Empirical comparison of univariate and multivariate meta-analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes.
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ABSTRACT:
SUBMITTER: Price MJ
PROVIDER: S-EPMC6771837 | biostudies-other | 2019 Sep
REPOSITORIES: biostudies-other
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Project description:BackgroundSystematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on the hazard ratio (HR) scale or can be dichotomized and analysed as binary outcomes using effect measures such as odds ratios (OR) or risk ratios (RR). We investigated the impact of reanalysing meta-analyses from the CDSR that used these different effect measures.MethodsWe extracted two types of meta-analysis data from the CDSR: either recorded in a binary form only ("binary"), or in binary form together with observed minus expected and variance statistics ("OEV"). We explored how results for time-to-event outcomes originally analysed as "binary" change when analysed using the complementary log-log (clog-log) link on a HR scale. For the data originally analysed as HRs ("OEV"), we compared these results to analysing them as binary on a HR scale using the clog-log link or using a logit link on an OR scale.ResultsThe pooled HR estimates were closer to 1 than the OR estimates in the majority of meta-analyses. Important differences in between-study heterogeneity between the HR and OR analyses were also observed. These changes led to discrepant conclusions between the OR and HR scales in some meta-analyses. Situations under which the clog-log link performed better than logit link and vice versa were apparent, indicating that the correct choice of the method does matter. Differences between scales arise mainly when event probability is high and may occur via differences in between-study heterogeneity or via increased within-study standard error in the OR relative to the HR analyses.ConclusionsWe identified that dichotomising time-to-event outcomes may be adequate for low event probabilities but not for high event probabilities. In meta-analyses where only binary data are available, the complementary log-log link may be a useful alternative when analysing time-to-event outcomes as binary, however the exact conditions need further exploration. These findings provide guidance on the appropriate methodology that should be used when conducting such meta-analyses.
| S-EPMC8934481 | biostudies-literature
Project description:Objectives Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. Study design and setting We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. Results Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R2 of 50%). Applied examples are used to illustrate the use of the BoS prediction model. Conclusions Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required. Supplementary Information The online version contains supplementary material available at 10.1186/s13643-022-01999-0.
| S-EPMC9316363 | biostudies-literature
Project description:BACKGROUND:There is growing interest in evaluating differences in healthcare interventions across routinely collected demographic characteristics. However, individual subgroup analyses in randomized controlled trials are often not prespecified, adjusted for multiple testing, or conducted using the appropriate statistical test for interaction, and therefore frequently lack credibility. Meta-analyses can be used to examine the validity of potential subgroup differences by collating evidence across trials. Here, we characterize the conduct and clinical translation of age-treatment subgroup analyses in Cochrane reviews. METHODS:For a random sample of 928 Cochrane intervention reviews of randomized trials, we determined how often subgroup analyses of age are reported, how often these analyses have a P?<?0.05 from formal interaction testing, how frequently subgroup differences first observed in an individual trial are later corroborated by other trials in the same meta-analysis, and how often statistically significant results are included in commonly used clinical management resources (BMJ Best Practice, UpToDate, Cochrane Clinical Answers, Google Scholar, and Google search). RESULTS:Among 928 Cochrane intervention reviews, 189 (20.4%) included plans to conduct age-treatment subgroup analyses. The vast majority (162 of 189, 85.7%) of the planned analyses were not conducted, commonly because of insufficient trial data. There were 22 reviews that conducted their planned age-treatment subgroup analyses, and another 3 reviews appeared to perform unplanned age-treatment subgroup analyses. These 25 (25 of 928, 2.7%) reviews conducted a total of 97 age-treatment subgroup analyses, of which 65 analyses (in 20 reviews) had non-overlapping subgroup levels. Among the 65 age-treatment subgroup analyses, 14 (21.5%) did not report any formal interaction testing. Seven (10.8%) reported P?<?0.05 from formal age-treatment interaction testing; however, none of these seven analyses were in reviews that discussed the potential biological rationale or clinical significance of the subgroup findings or had results that were included in common clinical practice resources. CONCLUSION:Age-treatment subgroup analyses in Cochrane intervention reviews were frequently planned but rarely conducted, and implications of detected interactions were not discussed in the reviews or mentioned in common clinical resources. When subgroup analyses are performed, authors should report the findings, compare the results to previous studies, and outline any potential impact on clinical care.
