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1.  Non-invasive prenatal diagnostic test accuracy for fetal sex using cell-free DNA a review and meta-analysis 
BMC Research Notes  2012;5:476.
Cell-free fetal DNA (cffDNA) can be detected in maternal blood during pregnancy, opening the possibility of early non-invasive prenatal diagnosis for a variety of genetic conditions. Since 1997, many studies have examined the accuracy of prenatal fetal sex determination using cffDNA, particularly for pregnancies at risk of an X-linked condition. Here we report a review and meta-analysis of the published literature to evaluate the use of cffDNA for prenatal determination (diagnosis) of fetal sex. We applied a sensitive search of multiple bibliographic databases including PubMed (MEDLINE), EMBASE, the Cochrane library and Web of Science.
Ninety studies, incorporating 9,965 pregnancies and 10,587 fetal sex results met our inclusion criteria. Overall mean sensitivity was 96.6% (95% credible interval 95.2% to 97.7%) and mean specificity was 98.9% (95% CI = 98.1% to 99.4%). These results vary very little with trimester or week of testing, indicating that the performance of the test is reliably high.
Based on this review and meta-analysis we conclude that fetal sex can be determined with a high level of accuracy by analyzing cffDNA. Using cffDNA in prenatal diagnosis to replace or complement existing invasive methods can remove or reduce the risk of miscarriage. Future work should concentrate on the economic and ethical considerations of implementing an early non-invasive test for fetal sex.
PMCID: PMC3444439  PMID: 22937795
Cell-free fetal DNA; Meta-analysis; Non-invasive prenatal diagnosis
2.  Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews 
Background Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-study heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of heterogeneity in particular areas of health care.
Methods Our analyses included 14 886 meta-analyses from the Cochrane Database of Systematic Reviews. We classified each meta-analysis according to the type of outcome, type of intervention comparison and medical specialty. By modelling the study data from all meta-analyses simultaneously, using the log odds ratio scale, we investigated the impact of meta-analysis characteristics on the underlying between-study heterogeneity variance. Predictive distributions were obtained for the heterogeneity expected in future meta-analyses.
Results Between-study heterogeneity variances for meta-analyses in which the outcome was all-cause mortality were found to be on average 17% (95% CI 10–26) of variances for other outcomes. In meta-analyses comparing two active pharmacological interventions, heterogeneity was on average 75% (95% CI 58–95) of variances for non-pharmacological interventions. Meta-analysis size was found to have only a small effect on heterogeneity. Predictive distributions are presented for nine different settings, defined by type of outcome and type of intervention comparison. For example, for a planned meta-analysis comparing a pharmacological intervention against placebo or control with a subjectively measured outcome, the predictive distribution for heterogeneity is a log-normal (−2.13, 1.582) distribution, which has a median value of 0.12. In an example of meta-analysis of six studies, incorporating external evidence led to a smaller heterogeneity estimate and a narrower confidence interval for the combined intervention effect.
Conclusions Meta-analysis characteristics were strongly associated with the degree of between-study heterogeneity, and predictive distributions for heterogeneity differed substantially across settings. The informative priors provided will be very beneficial in future meta-analyses including few studies.
PMCID: PMC3396310  PMID: 22461129
Meta-analysis; heterogeneity; intervention studies; Bayesian analysis
3.  Characteristics of meta-analyses and their component studies in the Cochrane Database of Systematic Reviews: a cross-sectional, descriptive analysis 
Cochrane systematic reviews collate and summarise studies of the effects of healthcare interventions. The characteristics of these reviews and the meta-analyses and individual studies they contain provide insights into the nature of healthcare research and important context for the development of relevant statistical and other methods.
We classified every meta-analysis with at least two studies in every review in the January 2008 issue of the Cochrane Database of Systematic Reviews (CDSR) according to the medical specialty, the types of interventions being compared and the type of outcome. We provide descriptive statistics for numbers of meta-analyses, numbers of component studies and sample sizes of component studies, broken down by these categories.
We included 2321 reviews containing 22,453 meta-analyses, which themselves consist of data from 112,600 individual studies (which may appear in more than one meta-analysis). Meta-analyses in the areas of gynaecology, pregnancy and childbirth (21%), mental health (13%) and respiratory diseases (13%) are well represented in the CDSR. Most meta-analyses address drugs, either with a control or placebo group (37%) or in a comparison with another drug (25%). The median number of meta-analyses per review is six (inter-quartile range 3 to 12). The median number of studies included in the meta-analyses with at least two studies is three (inter-quartile range 2 to 6). Sample sizes of individual studies range from 2 to 1,242,071, with a median of 91 participants.
It is clear that the numbers of studies eligible for meta-analyses are typically very small for all medical areas, outcomes and interventions covered by Cochrane reviews. This highlights the particular importance of suitable methods for the meta-analysis of small data sets. There was little variation in number of studies per meta-analysis across medical areas, across outcome data types or across types of interventions being compared.
