68 research outputs found

    Use of external evidence for design and Bayesian analysis of clinical trials:a qualitative study of trialists’ views

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    Abstract Background Evidence from previous studies is often used relatively informally in the design of clinical trials: for example, a systematic review to indicate whether a gap in the current evidence base justifies a new trial. External evidence can be used more formally in both trial design and analysis, by explicitly incorporating a synthesis of it in a Bayesian framework. However, it is unclear how common this is in practice or the extent to which it is considered controversial. In this qualitative study, we explored attitudes towards, and experiences of, trialists in incorporating synthesised external evidence through the Bayesian design or analysis of a trial. Methods Semi-structured interviews were conducted with 16 trialists: 13 statisticians and three clinicians. Participants were recruited across several universities and trials units in the United Kingdom using snowball and purposeful sampling. Data were analysed using thematic analysis and techniques of constant comparison. Results Trialists used existing evidence in many ways in trial design, for example, to justify a gap in the evidence base and inform parameters in sample size calculations. However, no one in our sample reported using such evidence in a Bayesian framework. Participants tended to equate Bayesian analysis with the incorporation of prior information on the intervention effect and were less aware of the potential to incorporate data on other parameters. When introduced to the concepts, many trialists felt they could be making more use of existing data to inform the design and analysis of a trial in particular scenarios. For example, some felt existing data could be used more formally to inform background adverse event rates, rather than relying on clinical opinion as to whether there are potential safety concerns. However, several barriers to implementing these methods in practice were identified, including concerns about the relevance of external data, acceptability of Bayesian methods, lack of confidence in Bayesian methods and software, and practical issues, such as difficulties accessing relevant data. Conclusions Despite trialists recognising that more formal use of external evidence could be advantageous over current approaches in some areas and useful as sensitivity analyses, there are still barriers to such use in practice

    How Often Do Safety Signals Occur by Chance in First-in-Human Trials?

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    Clinicians working on first-in-human clinical studies need to be able to judge whether safety signals observed on an investigational drug were more likely to have occurred by chance or to have been caused by the drug. We retrospectively reviewed 84 Novartis studies including 1234 healthy volunteers receiving placebo, to determine the expected incidence of changes in commonly measured laboratory parameters and vital signs, in the absence of any active agent. We calculated the frequency of random incidence of safety signals, focusing on the liver, cardiovascular system, kidney and pancreas. Using the liver enzyme alanine aminotransferase (ALT) as an example, we illustrate how a predictive model can be used to determine the probability of a given subject to experience an elevation of ALT above the upper limit of the normal range under placebo, conditional on the characteristics of this subject and the study

    Exploring the impact of selection bias in observational studies of COVID-19: a simulation study

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    BACKGROUND: Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis. METHODS: We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. RESULTS: In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios. CONCLUSIONS: Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest

    Impact of blinding on estimated treatment effects in randomised clinical trials:meta-epidemiological study

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    International audienceAbstract Objectives To study the impact of blinding on estimated treatment effects, and their variation between trials; differentiating between blinding of patients, healthcare providers, and observers; detection bias and performance bias; and types of outcome (the MetaBLIND study). Design Meta-epidemiological study. Data source Cochrane Database of Systematic Reviews (2013-14). Eligibility criteria for selecting studies Meta-analyses with both blinded and non-blinded trials on any topic. Review methods Blinding status was retrieved from trial publications and authors, and results retrieved automatically from the Cochrane Database of Systematic Reviews. Bayesian hierarchical models estimated the average ratio of odds ratios (ROR), and estimated the increases in heterogeneity between trials, for non-blinded trials (or of unclear status) versus blinded trials. Secondary analyses adjusted for adequacy of concealment of allocation, attrition, and trial size, and explored the association between outcome subjectivity (high, moderate, low) and average bias. An ROR lower than 1 indicated exaggerated effect estimates in trials without blinding. Results The study included 142 meta-analyses (1153 trials). The ROR for lack of blinding of patients was 0.91 (95% credible interval 0.61 to 1.34) in 18 meta-analyses with patient reported outcomes, and 0.98 (0.69 to 1.39) in 14 meta-analyses with outcomes reported by blinded observers. The ROR for lack of blinding of healthcare providers was 1.01 (0.84 to 1.19) in 29 meta-analyses with healthcare provider decision outcomes (eg, readmissions), and 0.97 (0.64 to 1.45) in 13 meta-analyses with outcomes reported by blinded patients or observers. The ROR for lack of blinding of observers was 1.01 (0.86 to 1.18) in 46 meta-analyses with subjective observer reported outcomes, with no clear impact of degree of subjectivity. Information was insufficient to determine whether lack of blinding was associated with increased heterogeneity between trials. The ROR for trials not reported as double blind versus those that were double blind was 1.02 (0.90 to 1.13) in 74 meta-analyses. Conclusion No evidence was found for an average difference in estimated treatment effect between trials with and without blinded patients, healthcare providers, or outcome assessors. These results could reflect that blinding is less important than often believed or meta-epidemiological study limitations, such as residual confounding or imprecision. At this stage, replication of this study is suggested and blinding should remain a methodological safeguard in trials

    Ethnic discordance in serum anti-Müllerian hormone in healthy women: a population study from China and Europe

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    Research question: Chinese women are known to have an earlier age of natural menopause than their European counterparts, but whether they also have a lower functional ovarian reserve is unknown. This study was designed to assess whether there are ethnic differences in anti-Müllerian hormone (AMH) concentrations in women of reproductive age. Design: Women in China and Europe with regular menstrual cycles, not on hormonal contraception and with no medical history of note, were recruited to provide a day 2–5 early follicular phase sample. AMH concentration was determined using the Roche Elecsys assay. Decline in AMH was modelled with linear, quadratic and quadratic with interaction on age equations to assess the impact of ethnicity. Results: A total of 887 European and 461 Chinese women participated in the study. Despite the Chinese population being slightly younger (34.1 ± 8.4 years) than their European counterparts (34.8±8.9 years), their median AMH was lower, at 1.87 ng/ml (interquartile range [IQR] 0.28–3.64) compared with 2.11 ng/ml (IQR 0.73–3.96), with evidence of increasing discordance from age 25 years. In all regression models of the age-related decline in AMH, there was evidence of a difference between Chinese and European women. Although AMH was 28.1% (95% confidence interval [CI] 18.2–36.7%) lower in the Chinese population at age 30, this decline increased to 79.4% (95% CI 75.4– 82.9%) at age 45. Conclusions: There were independent effects of age and ethnicity on serum AMH concentrations, with Chinese women having a substantially lower AMH in adult life than their European counterparts from age 25 onwards
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