Antiepileptic drug monotherapy for epilepsy: a network meta-analysis of individual participant data.
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ABSTRACT:
SUBMITTER: Nevitt SJ
PROVIDER: S-EPMC6486134 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC6481892 | biostudies-literature
Project description:Meta-analytic methods for combining data from multiple intervention trials are commonly used to estimate the effectiveness of an intervention. They can also be extended to study comparative effectiveness, testing which of several alternative interventions is expected to have the strongest effect. This often requires network meta-analysis (NMA), which combines trials involving direct comparison of two interventions within the same trial and indirect comparisons across trials. In this paper, we extend existing network methods for main effects to examining moderator effects, allowing for tests of whether intervention effects vary for different populations or when employed in different contexts. In addition, we study how the use of individual participant data may increase the sensitivity of NMA for detecting moderator effects, as compared with aggregate data NMA that employs study-level effect sizes in a meta-regression framework. A new NMA diagram is proposed. We also develop a generalized multilevel model for NMA that takes into account within-trial and between-trial heterogeneity and can include participant-level covariates. Within this framework, we present definitions of homogeneity and consistency across trials. A simulation study based on this model is used to assess effects on power to detect both main and moderator effects. Results show that power to detect moderation is substantially greater when applied to individual participant data as compared with study-level effects. We illustrate the use of this method by applying it to data from a classroom-based randomized study that involved two sub-trials, each comparing interventions that were contrasted with separate control groups. Copyright © 2016 John Wiley & Sons, Ltd.
| S-EPMC4893003 | biostudies-literature
Project description:When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.
| S-EPMC4847645 | biostudies-literature
Project description:BackgroundStratified medicine seeks to identify patients most likely to respond to treatment. Individual participant data (IPD) network meta-analysis (NMA) models have greater power than individual trials to identify treatment-covariate interactions (TCIs). Treatment-covariate interactions contain "within" and "across" trial interactions, where the across-trial interaction is more susceptible to confounding and ecological bias.MethodsWe considered a network of IPD from 37 trials (5922 patients) for cervical cancer (2394 events), where previous research identified disease stage as a potential interaction covariate. We compare 2 models for NMA with TCIs: (1) 2 effects separating within- and across-trial interactions and (2) a single effect combining within- and across-trial interactions. We argue for a visual assessment of consistency of within- and across-trial interactions and consider more detailed aspects of interaction modelling, eg, common vs trial-specific effects of the covariate. This leads us to propose a practical framework for IPD NMA with TCIs.ResultsFollowing our framework, we found no evidence in the cervical cancer network for a treatment-stage interaction on the basis of the within-trial interaction. The NMA provided additional power for an across-trial interaction over and above the pairwise evidence. Following our proposed framework, we found that the within- and across-trial interactions should not be combined.ConclusionAcross-trial interactions are susceptible to confounding and ecological bias. It is important to separate the sources of evidence to check their consistency and identify which sources of evidence are driving the conclusion. Our framework provides practical guidance for researchers, reducing the risk of unduly optimistic interpretation of TCIs.
| S-EPMC6159880 | biostudies-literature
Project description:Objective:To perform a systematic review and meta-analysis of real-world evidence for the use of low-frequency repetitive transcranial magnetic stimulation (rTMS) in the treatment of drug-resistant epilepsy. Methods:We systematically searched PubMed, Scopus, Medline, and clinicaltrials.gov for all relevant articles. Relevant patient and stimulation predictors as well as seizure outcomes were assessed. For studies with and without individual participant data (IPD), the primary outcomes were the rate of "favorable response" (reduction in seizure frequency ?50%) and pooled event rate of mean reduction in seizure frequency, respectively. Outcomes were assessed with comparative statistics and random-effects meta-analysis models. Results:Of 3,477 identified articles, 12 met eligibility and were included in this review. We were able to obtain IPD for 5 articles constituting 34 participants. Univariate analysis on IPD identified greater favorable response event rates between participants with temporal seizure focus versus extratemporal (50% vs. 14%, p = 0.045) and between participants who were stimulated with a figure-8 coil versus other types (47% vs. 0%, p = 0.01). We also performed study-level meta-analysis on the remaining 7 studies without IPD, which included 212 participants. The pooled mean event rate of 50% seizure reduction using low-frequency rTMS was 30% (95% confidence interval [CI] 12-57%). Sensitivity analysis revealed that studies with a mean age ?21 years and studies using targeted stimulation had the highest seizure reduction rates compared to studies with a mean age >21 years (69% vs. 18%) and not using a targeted stimulation (47% vs. 14-20%). Moreover, we identified high interstudy heterogeneity, moderate study bias, and high publication bias. Significance:Real-world evidence suggests that low-frequency rTMS using a figure-8 coil may be an effective therapy for the treatment of drug-resistant epilepsy in pediatric patients. This meta-analysis can inform the design and expedite recruitment of a subsequent randomized clinical trial.
