disseminationAll IPD will be deidentified and studies contributing IPD will have ethical approval from their respective local ethics committees. This study will provide robust evidence regarding the efficacy and safety of metformin use in pregnancy, and may identify subgroups of patients who may benefit most from this treatment modality. Findings will be published in peer-reviewed journals and disseminated at scientific meetings, providing much needed evidence to inform clinical and public health actions in this area.

scholarly journals Effects of treatment classifications in network meta-analysis

2020 ◽  
Vol 1 (1) ◽  
pp. 12-24
Author(s):  
Aiwen Xing ◽  
Lifeng Lin
Keyword(s):  
Meta Analysis ◽  
Deviance Information Criterion ◽  
Model Performance ◽  
Information Criterion ◽  
Credible Interval ◽  
Odds Ratios ◽  
Credible Intervals ◽  
Treatment Comparisons ◽  
Meta Analyses ◽  
The Impact

Objectives Network meta-analysis is a popular tool to simultaneously compare multiple treatments and improve treatment effect estimates. However, no widely accepted guidelines are available to classify the treatment nodes in a network meta-analysis, and the node-making process was often insufficiently reported. We aim at empirically examining the impact of different treatment classifications on network meta-analysis results. Methods We collected nine published network meta-analyses with various disease outcomes; each contained some similar treatments that may be lumped. The Bayesian random-effects model was applied to these network meta-analyses before and after lumping the similar treatments. We estimated the odds ratios and their 95% credible intervals in the original and lumped network meta-analyses. We used the adjusted deviance information criterion to assess the model performance in the lumped network meta-analyses, and used the ratios of credible interval lengths and ratios of odds ratios to quantitatively evaluate the estimates’ changes due to lumping. In addition, the unrelated mean effect model was applied to examine the extents of evidence inconsistency. Results The estimated odds ratios of many treatment comparisons had noticeable changes due to lumping; many of their precisions were substantially improved. The deviance information criterion values reduced after lumping similar treatments in seven (78%) network meta-analyses, indicating better model performance. Substantial evidence inconsistency was detected in only one network meta-analysis. Conclusions Different ways of classifying treatment nodes may substantially affect network meta-analysis results. Including many insufficiently compared treatments and analysing them as separate nodes may not yield more precise estimates. Researchers should report the node-making process in detail and investigate the results’ robustness to different ways of classifying treatments.

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