Large Pediatric Randomized Clinical Trials in ClinicalTrials.gov.

BACKGROUND: Large, randomized controlled trials (RCTs) are essential in answering pivotal questions in child health. METHODS: We created a bird's eye view of all large, noncluster, nonvaccine pediatric RCTs with ≥1000 participants registered in ClinicalTrials.gov (last search January 9, 2020). We analyzed the funding sources, countries, outcomes, publication status, and correlation with the pediatric global burden of disease (GBD) for eligible trials. RESULTS: We identified 247 large, nonvaccine, noncluster pediatric RCTs. Only 17 mega-trials with ≥5000 participants existed. Industry funding was involved in only 52 (21%) and exclusively funded 47 (19%) trials. Participants were from high-income countries (HICs) in 100 (40%) trials, from lower-middle-income countries (LMICs) in 122 (49%) trials, and from both HICs and LMICs in 19 (8%) trials; 6 trials did not report participants' country location. Of trials conducted in LMIC, 43% of investigators were from HICs. Of non-LMIC participants trials (HIC or HIC and LMIC), 39% were multicountry trials versus 11% of exclusively LMIC participants trials. Few trials (18%; 44 of 247) targeted mortality as an outcome. 35% (58 of 164) of the trials completed ≥12 months were unpublished at the time of our assessment. The number of trials per disease category correlated well with pediatric GBD overall (ρ = 0.76) and in LMICs (ρ = 0.69), but not in HICs (ρ = 0.29). CONCLUSIONS: Incentivization of investigator collaborations across diverse country settings, timely publication of results of large pediatric RCTs, and alignment with the pediatric GBD are of pivotal importance to ultimately improve child health globally.

P value and Bayesian analysis in randomized-controlled trials in child health research published over 10 years, 2007 to 2017: a methodological review protocol.

BACKGROUND: There is an unresolved debate about the reliability of the interpretation of P value. Some investigators have suggested that an alternative Bayesian method is preferred in conducting health research. As randomized-controlled trials (RCTs) are important in generating research evidence, we decided to investigate the extent, if any, the inferential statistical framework in published RCTs in child health research have changed over 10 years. We aim to examine the change in P value and Bayesian analysis in RCTs in child health research papers published from 2007 to 2017. METHODS: We will search the Cochrane Central Register of Controlled Trials (Wiley) to identify relevant citations. We will leverage a pre-existing sample of child health RCTs published in 2007 (n=300) used in our previous study of reporting quality of pediatric RCTs. Using the same strategy and study selection methods, we will identify a comparable random sample of child health RCTs published in 2017 (n=300). Eligible studies will include RCTs in health research among individuals aged 21 years and below. One reviewer will select studies for inclusion and extract the data and another reviewer will verify these. Disagreements will be resolved by a discussion between reviewers or by involving another reviewer. We will perform a descriptive analysis of 2007 and 2017 samples and analyze the results using both the frequentist and Bayesian methods. We will present specific characteristics of the clinical trials from 2007 and 2017 in tabular and graphical forms. We will report the difference in the proportion of P value and Bayesian analysis between 2007 and 2017 to assess the 10-year change. Clustering around P values of significance, if observed, will be reported. DISCUSSION: This review will present the difference in the proportion of trials that reported on P value and Bayesian analysis between 2007 and 2017 to assess the 10-year change. The implications for future clinical research will be discussed and this research work will be published in a peer-reviewed journal. This review has the potential to help inform the need for a change in the methodological approach from the null hypothesis significance test to Bayesian methods. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework https://osf.io/aj2df.

Assessment of transparency indicators across the biomedical literature: How open is open?

Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify 5 indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures, and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central (PMC). Our results indicate remarkable improvements in some (e.g., conflict of interest [COI] disclosures and funding disclosures), but not other (e.g., protocol registration and code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals, and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand, and promote transparency and reproducibility in science.

Point-of-care testing for Toxoplasma gondii IgG/IgM using Toxoplasma ICT IgG-IgM test with sera from the United States and implications for developing countries.