| S-EPMC6805640 | biostudies-literature
Project description:ObjectiveTo compare the methodological quality and conclusions in Cochrane reviews with those in industry supported meta-analyses and other meta-analyses of the same drugs.DesignSystematic review comparing pairs of meta-analyses that studied the same two drugs in the same disease and were published within two years of each other.Data sourcesCochrane Database of Systematic Reviews (2003, issue 1), PubMed, and Embase.Data extractionTwo observers independently extracted data and used a validated scale to judge the methodological quality of the reviews.Results175 of 1596 Cochrane reviews had a meta-analysis that compared two drugs. Twenty four meta-analyses that matched the Cochrane reviews were found: eight were industry supported, nine had undeclared support, and seven had no support or were supported by non-industry sources. On a 0-7 scale, the median quality score was 7 for Cochrane reviews and 3 for other reviews (P < 0.01). Compared with industry supported reviews and reviews with undeclared support, Cochrane reviews had more often considered the potential for bias in the review--for example, by describing the method of concealment of allocation and describing excluded patients or studies. The seven industry supported reviews that had conclusions recommended the experimental drug without reservations, compared with none of the Cochrane reviews (P = 0.02), although the estimated treatment effect was similar on average (z = 0.46, P = 0.64). Reviews with undeclared support and reviews with not for profit support or no support had conclusions that were similar in cautiousness to the Cochrane reviews.ConclusionsIndustry supported reviews of drugs should be read with caution as they were less transparent, had few reservations about methodological limitations of the included trials, and had more favourable conclusions than the corresponding Cochrane reviews.
| S-EPMC1602036 | biostudies-literature
Project description:Publication bias, more generally termed as small-study effect, is a major threat to the validity of meta-analyses. Most meta-analysts rely on the p values from statistical tests to make a binary decision about the presence or absence of small-study effects. Measures are available to quantify small-study effects' magnitude, but the current literature lacks clear rules to help evidence users in judging whether such effects are minimal or substantial. This article aims to provide rules of thumb for interpreting the measures. We use six measures to evaluate small-study effects in 29?932 meta-analyses from the Cochrane Database of Systematic Reviews They include Egger's regression intercept and the skewness under both the fixed-effect and random-effects settings, the proportion of suppressed studies, and the relative change of the estimated overall result due to small-study effects. The cut-offs for different extents of small-study effects are determined based on the quantiles in these distributions. We present the empirical distributions of the six measures and propose a rough guide to interpret the measures' magnitude. The proposed rules of thumb may help evidence users grade the certainty in evidence as impacted by small-study effects.
| S-EPMC6942244 | biostudies-literature
Project description:BACKGROUND:Information on subgroup assessments in systematic reviews (SR) of atrial fibrillation (AF) is limited. This review aims to describe subgroup analyses in AF SRs to inform the design of SRs and randomized trials as well as clinical practice. METHODS:We conducted a cross sectional meta-epidemiological study of Cochrane AF reviews by searching AF (including variants) in the title, abstract, or keyword field without date or language restrictions (Issue 9; September 2018). Two reviewers independently extracted study characteristics to summarize frequency of subgroups pre-specified and conducted and report credibility of subgroup effects claimed. RESULTS:Of 39 Cochrane reviews identified, 17 met inclusion criteria (including 168 reports of 127 randomized trials) and the majority (16; 94.1%) conducted meta-analysis of outcomes. Most (13; 76.5%) planned pre-specified subgroup analyses; 7 of which (41.2%) conducted subgroups. In these 7 reviews, 56 subgroups were planned, 17 (30.4%) conducted and 6 (10.7%) yielded subgroup effects. Variables such as co-morbid disease, stroke risk factors, prior stroke/transient ischemic attack, age, race, and sex represented 44% (24 subgroups) of all planned subgroups (8 conducted; 14.3%); however, information on covariate selection was lacking. Overall, more subgroups were planned than conducted (mean difference (95% CI) 2.3 (1.2-3.5, p?<?0.001)). Of all subgroups conducted, anticoagulant characteristics comprised a third of all subgroup effects (n?=?5, 35.7%). The credibility of subgroups identified (n?=?14) was assessed and less than half (43%) represented one of a small number of pre-specified hypothesis and rarely were effects seen within studies (7%). Of 5 reviews that reported subgroup effects, only 3 discussed subgroup effects as part of the overall conclusions; none discussed credibility of subgroup effects. CONCLUSIONS:This meta-epidemiological review of a subset of Cochrane AF reviews suggests that planning and reporting of subgroup analyses in AF reviews can be improved to better inform clinical management. Most pre-specified subgroup analyses were not performed, important variables (such as stroke, bleeding risk, and other comorbidities) were rarely examined and credibility of subgroup effects claimed was low. Future reviews should aim to identify important subgroups in their protocols and use recommended approaches to test subgroup effects in order to better support clinical decision-making.
