Meta‐analysis of rate ratios with differential follow‐up by treatment arm: inferring comparative effectiveness of medical devices

Kunz, Lauren; Normand, Sharon‐Lise T.; Sedrakyan, Art

doi:10.1002/sim.6530

Cited by 15 publications

(16 citation statements)

References 20 publications

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“…The ratio of the serological to the egg-positive prevalence and its 95%CI for each study were first calculated. We then meta-analyzed the point estimate of pooled ratio and the corresponding 95%CI [ 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

The ratio of the seroprevalence to the egg-positive prevalence of Schistosoma japonicum in China: a meta-analysis

et al. 2018

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BackgroundSchistosomiasis, caused by Schistosoma japonicum, remains one of the most important parasitic diseases, and detection of S. japonicum infections in humans plays a crucial role in control and treatment. However, comparisons between the parasitological and the immunological examinations in the fields of China are lacking. Therefore we performed a meta-analysis to compare the seroprevalence of Schistosoma japonicum, as determined by IHA or ELISA, with coprological prevalence, as determined by Kato-Katz, and estimate the ratio of the serological to the egg-positive prevalence in order to evaluate the potential threat of egg-negative but worm-positive schistosomiasis.MethodsStudies published up to July 2018 on the parasitological and immunological examinations of schistosomiasis in the fields of China were searched in five databases including CNKI, WanFang, VIP, PubMed and Web of Science. The ratio of the serological to the egg-positive prevalence and its 95%CI for each study were calculated, and then point estimates and their 95%CIs of pooled prevalence ratio were meta-analyzed. Subgroup meta-analyses were also performed according to potential influential factors.ResultsA total of 23 articles were included. The prevalence ratio varied from 0.57 to 48.83 for IHA to Kato-Katz and ranged from 0.38 to 13.97 for ELISA to Kato-Katz. The pooled ratio was 4.72 (95%CI: 3.87~ 5.76) for IHA to KK and 4.65 (95%CI: 3.50~ 6.17) for ELISA to KK. Subgroup analyses implied that the ratio of the serological to the egg-positive prevalence may decrease with the endemic levels. The highest prevalence ratio was observed when Kato-Katz was performed with three slides per stool or in hilly and mountainous regions.ConclusionsThe worm-determined prevalence by IHA or ELISA is 4- to 5-fold higher than the egg-determined prevalence by Kato-Katz, which implied Kato-Katz may largely underestimate the prevalence of S. japonicum in China. The degree of underestimation was greater when Kato-Katz with three slides per stool was carried out, especially in low endemic areas or in hilly and mountainous regions. Therefore, more attention should be paid to those egg-negative but worm-positive patients with the aim of final elimination of S. japonicum in China.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3320-5) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

The ratio of the seroprevalence to the egg-positive prevalence of Schistosoma japonicum in China: a meta-analysis

et al. 2018

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“…Alternative, more advanced methods such as a dose-response meta-analysis are used. 58,59 Consultation with an experienced methodologist is recommended when such alternatives are considered. Formally, subgroup analysis includes 2 parts: calculating the summary outcome measure and statistical heterogeneity in each subgroup and comparing the summary outcome measure across subgroups.…”

Section: Subgroup Analysismentioning

confidence: 99%

Methodological Standards for Meta-Analyses and Qualitative Systematic Reviews of Cardiac Prevention and Treatment Studies: A Scientific Statement From the American Heart Association