BACKGROUND: Congenital toxoplasmosis is a serious but preventable and treatable disease. Gestational screening facilitates early detection and treatment of primary acquisition. Thus, fetal infection can be promptly diagnosed and treated and outcomes can be improved. METHODS: We tested 180 sera with the Toxoplasma ICT IgG-IgM point-of-care (POC) test. Sera were from 116 chronically infected persons (48 serotype II; 14 serotype I-III; 25 serotype I-IIIa; 28 serotype Atypical, haplogroup 12; 1 not typed). These represent strains of parasites infecting mothers of congenitally infected children in the U.S. 51 seronegative samples and 13 samples from recently infected persons known to be IgG/IgM positive within the prior 2.7 months also were tested. Interpretation was confirmed by two blinded observers. A comparison of costs for POC vs. commercial laboratory testing methods was performed. RESULTS: We found that this new Toxoplasma ICT IgG-IgM POC test was highly sensitive (100%) and specific (100%) for distinguishing IgG/IgM-positive from negative sera. Use of such reliable POC tests can be cost-saving and benefit patients. CONCLUSIONS: Our work demonstrates that the Toxoplasma ICT IgG-IgM test can function reliably as a point-of-care test to diagnose Toxoplasma gondii infection in the U.S. This provides an opportunity to improve maternal-fetal care by using approaches, diagnostic tools, and medicines already available. This infection has serious, lifelong consequences for infected persons and their families. From the present study, it appears a simple, low-cost POC test is now available to help prevent morbidity/disability, decrease cost, and make gestational screening feasible. It also offers new options for improved prenatal care in low- and middle-income countries.

Impact of vaccine herd-protection effects in cost-effectiveness analyses of childhood vaccinations. A quantitative comparative analysis.

BACKGROUND: Inclusion of vaccine herd-protection effects in cost-effectiveness analyses (CEAs) can impact the CEAs-conclusions. However, empirical epidemiologic data on the size of herd-protection effects from original studies are limited. METHODS: We performed a quantitative comparative analysis of the impact of herd-protection effects in CEAs for four childhood vaccinations (pneumococcal, meningococcal, rotavirus and influenza). We considered CEAs reporting incremental-cost-effectiveness-ratios (ICERs) (per quality-adjusted-life-years [QALY] gained; per life-years [LY] gained or per disability-adjusted-life-years [DALY] avoided), both with and without herd protection, while keeping all other model parameters stable. We calculated the size of the ICER-differences without vs with-herd-protection and estimated how often inclusion of herd-protection led to crossing of the cost-effectiveness threshold (of an assumed societal-willingness-to-pay) of $50,000 for more-developed countries or X3GDP/capita (WHO-threshold) for less-developed countries. RESULTS: We identified 35 CEA studies (20 pneumococcal, 4 meningococcal, 8 rotavirus and 3 influenza vaccines) with 99 ICER-analyses (55 per-QALY, 27 per-LY and 17 per-DALY). The median ICER-absolute differences per QALY, LY and DALY (without minus with herd-protection) were $15,620 (IQR: $877 to $48,376); $54,871 (IQR: $787 to $115,026) and $49 (IQR: $15 to $1,636) respectively. When the target-vaccination strategy was not cost-saving without herd-protection, inclusion of herd-protection always resulted in more favorable results. In CEAs that had ICERs above the cost-effectiveness threshold without herd-protection, inclusion of herd-protection led to crossing of that threshold in 45% of the cases. This impacted only CEAs for more developed countries, as all but one CEAs for less developed countries had ICERs below the WHO-cost-effectiveness threshold even without herd-protection. In several analyses, recommendation for the adoption of the target vaccination strategy depended on the inclusion of the herd protection effect. CONCLUSIONS: Inclusion of herd-protection effects in CEAs had a substantial impact in the estimated ICERs and made target-vaccination strategies more attractive options in almost half of the cases where ICERs were above the societal-willingness to pay threshold without herd-protection. More empirical epidemiologic data are needed to determine the size of herd-protection effects across diverse settings and also the size of negative vaccine effects, e.g. from serotype substitution.

Patient safety strategies targeted at diagnostic errors: a systematic review.

Missed, delayed, or incorrect diagnosis can lead to inappropriate patient care, poor patient outcomes, and increased cost. This systematic review analyzed evaluations of interventions to prevent diagnostic errors. Searches used MEDLINE (1966 to October 2012), the Agency for Healthcare Research and Quality's Patient Safety Network, bibliographies, and prior systematic reviews. Studies that evaluated any intervention to decrease diagnostic errors in any clinical setting and with any study design were eligible, provided that they addressed a patient-related outcome. Two independent reviewers extracted study data and rated study quality. There were 109 studies that addressed 1 or more intervention categories: personnel changes (n = 6), educational interventions (n = 11), technique (n = 23), structured process changes (n = 27), technology-based systems interventions (n = 32), and review methods (n = 38). Of 14 randomized trials, which were rated as having mostly low to moderate risk of bias, 11 reported interventions that reduced diagnostic errors. Evidence seemed strongest for technology-based systems (for example, text message alerting) and specific techniques (for example, testing equipment adaptations). Studies provided no information on harms, cost, or contextual application of interventions. Overall, the review showed a growing field of diagnostic error research and categorized and identified promising interventions that warrant evaluation in large studies across diverse settings.