| S-EPMC5839309 | biostudies-literature
Project description:Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse-variance weighted meta-analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between-study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse-variance weighted meta-analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta-analysis, rather than meta-analyzing each of the multiple imputations and then combining the meta-analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration.
| S-EPMC3963448 | biostudies-literature
Project description:BackgroundTherapeutic efficacy studies in uncomplicated Plasmodium falciparum malaria are confounded by new infections, which constitute competing risk events since they can potentially preclude/pre-empt the detection of subsequent recrudescence of persistent, sub-microscopic primary infections.MethodsAntimalarial studies typically report the risk of recrudescence derived using the Kaplan-Meier (K-M) method, which considers new infections acquired during the follow-up period as censored. Cumulative Incidence Function (CIF) provides an alternative approach for handling new infections, which accounts for them as a competing risk event. The complement of the estimate derived using the K-M method (1 minus K-M), and the CIF were used to derive the risk of recrudescence at the end of the follow-up period using data from studies collated in the WorldWide Antimalarial Resistance Network data repository. Absolute differences in the failure estimates derived using these two methods were quantified. In comparative studies, the equality of two K-M curves was assessed using the log-rank test, and the equality of CIFs using Gray's k-sample test (both at 5% level of significance). Two different regression modelling strategies for recrudescence were considered: cause-specific Cox model and Fine and Gray's sub-distributional hazard model.ResultsData were available from 92 studies (233 treatment arms, 31,379 patients) conducted between 1996 and 2014. At the end of follow-up, the median absolute overestimation in the estimated risk of cumulative recrudescence by using 1 minus K-M approach was 0.04% (interquartile range (IQR): 0.00-0.27%, Range: 0.00-3.60%). The overestimation was correlated positively with the proportion of patients with recrudescence [Pearson's correlation coefficient (?): 0.38, 95% Confidence Interval (CI) 0.30-0.46] or new infection [?: 0.43; 95% CI 0.35-0.54]. In three study arms, the point estimates of failure were greater than 10% (the WHO threshold for withdrawing antimalarials) when the K-M method was used, but remained below 10% when using the CIF approach, but the 95% confidence interval included this threshold.ConclusionsThe 1 minus K-M method resulted in a marginal overestimation of recrudescence that became increasingly pronounced as antimalarial efficacy declined, particularly when the observed proportion of new infection was high. The CIF approach provides an alternative approach for derivation of failure estimates in antimalarial trials, particularly in high transmission settings.
| S-EPMC6612160 | biostudies-literature
Project description:ObjectiveTo perform a systematic review and individual participant data meta-analysis to identify preoperative factors associated with a good seizure outcome in children with Tuberous Sclerosis Complex undergoing resective epilepsy surgery.Data sourcesElectronic databases (MEDLINE, EMBASE, CINAHL and Web of Science), archives of major epilepsy and neurosurgery meetings, and bibliographies of relevant articles, with no language or date restrictions.Study selectionWe included case-control or cohort studies of consecutive participants undergoing resective epilepsy surgery that reported seizure outcomes. We performed title and abstract and full text screening independently and in duplicate. We resolved disagreements through discussion.Data extractionOne author performed data extraction which was verified by a second author using predefined data fields including study quality assessment using a risk of bias instrument we developed. We recorded all preoperative factors that may plausibly predict seizure outcomes.Data synthesisTo identify predictors of a good seizure outcome (i.e. Engel Class I or II) we used logistic regression adjusting for length of follow-up for each preoperative variable.ResultsOf 9863 citations, 20 articles reporting on 181 participants were eligible. Good seizure outcomes were observed in 126 (69%) participants (Engel Class I: 102(56%); Engel class II: 24(13%)). In univariable analyses, absence of generalized seizure semiology (OR = 3.1, 95%CI = 1.2-8.2, p = 0.022), no or mild developmental delay (OR = 7.3, 95%CI = 2.1-24.7, p = 0.001), unifocal ictal scalp electroencephalographic (EEG) abnormality (OR = 3.2, 95%CI = 1.4-7.6, p = 0.008) and EEG/Magnetic resonance imaging concordance (OR = 4.9, 95%CI = 1.8-13.5, p = 0.002) were associated with a good postoperative seizure outcome.ConclusionsSmall retrospective cohort studies are inherently prone to bias, some of which are overcome using individual participant data. The best available evidence suggests four preoperative factors predictive of good seizure outcomes following resective epilepsy surgery. Large long-term prospective multicenter observational studies are required to further evaluate the risk factors identified in this review.