Rao¹,

López-Jiménez²,

Boyd³

et al. 2017

Circulation

198

128

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Meta-analyses are becoming increasingly popular, especially in the fields of cardiovascular disease prevention and treatment. They are often considered to be a reliable source of evidence for making healthcare decisions. Unfortunately, problems among meta-analyses such as the misapplication and misinterpretation of statistical methods and tests are long-standing and widespread. The purposes of this statement are to review key steps in the development of a metaanalysis and to provide recommendations that will be useful for carrying out meta-analyses and for readers and journal editors, who must interpret the findings and gauge methodological quality. To make the statement practical and accessible, detailed descriptions of statistical methods have been omitted. Based on a survey of cardiovascular metaanalyses, published literature on methodology, expert consultation, and consensus among the writing group, key recommendations are provided. Recommendations reinforce several current practices, including protocol registration; comprehensive search strategies; methods for data extraction and abstraction; methods for identifying, measuring, and dealing with heterogeneity; and statistical methods for pooling results. Other practices should be discontinued, including the use of levels of evidence and evidence hierarchies to gauge the value and impact of different study designs (including meta-analyses) and the use of structured tools to assess the quality of studies to be included in a metaanalysis. We also recommend choosing a pooling model for conventional meta-analyses (fixed effect or random effects) on the basis of clinical and methodological similarities among studies to be included, rather than the results of a test for statistical heterogeneity. CLINICAL STATEMENTS AND GUIDELINES D espite the increasing popularity of meta-analyses and systematic reviews in general, problems with methodology are widespread and frequently undermine the credibility of the results. New guidance is needed for both researchers who carry out meta-analyses and systematic reviews in general and the consumers who read them and rely on the results. The term meta-analysis was coined in 1976 by the American statistician Gene Glass, who wrote, "I use it to refer to the statistical analysis of a large collection of results from individual studies for the purpose of integrating findings. It connotes a rigorous alternative to the casual, narrative discussions of research studies which typify our attempts to make sense of the rapidly expanding literature."1 Meta-analyses are a subcategory of the broader category of studies known as systematic reviews.Qualitative systematic reviews include explicit and detailed methods for identification, selection, and grading the quality of individual studies and overall evidence but do not pool results across studies. Meta-analysis is synonymous with the term quantitative systematic review and by definition includes pooling of results across studies. The emphasis in this statement is on metaanalysis bec...

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“…In a rare events setting, it may be reasonable to use the total duration of follow-up to censoring d ij· instead of t ij· , as e.g. Stijnen et al (2010), Böhning et al (2015) and Kunz et al (2015) have done. This is especially appropriate, if the event of interest is always fatal so that d ij· = t ij· .…”

Section: Time-to-event Approaches For Aggregate Datamentioning

confidence: 99%

Meta-analysis of aggregate data on medical events

Holzhauer

2016

Statist. Med.

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A meta-analysis combines analysis results from multiple independent sets of data. Metaanalyses to assess whether a treatment affects the occurrence of a medical event are frequently based on published aggregate data from controlled clinical trials. In that setting, the number of patients with an event in each trial is often treated as a binomial random variable. This assumes that censoring times have the same distribution in all treatment groups of a trial, and are independent of event times.To allow for different drop-out time distributions across treatment groups, we derive a likelihood for commonly available aggregate data that assumes specific event and drop-out time distributions for a number of situations. These include exponentially or Weibull distributed event and drop-out times, event-driven trials, the situation when a patient may experience multiple potentially fatal events, and when individual patient data are available for some trials.The assumption that parameters of survival distributions are exchangeable between trials is more plausible than for the expected proportion of patients with an event.For this reason the proposed likelihood is more suitable than a binomial likelihood for use in hierarchical meta-analysis models and for incorporating prior information from historical control group data. Hierarchical models and prior information are useful in sparse data settings and to avoid parameter identifiability problems. We use simulations to compare hierarchical Bayesian models with the proposed trial-level likelihood against other meta-analysis methods and to compare methods for using historical control group data. We also demonstrate how conjugate priors may be used to analyze exponentially distributed failure times without the need for Markov chain Monte Carlo methods.

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Meta‐analysis of rate ratios with differential follow‐up by treatment arm: inferring comparative effectiveness of medical devices

Cited by 15 publications

References 20 publications

The ratio of the seroprevalence to the egg-positive prevalence of Schistosoma japonicum in China: a meta-analysis

The ratio of the seroprevalence to the egg-positive prevalence of Schistosoma japonicum in China: a meta-analysis

Methodological Standards for Meta-Analyses and Qualitative Systematic Reviews of Cardiac Prevention and Treatment Studies: A Scientific Statement From the American Heart Association

Meta-analysis of aggregate data on medical events

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