Comparative effect sizes in randomised trials from less developed and more developed countries: meta-epidemiological assessment.

OBJECTIVE: To compare treatment effects from randomised trials conducted in more developed versus less developed countries. DESIGN: Meta-epidemiological study. DATA SOURCES: Cochrane Database of Systematic Reviews (August 2012). DATA EXTRACTION: Meta-analyses with mortality outcomes including data from at least one randomised trial conducted in a less developed country and one in a more developed country. Relative risk estimates of more versus less developed countries were compared by calculating the relative relative risks for each topic and the summary relative relative risks across all topics. Similar analyses were performed for the primary binary outcome of each topic. RESULTS: 139 meta-analyses with mortality outcomes were eligible. No nominally significant differences between more developed and less developed countries were found for 128 (92%) meta-analyses. However, differences were beyond chance in 11 (8%) cases, always showing more favourable treatment effects in trials from less developed countries. The summary relative relative risk was 1.12 (95% confidence interval 1.06 to 1.18; P<0.001; I(2)=0%), suggesting significantly more favourable mortality effects in trials from less developed countries. Results were similar for meta-analyses with nominally significant treatment effects for mortality (1.15), meta-analyses with recent trials (1.14), and when excluding trials from less developed countries that subsequently became more developed (1.12). For the primary binary outcomes (127 meta-analyses), 20 topics had differences in treatment effects beyond chance (more favourable in less developed countries in 15/20 cases). CONCLUSIONS: Trials from less developed countries in a few cases show significantly more favourable treatment effects than trials in more developed countries and, on average, treatment effects are more favourable in less developed countries. These discrepancies may reflect biases in reporting or study design as well as genuine differences in baseline risk or treatment implementation and should be considers when generalising evidence across different settings.

Translation of highly promising basic science research into clinical applications.

PURPOSE: To evaluate the predictors of and time taken for the translation of highly promising basic research into clinical experimentation and use. METHODS: We identified 101 articles, published between 1979 and 1983 in six major basic science journals, which clearly stated that the technology studied had novel therapeutic or preventive promises. Each case was evaluated for whether the promising finding resulted in relevant randomized controlled trials and clinical use. Main outcomes included the time to published trials, time to published trials with favorable results ("positive" trials), and licensed clinical use. RESULTS: By October 2002, 27 of the promising technologies had resulted in at least one published randomized trial, 19 of which had led to the publication of at least one positive randomized trial. Five basic science findings are currently licensed for clinical use, but only has been used extensively for the licensed indications. Promising technologies that did not lead to a published human study within 10 to 12 years were unlikely to be tested in humans subsequently. Some form of industry involvement in the basic science publication was the strongest predictor of clinical experimentation, accelerating the process by about eightfold (95% confidence interval: 3 to 19) when an author had industry affiliations. CONCLUSION: Even the most promising findings of basic research take a long time to translate into clinical experimentation, and adoption in clinical practice is rare.

Genetic associations in large versus small studies: an empirical assessment.

BACKGROUND: Advances in human genetics could help us to assess prognosis on an individual basis and to optimise the management of complex diseases. However, different studies on the same genetic association sometimes have discrepant results. Our aim was to assess how often large studies arrive at different conclusions than smaller studies, and whether this situation arises more frequently when findings of first published studies disagree with those of subsequent research. METHODS: We examined the results of 55 meta-analyses (579 study comparisons) of genetic associations and tested whether the magnitude of the genetic effect differs in large versus smaller studies. FINDINGS: We noted significant between-study heterogeneity in 26 (47%) meta-analyses. The magnitude of the genetic effect differed significantly in large versus smaller studies in ten (18%), 20 (36%), and 21 (38%) meta-analyses with tests of rank correlation, regression on SE, and regression on inverse of variance, respectively. The largest studies generally yielded more conservative results than the complete meta-analyses, which included all studies (p=0.005). In 14 (26%) meta-analyses the proposed association was significantly stronger in the first studies than in subsequent research. Only in nine (16%) meta-analyses was the genetic association significant and replicated without hints of heterogeneity or bias. There was little concordance in first versus subsequent discrepancies, and large versus small discrepancies. INTERPRETATION: Genuine heterogeneity and bias could affect the results of genetic association studies. Genetic risk factors for complex diseases should be assessed cautiously and, if possible, using large scale evidence.