Influence of trial sample size on treatment effect estimates: meta-epidemiological study

Objective To assess the influence of trial sample size on treatment effect estimates within meta-analyses. Design Meta-epidemiological study. Data sources 93 meta-analyses (735 randomised controlled trials) assessing therapeutic interventions with binary outcomes, published in the 10 leading journals of each medical subject category of the Journal Citation Reports or in the Cochrane Database of Systematic Reviews. Data extraction Sample size, outcome data, and risk of bias extracted from each trial. Data synthesis Trials within each meta-analysis were sorted by their sample size: using quarters within each meta-analysis (from quarter 1 with 25% of the smallest trials, to quarter 4 with 25% of the largest trials), and using size groups across meta-analyses (ranging from <50 to ≥1000 patients). Treatment effects were compared within each meta-analysis between quarters or between size groups by average ratios of odds ratios (where a ratio of odds ratios less than 1 indicates larger effects in smaller trials). Results Treatment effect estimates were significantly larger in smaller trials, regardless of sample size. Compared with quarter 4 (which included the largest trials), treatment effects were, on average, 32% larger in trials in quarter 1 (which included the smallest trials; ratio of odds ratios 0.68, 95% confidence interval 0.57 to 0.82), 17% larger in trials in quarter 2 (0.83, 0.75 to 0.91), and 12% larger in trials in quarter 3 (0.88, 0.82 to 0.95). Similar results were obtained when comparing treatment effect estimates between different size groups. Compared with trials of 1000 patients or more, treatment effects were, on average, 48% larger in trials with fewer than 50 patients (0.52, 0.41 to 0.66) and 10% larger in trials with 500-999 patients (0.90, 0.82 to 1.00). Conclusions Treatment effect estimates differed within meta-analyses solely based on trial sample size, with stronger effect estimates seen in small to moderately sized trials than in the largest trials

Comparison of Treatment Effect Estimates for Pharmacological Randomized Controlled Trials Enrolling Older Adults Only and Those including Adults: A Meta-Epidemiological Study

Context Older adults are underrepresented in clinical research. To assess therapeutic efficacy in older patients, some randomized controlled trials (RCTs) include older adults only. Objective To compare treatment effects between RCTs including older adults only (elderly RCTs) and RCTs including all adults (adult RCTs) by a meta-epidemiological approach. Methods All systematic reviews published in the Cochrane Library (Issue 4, 2011) were screened. Eligible studies were meta-analyses of binary outcomes of pharmacologic treatment including at least one elderly RCT and at least one adult RCT. For each meta-analysis, we compared summary odds ratios for elderly RCTs and adult RCTs by calculating a ratio of odds ratios (ROR). A summary ROR was estimated across all meta-analyses. Results We selected 55 meta-analyses including 524 RCTs (17% elderly RCTs). The treatment effects differed beyond that expected by chance for 7 (13%) meta-analyses, showing more favourable treatment effects in elderly RCTs in 5 cases and in adult RCTs in 2 cases. The summary ROR was 0.91 (95% CI, 0.77–1.08, p = 0.28), with substantial heterogeneity (I2 = 51% and τ2 = 0.14). Sensitivity and subgroup analyses by type-of-age RCT (elderly RCTs vs RCTs excluding older adults and vs RCTs of mixed-age adults), type of outcome (mortality or other) and type of comparator (placebo or active drug) yielded similar results. Conclusions The efficacy of pharmacologic treatments did not significantly differ, on average, between RCTs including older adults only and RCTs of all adults. However, clinically important discrepancies may occur and should be considered when generalizing evidence from all adults to older adults

Meta-Analysis of a Complex Network of Non-Pharmacological Interventions: The Example of Femoral Neck Fracture

Background Surgical interventions raise specific methodological issues in network meta-analysis (NMA). They are usually multi-component interventions resulting in complex networks of randomized controlled trials (RCTs), with multiple groups and sparse connections. Purpose To illustrate the applicability of the NMA in a complex network of surgical interventions and to prioritize the available interventions according to a clinically relevant outcome. Methods We considered RCTs of treatments for femoral neck fracture in adults. We searched CENTRAL, MEDLINE, EMBASE and ClinicalTrials.gov up to November 2015. Two reviewers independently selected trials, extracted data and used the Cochrane Collaboration’s tool for assessing the risk of bias. A group of orthopedic surgeons grouped similar but not identical interventions under the same node. We synthesized the network using a Bayesian network meta-analysis model. We derived posterior odds ratios (ORs) and 95% credible intervals (95% CrIs) for all possible pairwise comparisons. The primary outcome was all-cause revision surgery. Results Data from 27 trials were combined, for 4,186 participants (72% women, mean age 80 years, 95% displaced fractures). The median follow-up was 2 years. With hemiarthroplasty (HA) and total hip arthroplasty (THA) as a comparison, risk of surgical revision was significantly higher with the treatments unthreaded cervical osteosynthesis (OR 8.0 [95% CrI 3.6–15.5] and 5.9 [2.4–12.0], respectively), screw (9.4 [6.0–16.5] and 6.7 [3.9–13.6]) and plate (12.5 [5.8–23.8] and 7.8 [3.8–19.4]). Conclusions In older women with displaced femoral neck fractures, arthroplasty (HA and THA) is the most effective treatment in terms of risk of revision surgery

