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Meta-analysis of rare binary events in treatment groups with unequal variability.

ABSTRACT: Meta-analysis has been widely used to synthesize information from related studies to achieve reliable findings. However, in studies of rare events, the event counts are often low or even zero, and so standard meta-analysis methods such as fixed-effect models with continuity correction may cause substantial bias in estimation. Recently, Bhaumik et al. developed a simple average estimator for the overall treatment effect based on a random effects model. They proved that the simple average method with the continuity correction factor 0.5 (SA_0.5) is the least biased for large samples and showed via simulation that it has superior performance when compared with other commonly used estimators. However, the random effects models used in previous work are restrictive because they all assume that the variability in the treatment group is equal to or always greater than that in the control group. Under a general framework that explicitly allows treatment groups with unequal variability but assumes no direction, we prove that SA_0.5 is still the least biased for large samples. Meanwhile, to account for a trade-off between the bias and variance in estimation, we consider the mean squared error to assess estimation efficiency and show that SA_0.5 fails to minimize the mean squared error. Under a new random effects model that accommodates groups with unequal variability, we thoroughly compare the performance of various methods for both large and small samples via simulation and draw conclusions about when to use which method in terms of bias, mean squared error, type I error, and confidence interval coverage. A data example of rosiglitazone meta-analysis is used to provide further comparison.

SUBMITTER: Li L

PROVIDER: S-EPMC5599377 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Meta-analysis of rare binary events in treatment groups with unequal variability.

Li Lie L Wang Xinlei X

Statistical methods in medical research 20170731 1

Meta-analysis has been widely used to synthesize information from related studies to achieve reliable findings. However, in studies of rare events, the event counts are often low or even zero, and so standard meta-analysis methods such as fixed-effect models with continuity correction may cause substantial bias in estimation. Recently, Bhaumik et al. developed a simple average estimator for the overall treatment effect based on a random effects model. They proved that the simple average method w ...[more]

PMID: 28760075

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Project description:Background/aimsRegulatory approval of a drug or device involves an assessment of not only the benefits but also the risks of adverse events associated with the therapeutic agent. Although randomized controlled trials (RCTs) are the gold standard for evaluating effectiveness, the number of treated patients in a single RCT may not be enough to detect a rare but serious side effect of the treatment. Meta-analysis plays an important role in the evaluation of the safety of medical products and has advantage over analyzing a single RCT when estimating the rate of adverse events.MethodsIn this article, we compare 15 widely used meta-analysis models under both Bayesian and frequentist frameworks when outcomes are extremely infrequent or rare. We present extensive simulation study results and then apply these methods to a real meta-analysis that considers RCTs investigating the effect of rosiglitazone on the risks of myocardial infarction and of death from cardiovascular causes.ResultsOur simulation studies suggest that the beta hyperprior method modeling treatment group-specific parameters and accounting for heterogeneity performs the best. Most models ignoring between-study heterogeneity give poor coverage probability when such heterogeneity exists. In the data analysis, different methods provide a wide range of log odds ratio estimates between rosiglitazone and control treatments with a mixed conclusion on their statistical significance based on 95% confidence (or credible) intervals.ConclusionIn the rare event setting, treatment effect estimates obtained from traditional meta-analytic methods may be biased and provide poor coverage probability. This trend worsens when the data have large between-study heterogeneity. In general, we recommend methods that first estimate the summaries of treatment-specific risks across studies and then relative treatment effects based on the summaries when appropriate. Furthermore, we recommend fitting various methods, comparing the results and model performance, and investigating any significant discrepancies among them.

Project description:Meta-analysis of clinical trials targeting rare events face particular challenges when the data lack adequate number of events and are susceptible to high levels of heterogeneity. The standard meta-analysis methods (DerSimonian Laird (DL) and Mantel-Haenszel (MH)) often lead to serious distortions because of such data sparsity. Applications of the methods suited to specific incidence and heterogeneity characteristics are lacking, thus we compared nine available methods in a simulation study. We generated 360 meta-analysis scenarios where each considered different incidences, sample sizes, between-study variance (heterogeneity) and treatment allocation. We include globally recommended methods such as inverse-variance fixed/random-effect (IV-FE/RE), classical-MH, MH-FE, MH-DL, Peto, Peto-DL and the two extensions for MH bootstrapped-DL (bDL) and Peto-bDL. Performance was assessed on mean bias, mean error, coverage and power. In the absence of heterogeneity, the coverage and power when combined revealed small differences in meta-analysis involving rare and very rare events. The Peto-bDL method performed best, but only in smaller sample sizes involving rare events. For medium-to-larger sample sizes, MH-bDL was preferred. For meta-analysis involving very rare events, Peto-bDL was the best performing method which was sustained across all sample sizes. However, in meta-analysis with 20% or more heterogeneity, the coverage and power were insufficient. Performance based on mean bias and mean error was almost identical across methods. To conclude, in meta-analysis of rare binary outcomes, our results suggest that Peto-bDL is better in both rare and very rare event settings in meta-analysis with limited sample sizes. However, when heterogeneity is large, the coverage and power to detect rare events are insufficient. Whilst this study shows that some of the less studied methods appear to have good properties under sparse data scenarios, further work is needed to assess them against the more complex distributional-based methods to understand their overall performances.

