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For the purpose of comparing the efficacy and safety of a Similar Biotherapeutic Product (SBP) to a Reference Biotherapeutic Product (RBP), the " Guidelines on Evaluation of Similar Biotherapeutic Products (SBPs)" issued by the World Health Organisation (WHO), states that equivalence or non-inferiority studies may be acceptable. While in principle, equivalence trials are preferred, non-inferiority trials may be considered if appropriately justified, such as for a medicinal product with a wide safety margin. However, the statistical issues involved in the design, conduct, analysis and interpretation of equivalence and non-inferiority trials are complex and subtle, and require that all aspects of these trials be given careful consideration. These issues are important in order to ensure that equivalence and non-inferiority trials provide valid data that are necessary to draw reliable conclusions regarding the clinical similarity of an SBP to an RBP. © 2011. Source

Pullenayegum E.M.,McMaster University | Pullenayegum E.M.,Biostatistics Unit
Statistics in Medicine | Year: 2011

It is well known that when a Bayesian meta-analysis includes a small number of studies, inference can be sensitive to the choice of prior for the between-study variance. Choosing a vague prior does not solve the problem, as inferences can be substantially different depending on the degree of vagueness. Moreover, because the data provide little information on between-study heterogeneity, posterior inferences for the between-study variance based on vague priors will tend to be unrealistic. It is thus preferable to adopt a reasonable, informed prior for the between-study variance. However, relatively little is known about what constitutes a realistic distribution. On the basis of data from the Cochrane Database of Systematic Reviews, this paper describes the distribution of between-study variance in published meta-analyses, and proposes some realistic, informed priors for use in meta-analyses of binary outcomes. It is hoped that these priors will improve the calibration of inferences from Bayesian meta-analyses. © 2011 John Wiley & Sons, Ltd. Source

Thorlund K.,McMaster University | Awad T.,McMaster University | Thabane L.,McMaster University | Thabane L.,Biostatistics Unit
BMC Infectious Diseases | Year: 2011

Background: Antivirals play a critical role in the prevention and the management of influenza. One class of antivirals, neuraminidase inhibitors (NAIs), is effective against all human influenza viruses. Currently there are two NAI drugs which are licensed worldwide: oseltamivir (Tamiflu®) and zanamivir (Relenza®); and two drugs which have received recent approval in Japan: peramivir and laninamivir. Until recently, the prevalence of antiviral resistance has been relatively low. However, almost all seasonal H1N1 strains that circulated in 2008-09 were resistant to oseltamivir whereas about 1% of tested 2009 pandemic H1N1 viruses were found to be resistant to oseltamivir. To date, no studies have demonstrated widespread resistance to zanamivir. It seems likely that the literature on antiviral resistance associated with oseltamivir as well as zanamivir is now sufficiently comprehensive to warrant a systematic review.The primary objectives were to systematically review the literature to determine the incidence of resistance to oseltamivir, zanamivir, and peramivir in different population groups as well as assess the clinical consequences of antiviral resistance.Methods: We searched MEDLINE and EMBASE without language restrictions in September 2010 to identify studies reporting incidence of resistance to oseltamivir, zanamivir, and peramivir. We used forest plots and meta-analysis of incidence of antiviral resistance associated with the three NAIs. Subgroup analyses were done across a number of population groups. Meta-analysis was also performed to evaluate associations between antiviral resistance and clinical complications and symptoms.Results: We identified 19 studies reporting incidence of antiviral resistance. Meta-analysis of 15 studies yielded a pooled incidence rate for oseltamivir resistance of 2.6% (95%CI 0.7% to 5.5%). The incidence rate for all zanamivir resistance studies was 0%. Only one study measured incidence of antiviral resistance among subjects given peramivir and was reported to be 0%. Subgroup analyses detected higher incidence rates among influenza A patients, especially for H1N1 subtype influenza. Considerable heterogeneity between studies precluded definite inferences about subgroup results for immunocompromised patients, in-patients, and children. A meta-analysis of 4 studies reporting association between oseltamivir-resistance and pneumonia yielded a statistically significant risk ratio of 4.2 (95% CI 1.3 to 13.1, p = 0.02). Oseltamivir-resistance was not statistically significantly associated with other clinical complications and symptoms.Conclusion: Our results demonstrate that that a substantial number of patients may become oseltamivir-resistant as a result of oseltamivir use, and that oseltamivir resistance may be significantly associated with pneumonia. In contrast, zanamivir resistance has been rarely reported to date. © 2011 Thorlund et al; licensee BioMed Central Ltd. Source

Agosta F.,Vita-Salute San Raffaele University | Pievani M.,Vita-Salute San Raffaele University | Pievani M.,Irccs Centro San Giovanni Of Dio Fatebenefratelli | Geroldi C.,Irccs Centro San Giovanni Of Dio Fatebenefratelli | And 3 more authors.
Neurobiology of Aging | Year: 2012

Using resting state (RS) functional magnetic resonance imaging (fMRI), the connectivity patterns of the default mode (DMN), frontoparietal, executive, and salience networks were explored in 13 Alzheimer's disease (AD) patients, 12 amnestic mild cognitive impairment (aMCI) patients, and 13 healthy controls. Compared with controls and aMCI, AD was associated with opposing connectivity effects in the DMN (decreased) and frontal networks (enhanced). The only RS abnormality found in aMCI patients compared with controls was a precuneus connectivity reduction in the DMN. RS fMRI group differences were only partly related to gray matter atrophy. In AD patients, the mean executive network connectivity was positively associated with frontal-executive and language neuropsychological scores. These results suggest that AD is associated with an alteration of large-scale functional brain networks, which extends well beyond the DMN. In AD, the limited resources of the DMN may be paralleled, in an attempt to maintain cognitive efficiency, by an increased prefrontal connectivity. A medial parietal RS fMRI signal change seems to be present since the early phase of AD. © 2012 Elsevier Inc.. Source

Wang D.,Biostatistics Unit | Pocock S.,London School of Hygiene and Tropical Medicine
Pharmaceutical Statistics | Year: 2016

Clinical trials are often designed to compare continuous non-normal outcomes. The conventional statistical method for such a comparison is a non-parametric Mann-Whitney test, which provides a P-value for testing the hypothesis that the distributions of both treatment groups are identical, but does not provide a simple and straightforward estimate of treatment effect. For that, Hodges and Lehmann proposed estimating the shift parameter between two populations and its confidence interval (CI). However, such a shift parameter does not have a straightforward interpretation, and its CI contains zero in some cases when Mann-Whitney test produces a significant result. To overcome the aforementioned problems, we introduce the use of the win ratio for analysing such data. Patients in the new and control treatment are formed into all possible pairs. For each pair, the new treatment patient is labelled a 'winner' or a 'loser' if it is known who had the more favourable outcome. The win ratio is the total number of winners divided by the total numbers of losers. A 95% CI for the win ratio can be obtained using the bootstrap method. Statistical properties of the win ratio statistic are investigated using two real trial data sets and six simulation studies. Results show that the win ratio method has about the same power as the Mann-Whitney method. We recommend the use of the win ratio method for estimating the treatment effect (and CI) and the Mann-Whitney method for calculating the P-value for comparing continuous non-Normal outcomes when the amount of tied pairs is small. Copyright © 2016 John Wiley & Sons, Ltd. Source

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