A test for reporting bias in trial networks: simulation and case studies

Networks of trials assessing several treatment options available for the same condition are increasingly considered. Randomized trial evidence may be missing because of reporting bias. We propose a test for reporting bias in trial networks. We test whether there is an excess of trials with statistically significant results across a network of trials. The observed number of trials with nominally statistically significant p-values across the network is compared with the expected number. The performance of the test (type I error rate and power) was assessed using simulation studies under different scenarios of selective reporting bias. Examples are provided for networks of antidepressant and antipsychotic trials, where reporting biases have been previously demonstrated by comparing published to Food and Drug Administration (FDA) data. In simulations, the test maintained the type I error rate and was moderately powerful after adjustment for type I error rate, except when the between-trial variance was substantial. In all, a positive test result increased moderately or markedly the probability of reporting bias being present, while a negative test result was not very informative. In the two examples, the test gave a signal for an excess of statistically significant results in the network of published data but not in the network of FDA data. The test could be useful to document an excess of significant findings in trial networks, providing a signal for potential publication bias or other selective analysis and outcome reporting biases

Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries

Includes details on the implementation of MetaMap and IntraMap, prioritization rules, the test set of clinical trials and the classification of the external test set according to the 171 GBD categories. Dataset S1: Expert-based enrichment database for the classification according to the 28 GBD categories. Manual classification of 503 UMLS concepts that could not be mapped to any of the 28 GBD categories. Dataset S2: Expert-based enrichment database for the classification according to the 171 GBD categories. Manual classification of 655 UMLS concepts that could not be mapped to any of the 171 GBD categories, among which 108 could be projected to candidate GBD categories. Table S1: Excluded residual GBD categories for the grouping of the GBD cause list in 171 GBD categories. A grouping of 193 GBD categories was defined during the GBD 2010 study to inform policy makers about the main health problems per country. From these 193 GBD categories, we excluded the 22 residual categories listed in the Table. We developed a classifier for the remaining 171 GBD categories. Among these residual categories, the unique excluded categories in the grouping of 28 GBD categories were “Other infectious diseases” and “Other endocrine, nutritional, blood, and immune disorders”. Table S2: Per-category evaluation of performance of the classifier for the 171 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities, specificities (in %) and likelihood ratios for each of the 171 GBD categories plus the “No GBD” category for the classifier using the Word Sense Disambiguation server, the expert-based enrichment database and the priority to the health condition field. Table S3: Performance of the 8 versions of the classifier for the 171 GBD categories. Exact-matching and weighted averaged sensitivities and specificities for 8 versions of the classifier for the 171 GBD categories. Exact-matching corresponds to the proportion (in %) of trials for which the automatic GBD classification is correct. Exact-matching was estimated over all trials (N = 2,763), trials concerning a unique GBD category (N = 2,092), trials concerning 2 or more GBD categories (N = 187), and trials not relevant for the GBD (N = 484). The weighted averaged sensitivity and specificity corresponds to the weighted average across GBD categories of the sensitivities and specificities for each GBD category plus the “No GBD” category (in %). The 8 versions correspond to the combinations of the use or not of the Word Sense Disambiguation server during the text annotation, the expert-based enrichment database, and the priority to the health condition field as a prioritization rule. Table S4: Per-category evaluation of the performance of the baseline for the 28 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities and specificities (in %) of the 28 GBD categories plus the “No GBD” category for the classification of clinical trial records towards GBD categories without using the UMLS knowledge source but based on the recognition in free text of the names of diseases defining in each GBD category only. For the baseline a clinical trial records was classified with a GBD category if at least one of the 291 disease names from the GBD cause list defining that GBD category appeared verbatim in the condition field, the public or scientific titles, separately, or in at least one of these three text fields. (DOCX 84 kb

Impact of single centre status on estimates of intervention effects in trials with continuous outcomes: meta-epidemiological study

Objective To compare estimates of intervention effects between single centre and multicentre randomised controlled trials with continuous outcomes

Newly diagnosed atrial fibrillation and hospital utilization in heart failure:a nationwide cohort study