Project description:ObjectiveTo examine the association between antihypertensive treatment and specific adverse events.DesignSystematic review and meta-analysis.Eligibility criteriaRandomised controlled trials of adults receiving antihypertensives compared with placebo or no treatment, more antihypertensive drugs compared with fewer antihypertensive drugs, or higher blood pressure targets compared with lower targets. To avoid small early phase trials, studies were required to have at least 650 patient years of follow-up.Information sourcesSearches were conducted in Embase, Medline, CENTRAL, and the Science Citation Index databases from inception until 14 April 2020.Main outcome measuresThe primary outcome was falls during trial follow-up. Secondary outcomes were acute kidney injury, fractures, gout, hyperkalaemia, hypokalaemia, hypotension, and syncope. Additional outcomes related to death and major cardiovascular events were extracted. Risk of bias was assessed using the Cochrane risk of bias tool, and random effects meta-analysis was used to pool rate ratios, odds ratios, and hazard ratios across studies, allowing for between study heterogeneity (?2).ResultsOf 15?023 articles screened for inclusion, 58 randomised controlled trials were identified, including 280?638 participants followed up for a median of 3 (interquartile range 2-4) years. Most of the trials (n=40, 69%) had a low risk of bias. Among seven trials reporting data for falls, no evidence was found of an association with antihypertensive treatment (summary risk ratio 1.05, 95% confidence interval 0.89 to 1.24, ?2=0.009). Antihypertensives were associated with an increased risk of acute kidney injury (1.18, 95% confidence interval 1.01 to 1.39, ?2=0.037, n=15), hyperkalaemia (1.89, 1.56 to 2.30, ?2=0.122, n=26), hypotension (1.97, 1.67 to 2.32, ?2=0.132, n=35), and syncope (1.28, 1.03 to 1.59, ?2=0.050, n=16). The heterogeneity between studies assessing acute kidney injury and hyperkalaemia events was reduced when focusing on drugs that affect the renin angiotensin-aldosterone system. Results were robust to sensitivity analyses focusing on adverse events leading to withdrawal from each trial. Antihypertensive treatment was associated with a reduced risk of all cause mortality, cardiovascular death, and stroke, but not of myocardial infarction.ConclusionsThis meta-analysis found no evidence to suggest that antihypertensive treatment is associated with falls but found evidence of an association with mild (hyperkalaemia, hypotension) and severe adverse events (acute kidney injury, syncope). These data could be used to inform shared decision making between doctors and patients about initiation and continuation of antihypertensive treatment, especially in patients at high risk of harm because of previous adverse events or poor renal function.RegistrationPROSPERO CRD42018116860.

Project description:BackgroundDespite a historical association with poor tolerability, a comprehensive review on safety of antileishmanial chemotherapies is lacking. We carried out an update of a previous systematic review of all published clinical trials in visceral leishmaniasis (VL) from 1980 to 2019 to document any reported serious adverse events (SAEs).MethodsFor this updated systematic review, we searched the following databases from 1st Jan 2016 through 2nd of May 2019: PUBMED, Embase, Scopus, Web of Science, Cochrane, clinicaltrials.gov, WHO ICTRP, and the Global Index Medicus. We included randomised and non-randomised interventional studies aimed at assessing therapeutic efficacy and extracted the number of SAEs reported within the first 30 days of treatment initiation. The incidence rate of death (IRD) from individual treatment arms were combined in a meta-analysis using random effects Poisson regression.ResultsWe identified 157 published studies enrolling 35,376 patients in 347 treatment arms. Pentavalent antimony was administered in 74 (21.3%), multiple-dose liposomal amphotericin B (L-AmB) in 52 (15.0%), amphotericin b deoxycholate in 51 (14.7%), miltefosine in 33 (9.5%), amphotericin b fat/lipid/colloid/cholesterol in 31 (8.9%), and single-dose L-AmB in 17 (4.9%) arms. There was a total of 804 SAEs reported of which 793 (including 428 deaths) were extracted at study arm level (11 SAEs were reported at study level only). During the first 30 days, there were 285 (66.6%) deaths with the overall IRD estimated at 0.068 [95% confidence interval (CI): 0.041-0.114; I2 = 81.4%; 95% prediction interval (PI): 0.001-2.779] per 1,000 person-days at risk; the rate was 0.628 [95% CI: 0.368-1.021; I2 = 82.5%] in Eastern Africa, and 0.041 [95% CI: 0.021-0.081; I2 = 68.1%] in the Indian Subcontinent. In 21 study arms which clearly indicated allowing the inclusion of patients with HIV co-infections the IRD was 0.575 [95% CI: 0.244-1.355; I2 = 91.9%] compared to 0.043 [95% CI: 0.020-0.090; I2 = 62.5%] in 160 arms which excluded HIV co-infections.ConclusionMortality within the first 30 days of VL treatment initiation was a rarely reported event in clinical trials with an overall estimated rate of 0.068 deaths per 1,000 person-days at risk, though it varied across regions and patient populations. These estimates may serve as a benchmark for future trials against which mortality data from prospective and pharmacovigilance studies can be compared. The methodological limitations exposed by our review support the need to assemble individual patient data (IPD) to conduct robust IPD meta-analyses and generate stronger evidence from existing trials to support treatment guidelines and guide future research.

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Meta-analysis of rare binary events in treatment groups with unequal variability.

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Meta-analysis of rare binary events in treatment groups with unequal variability.

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