| S-EPMC3566144 | biostudies-literature
Project description:PurposeThe optimal neoadjuvant treatment for resectable carcinoma of the thoracic esophagus (TE) or gastroesophageal junction (GEJ) remains a matter of debate. We performed an individual participant data (IPD) network meta-analysis (NMA) of randomized controlled trials (RCTs) to study the effect of chemotherapy or chemoradiotherapy, with a focus on tumor location and histology subgroups.Patients and methodsAll, published or unpublished, RCTs closed to accrual before December 31, 2015 and having compared at least two of the following strategies were eligible: upfront surgery (S), chemotherapy followed by surgery (CS), and chemoradiotherapy followed by surgery (CRS). All analyses were conducted on IPD obtained from investigators. The primary end point was overall survival (OS). The IPD-NMA was analyzed by a one-step mixed-effect Cox model adjusted for age, sex, tumor location, and histology. The NMA was registered in PROSPERO (CRD42018107158).ResultsIPD were obtained for 26 of 35 RCTs (4,985 of 5,807 patients) corresponding to 12 comparisons for CS-S, 12 for CRS-S, and four for CRS-CS. CS and CRS led to increased OS when compared with S with hazard ratio (HR) = 0.86 (0.75 to 0.99), P = .03 and HR = 0.77 (0.68 to 0.87), P < .001 respectively. The NMA comparison of CRS versus CS for OS gave a HR of 0.90 (0.74 to 1.09), P = .27 (consistency P = .26, heterogeneity P = .0038). For CS versus S, a larger effect on OS was observed for GEJ versus TE tumors (P = .036). For the CRS versus S and CRS versus CS, a larger effect on OS was observed for women (P = .003, .012, respectively).ConclusionNeoadjuvant chemotherapy and chemoradiotherapy were consistently better than S alone across histology, but with some variation in the magnitude of treatment effect by sex for CRS and tumor location for CS. A strong OS difference between CS and CRS was not identified.
| S-EPMC10553121 | biostudies-literature
Project description:BackgroundFour studies previously indicated that the effect of malaria infection during pregnancy on the risk of low birthweight (LBW; <2,500 g) may depend upon maternal nutritional status. We investigated this dependence further using a large, diverse study population.Methods and findingsWe evaluated the interaction between maternal malaria infection and maternal anthropometric status on the risk of LBW using pooled data from 14,633 pregnancies from 13 studies (6 cohort studies and 7 randomized controlled trials) conducted in Africa and the Western Pacific from 1996-2015. Studies were identified by the Maternal Malaria and Malnutrition (M3) initiative using a convenience sampling approach and were eligible for pooling given adequate ethical approval and availability of essential variables. Study-specific adjusted effect estimates were calculated using inverse probability of treatment-weighted linear and log-binomial regression models and pooled using a random-effects model. The adjusted risk of delivering a baby with LBW was 8.8% among women with malaria infection at antenatal enrollment compared to 7.7% among uninfected women (adjusted risk ratio [aRR] 1.14 [95% confidence interval (CI): 0.91, 1.42]; N = 13,613), 10.5% among women with malaria infection at delivery compared to 7.9% among uninfected women (aRR 1.32 [95% CI: 1.08, 1.62]; N = 11,826), and 15.3% among women with low mid-upper arm circumference (MUAC <23 cm) at enrollment compared to 9.5% among women with MUAC ? 23 cm (aRR 1.60 [95% CI: 1.36, 1.87]; N = 9,008). The risk of delivering a baby with LBW was 17.8% among women with both malaria infection and low MUAC at enrollment compared to 8.4% among uninfected women with MUAC ? 23 cm (joint aRR 2.13 [95% CI: 1.21, 3.73]; N = 8,152). There was no evidence of synergism (i.e., excess risk due to interaction) between malaria infection and MUAC on the multiplicative (p = 0.5) or additive scale (p = 0.9). Results were similar using body mass index (BMI) as an anthropometric indicator of nutritional status. Meta-regression results indicated that there may be multiplicative interaction between malaria infection at enrollment and low MUAC within studies conducted in Africa; however, this finding was not consistent on the additive scale, when accounting for multiple comparisons, or when using other definitions of malaria and malnutrition. The major limitations of the study included availability of only 2 cross-sectional measurements of malaria and the limited availability of ultrasound-based pregnancy dating to assess impacts on preterm birth and fetal growth in all studies.ConclusionsPregnant women with malnutrition and malaria infection are at increased risk of LBW compared to women with only 1 risk factor or none, but malaria and malnutrition do not act synergistically.
| S-EPMC5549702 | biostudies-literature