AIMS: Atrial fibrillation (AF) constitutes a major burden to health services, but the importance of incident AF in patients with heart failure (HF) is unclear. We examined the associations between incident AF and hospital utilization in patients with HF. METHODS AND RESULTS: In a nationwide matched‐cohort study of HF patients, we identified patients diagnosed with incident AF between 2008 and 2018 in the Danish Heart Failure Registry (N = 4463), and we compared them to matched referents without AF (N = 17 802). Incident AF was associated with a multivariable‐adjusted 4.8‐fold increase (95% CI 4.1–5.6) and 4.3‐fold increase (95% CI 3.9–4.8) in the cumulative incidence of inpatient and outpatient contacts within 30 days, respectively. At 1 year, the cumulative incidence ratios were 1.8 (95% CI 1.7–1.9) and 1.4 (95% CI 1.4–1.5). Incident AF was also associated with increases in the total numbers of inpatient and outpatient hospital contacts within 30 days (multivariable‐adjusted rate ratio 1.4, 95% CI 1.4–1.5, and 1.6, 95% CI 1.6–1.7, respectively). At 1 year, the ratios were 2.2 (95% CI 2.1–2.3) and 2.0 (95% CI 1.9–2.1). The multivariable‐adjusted proportion of bed‐day use among HF patients with incident AF was 10.9‐fold (95% CI 9.3–12.9) higher at 30 days and 5.3‐fold (95% CI 4.3–6.4) higher at 1 year compared with AF‐free referents. CONCLUSIONS: Incident AF in HF is associated with earlier hospital contact, more hospital contacts, and more hospital bed‐days. More evidence on interventions that may prevent the risk and subsequent burden of AF in HF is urgently needed

Electrical energy by electrode placement for cardioversion of atrial fibrillation: a systematic review and meta-analysis

OBJECTIVE: Electrode patch position may not be critical for success when cardioverting atrial fibrillation (AF), but the relevance of applied electrical energy is unclarified. Our objective was to perform a meta-analysis of randomised trials to examine the dose-response relation between energy level and cardioversion success by electrode position in elective cardioversion.METHODS: We searched PubMed, Embase, The Cochrane Library, Google Scholar and Scopus Citations. Inclusion criteria were randomised controlled trials using biphasic shock waves and self-adhesive patches, and publication date from 2000 to 2023. We used random-effects dose-response models to meta-analyse the relation between energy level and cardioversion success by anterolateral and anteroposterior position. Random-effects models estimated pooled risk ratios (RR) for cardioversion success after the first and the final shocks between the two electrode positions.RESULTS: We included five randomised controlled trials (N=1078). After the first low-energy shock, the electrode position was not significantly associated with the likelihood of successful cardioversion (pooled RR anterolateral vs anteroposterior placement 1.28, 95% CI 0.93 to 1.76, with considerable heterogeneity). After a high-energy final shock, there was no evidence of an association between the electrode position and the cumulative chance of cardioversion success (pooled RR anterolateral vs anteroposterior 1.05, 95% CI 0.97 to 1.14). Regardless of electrode position, cardioversion success was significantly less likely with shock energy levels &lt; 200J compared with 200J.CONCLUSION: Evidence from contemporary randomised trials suggests that higher level of electrical energy is associated with higher conversion rate when cardioverting AF with a biphasic shockwave. Positioning of electrodes can be based on convenience.</p

Social determinants of health and cardiovascular outcomes in patients with heart failure

BackgroundWe examined the associations between family income and educational attainment with incident atrial fibrillation (AF), myocardial infarction (MI), stroke and cardiovascular (CV) death among patients with newly-diagnosed heart failure (HF).MethodsIn a nationwide Danish registry of HF patients diagnosed between 2008 and 2018, we established a cohort for each outcome. When examining AF, MI and stroke, respectively, patients with a history of these outcomes at diagnosis of HF were excluded. We used cause-specific proportional hazard models to estimate hazard ratios for tertile groups of family income and three levels of educational attainment.ResultsAmong 27,947 AF-free patients, we found no association between income or education and incident AF. Among 27,309 MI-free patients, we found that lower income (hazard ratio 1.28 [95% CI 1.11-1.48] and 1.11 [0.96-1.28] for lower and medium vs. higher income) and education (1.23 [1.04-1.45] and 1.15 [0.97-1.36] for lower and medium vs. higher education) were associated with MI. Among 36,801 stroke-free patients, lower income was associated with stroke (1.38 [1.23-1.56] and 1.27 [1.12-1.44] for lower and medium vs. higher income) but not education. Lower income (1.56 [1.46-1.67] and 1.32 [1.23-1.42] for lower and medium vs. higher income) and education (1.20 [1.11-1.29] and 1.07 [0.99-1.15] for lower and medium vs. higher education) were associated with CV death.ConclusionsIn patients with newly-diagnosed HF, lower family income was associated with higher rates of acute MI, stroke and cardiovascular death. Lower educational attainment was associated with higher rates of acute MI and cardiovascular death. There was no evidence of associations between income and education with incident AF