PMCID: PMC3247075  PMID: 22114982
4.  Imputation methods for missing outcome data in meta-analysis of clinical trials 
Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.
To review and develop imputation methods for missing outcome data in meta-analysis of clinical trials with binary outcomes.
We review some common strategies, such as simple imputation of positive or negative outcomes, and develop a general approach involving ‘informative missingness odds ratios’ (IMORs). We describe several choices for weighting studies in the meta-analysis, and illustrate methods using a meta-analysis of trials of haloperidol for schizophrenia.
IMORs describe the relationship between the unknown risk among missing participants and the known risk among observed participants. They are allowed to differ between treatment groups and across trials. Application of IMORs and other methods to the haloperidol trials reveals the overall conclusion to be robust to different assumptions about the missing data.
The methods are based on summary data from each trial (number of observed positive outcomes, number of observed negative outcomes and number of missing outcomes) for each intervention group. This limits the options for analysis, and greater flexibility would be available with individual participant data.
We propose that available reasons for missingness be used to determine appropriate IMORs. We also recommend a strategy for undertaking sensitivity analyses, in which the IMORs are varied over plausible ranges.
PMCID: PMC2602608  PMID: 18559412
5.  Online genetic databases informing human genome epidemiology 
With the advent of high throughput genotyping technology and the information available via projects such as the human genome sequencing and the HapMap project, more and more data relevant to the study of genetics and disease risk will be produced. Systematic reviews and meta-analyses of human genome epidemiology studies rely on the ability to identify relevant studies and to obtain suitable data from these studies. A first port of call for most such reviews is a search of MEDLINE. We examined whether this could be usefully supplemented by identifying databases on the World Wide Web that contain genetic epidemiological information.
We conducted a systematic search for online databases containing genetic epidemiological information on gene prevalence or gene-disease association. In those containing information on genetic association studies, we examined what additional information could be obtained to supplement a MEDLINE literature search.
We identified 111 databases containing prevalence data, 67 databases specific to a single gene and only 13 that contained information on gene-disease associations. Most of the latter 13 databases were linked to MEDLINE, although five contained information that may not be available from other sources.
There is no single resource of structured data from genetic association studies covering multiple diseases, and in relation to the number of studies being conducted there is very little information specific to gene-disease association studies currently available on the World Wide Web. Until comprehensive data repositories are created and utilized regularly, new data will remain largely inaccessible to many systematic review authors and meta-analysts.
PMCID: PMC1929117  PMID: 17610726
6.  STrengthening the REporting of Genetic Association studies (STREGA) – an extension of the STROBE statement 
Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.
PMCID: PMC2730482  PMID: 19297801
Epidemiology; gene-disease associations; gene-environment interaction; genetics; genome-wide association; meta-analysis; reporting recommendations; systematic review
7.  Planning future studies based on the conditional power of a meta-analysis 
Statistics in Medicine  2012;32(1):11-24.
Systematic reviews often provide recommendations for further research. When meta-analyses are inconclusive, such recommendations typically argue for further studies to be conducted. However, the nature and amount of future research should depend on the nature and amount of the existing research. We propose a method based on conditional power to make these recommendations more specific. Assuming a random-effects meta-analysis model, we evaluate the influence of the number of additional studies, of their information sizes and of the heterogeneity anticipated among them on the ability of an updated meta-analysis to detect a prespecified effect size. The conditional powers of possible design alternatives can be summarized in a simple graph which can also be the basis for decision making. We use three examples from the Cochrane Database of Systematic Reviews to demonstrate our strategy. We demonstrate that if heterogeneity is anticipated, it might not be possible for a single study to reach the desirable power no matter how large it is. Copyright © 2012 John Wiley & Sons, Ltd.
PMCID: PMC3562483  PMID: 22786670
meta-analysis; power; sample size; evidence-based medicine; random effects; cumulative meta-analysis
8.  Estimating within-study covariances in multivariate meta-analysis with multiple outcomes 
Statistics in Medicine  2012;32(7):1191-1205.
Multivariate meta-analysis allows the joint synthesis of effect estimates based on multiple outcomes from multiple studies, accounting for the potential correlations among them. However, standard methods for multivariate meta-analysis for multiple outcomes are restricted to problems where the within-study correlation is known or where individual participant data are available. This paper proposes an approach to approximating the within-study covariances based on information about likely correlations between underlying outcomes. We developed methods for both continuous and dichotomous data and for combinations of the two types. An application to a meta-analysis of treatments for stroke illustrates the use of the approximated covariance in multivariate meta-analysis with correlated outcomes. Copyright © 2012 John Wiley & Sons, Ltd.
PMCID: PMC3618374  PMID: 23208849
multivariate meta-analysis; correlated outcomes; nested events; delta method; within-study correlation

Results 1-8 (8)