Applications of simple and accessible methods for meta-analysis involving rare events: A simulation study

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

Assessments of harms in clinical trials

Introduction and Aims Healthcare interventions are usually associated with a risk of harmful events that must be balanced against the potential favorable outcomes. However reliable evidence on harms for interventions is often inadequate, and hampered by the many challenges that stem from the reporting, analysis and interpretation of harms data in clinical trials. This thesis addresses some of these issues. Methods Reporting of harms data is assessed in a systematic review of reviews and a case study investigating the additional value of harms data reported in clinical study reports (CSRs). A framework for searching and identifying relevant sources of harms data is outlined, and then explored further in a survey assessing current practices in clinical trial units (CTUs). Signal detection methods are introduced, and evaluated using simulated data to assess their performance when detecting safety signals in CTU databases. Results The systematic review highlights that the reporting of harms in RCTs is inconsistent, and often inadequate. In the case study, CSRs presented data on harms, including SAEs which are not reported or mentioned in publications, they also provide more detail about the design, conduct and analysis of the trial which facilitate the assessment of risk of bias in evidence synthesis. A wide range of sources for harms data have been identified, each with distinct strengths and limitations discussed. Selection of appropriate sources depends on the research question, and whether a hypothesis generating or hypothesis testing approach should be taken. Relevant sources have been identified for each approach, with examples of their exploitation in CTUs evaluated in the survey. The simulation study has shown that some of the current available signal detection methods are not able to control the false discovery rate well, and are only able to detect few safety signals for small or sparse data. Conclusions The work carried out within this thesis provides some recommendations to address the reporting, conduct, and analysis of harms in clinical trials. Wider adoption of recommendations made by the CONSORT-harms guideline will enhance the quality of reporting and improve subsequent evidence synthesis. Recent initiatives to promote open access to clinical trials data including CSRs is a major step towards supporting better data transparency. It is important to identify and consider different sources that are most likely to yield robust data on harms of interest, rather than relying on studies that cannot reliably detect harm. The survey identified published literature and systematic reviews as the most common source being used in the trial safety monitoring within CTUs. Signal detection methods are potentially unsuitable for use in CTUs. Further tools and guidelines for enhanced signal detection are needed in clinical trials

Applications of simple and accessible methods for meta-analysis involving rare events: A simulation study

From SAGE Publishing via Jisc Publications RouterHistory: epub 2021-06-17Publication status: PublishedMeta-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

Global burden of preventable medication-related harm in health care: a systematic review

A series of WHO initiatives, such as the Global Patient Safety Challenge: Medication Without Harm and the Global Patient Safety Action Plan 2021-2030, address patient harm associated with use of medications. Medication-related harm is considered preventable if it occurs as a result of an identifiable,modifiable cause and its recurrence can be avoided by appropriate adaptation to a process or adherence to guidelines. Understanding the prevalence, natureand severity of preventable medication-related harm is critical for setting targets for clinically relevant, implementable improvements in patient safety. This report presents an updated systematic review and meta-analysis of studies of the prevalence, nature and severity of preventable medication-related harm in theinternational literature including in low- and middle-income countries (LMICs). A total of 100 studies were included in the review, involving 487 162 patients. Ofthese reports, 70 were from high-income countries (HICs) and 30 from LMICs. The results were as follows. Global prevalence and severity of preventablemedication-related harm: The pooled prevalence of preventable medication-related harm in all 100 studies was 5% (1 in 20 patients). One fourth of the harm was severe or potentially life-threatening. Geographical distribution of preventable medication-related harm: The prevalence of preventable medication-related harm was 7% in 30 studies in LMICs and 4% (3–5%, one in 25 patients) in 70 studies in HICs. The highest prevalence rates of preventable medication-related harm were in the African (9%) and South-East Asian regions (9%). Health care settings in which the most vulnerable patients are managed for preventable medication-related harm: Globally, the highest prevalence ratesfor preventable medication-related harm are for patients managed in geriatric care units (17%) and among patients in highly specialized or surgical care (9%).Stages of medication at which most preventable medication-related harm occurs: Globally about half (53%) of all preventable medication-related harmoccurred at the “ordering/prescribing” stage and 36% at the monitoring/reporting” stage. In LMICs, almost 80% of preventable medication-related harm occurred during the “ordering/prescribing” stage. Medicines that contribute most to medication- related harm: Antibacterials, antipsychotics,cardiovascular medications, drugs for functional gastrointestinal disorders, endocrine therapy, hypnotics, sedatives and non-steroidal anti-inflammatory products contributed most to medication-related harm globally.Way forward: The analysis showed that at least one in 20 patients are affected by preventable medication-related harm globally and that more than one fourthof preventable harm is severe or life-threatening. The prevalence of preventable medication-related harm in LMICs was almost twice as high as in HICs;however, few data were available on the severity and nature of medication-related harm in LMICs. A prerequisite for the success of future strategiesto mitigate preventable medication-related harm in LMICs would be to encourage reporting of any preventable medication-related harm and commission high-quality studies with standard methods for assessing and reporting such harm and also studies of the underlying causes for designing interventions that are most likely to work in LMICs. There is also an urgent need to implement improvement strategies in settings in which patients are managed, especially those who are vulnerable to preventable medication related harm, such as geriatric care and surgical care settings. Finally, most of the evidence summarized in this report was produced in hospitals and should be strengthened with more research in major specialties, including primary care, and mental health