| S-EPMC6814034 | biostudies-literature
Project description:Objective To evaluate the frequency, validity, and relevance of statistically significant (P<0.05) sex-treatment interactions in randomized controlled trials in Cochrane meta-analyses.Design Meta-epidemiological study.Data sources Cochrane Database of Systematic Reviews (CDSR) and PubMed.Eligibility criteria for study selection Reviews published in the CDSR with sex-treatment subgroup analyses in the forest plots, using data from randomized controlled trials.Data extraction Information on the study design and sex subgroup data were extracted from reviews and forest plots that met inclusion criteria. For each statistically significant sex-treatment interaction, the potential for biological plausibility and clinical significance was considered.Results Among the 41 reviews with relevant data, there were 109 separate treatment-outcome analyses ("topics"). Among the 109 topics, eight (7%) had a statistically significant sex-treatment interaction. The 109 topics included 311 randomized controlled trials (162 with both sexes, 46 with males only, 103 with females only). Of the 162 individual randomized controlled trials that included both sexes, 15 (9%) had a statistically significant sex-treatment interaction. Of four topics where the first published randomized controlled trial had a statistically significant sex-treatment interaction, no meta-analyses that included other randomized controlled trials retained the statistical significance and no meta-analyses showed statistical significance when data from the first published randomized controlled trial were excluded. Of the eight statistically significant sex-treatment interactions from the overall analyses, only three were discussed by the CDSR reviewers for a potential impact on different clinical management for males compared with females. None of these topics had a sex-treatment interaction that influenced treatment recommendations in recent guidelines. UpToDate, an online physician-authored clinical decision support resource, suggested differential management of men and women for one of these sex-treatment interactions.Conclusion Statistically significant sex-treatment interactions are only slightly more frequent than what would be expected by chance and there is little evidence of subsequent corroboration or clinical relevance of sex-treatment interactions.
| S-EPMC5122320 | biostudies-literature
Project description:BackgroundMost meta-analyses include data from one or more small studies that, individually, do not have power to detect an intervention effect. The relative influence of adequately powered and underpowered studies in published meta-analyses has not previously been explored. We examine the distribution of power available in studies within meta-analyses published in Cochrane reviews, and investigate the impact of underpowered studies on meta-analysis results.Methods and findingsFor 14,886 meta-analyses of binary outcomes from 1,991 Cochrane reviews, we calculated power per study within each meta-analysis. We defined adequate power as ?50% power to detect a 30% relative risk reduction. In a subset of 1,107 meta-analyses including 5 or more studies with at least two adequately powered and at least one underpowered, results were compared with and without underpowered studies. In 10,492 (70%) of 14,886 meta-analyses, all included studies were underpowered; only 2,588 (17%) included at least two adequately powered studies. 34% of the meta-analyses themselves were adequately powered. The median of summary relative risks was 0.75 across all meta-analyses (inter-quartile range 0.55 to 0.89). In the subset examined, odds ratios in underpowered studies were 15% lower (95% CI 11% to 18%, P<0.0001) than in adequately powered studies, in meta-analyses of controlled pharmacological trials; and 12% lower (95% CI 7% to 17%, P<0.0001) in meta-analyses of controlled non-pharmacological trials. The standard error of the intervention effect increased by a median of 11% (inter-quartile range -1% to 35%) when underpowered studies were omitted; and between-study heterogeneity tended to decrease.ConclusionsWhen at least two adequately powered studies are available in meta-analyses reported by Cochrane reviews, underpowered studies often contribute little information, and could be left out if a rapid review of the evidence is required. However, underpowered studies made up the entirety of the evidence in most Cochrane reviews.
| S-EPMC3609745 | biostudies-literature
Project description:BackgroundMany 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.MethodsOur 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.ResultsBetween-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, 1.58(2)) 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.ConclusionsMeta-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.
| S-EPMC3396310 | biostudies-literature
Project description:BackgroundMeta-analysis is increasingly used to synthesize proportions (e.g., disease prevalence). It can be implemented with widely used two-step methods or one-step methods, such as generalized linear mixed models (GLMMs). Existing simulation studies have shown that GLMMs outperform the two-step methods in some settings. It is, however, unclear whether these simulation settings are common in the real world. We aim to compare the real-world performance of various meta-analysis methods for synthesizing proportions.MethodsWe extracted datasets of proportions from the Cochrane Library and applied 12 two-step and one-step methods to each dataset. We used Spearman's ρ and the Bland-Altman plot to assess their results' correlation and agreement. The GLMM with the logit link was chosen as the reference method. We calculated the absolute difference and fold change (ratio of estimates) of the overall proportion estimates produced by each method vs. the reference method.ResultsWe obtained a total of 43,644 datasets. The various methods generally had high correlations (ρ > 0.9) and agreements. GLMMs had computational issues more frequently than the two-step methods. However, the two-step methods generally produced large absolute differences from the GLMM with the logit link for small total sample sizes (< 50) and crude event rates within 10-20% and 90-95%, and large fold changes for small total event counts (< 10) and low crude event rates (< 20%).ConclusionsAlthough different methods produced similar overall proportion estimates in most datasets, one-step methods should be considered in the presence of small total event counts or sample sizes and very low or high event rates.
| S-EPMC8432281 | biostudies-literature