Bmc Med Res Methodol
-
Bmc Med Res Methodol · May 2014
Network-meta analysis made easy: detection of inconsistency using factorial analysis-of-variance models.
Network meta-analysis can be used to combine results from several randomized trials involving more than two treatments. Potential inconsistency among different types of trial (designs) differing in the set of treatments tested is a major challenge, and application of procedures for detecting and locating inconsistency in trial networks is a key step in the conduct of such analyses. ⋯ Factorial analysis of variance provides a convenient framework for conducting network meta-analysis, including diagnostic checks for inconsistency.
-
Systematic Reviews (SRs) of experimental animal studies are not yet common practice, but awareness of the merits of conducting such SRs is steadily increasing. As animal intervention studies differ from randomized clinical trials (RCT) in many aspects, the methodology for SRs of clinical trials needs to be adapted and optimized for animal intervention studies. The Cochrane Collaboration developed a Risk of Bias (RoB) tool to establish consistency and avoid discrepancies in assessing the methodological quality of RCTs. A similar initiative is warranted in the field of animal experimentation. ⋯ SYRCLE's RoB tool is an adapted version of the Cochrane RoB tool. Widespread adoption and implementation of this tool will facilitate and improve critical appraisal of evidence from animal studies. This may subsequently enhance the efficiency of translating animal research into clinical practice and increase awareness of the necessity of improving the methodological quality of animal studies.
-
Bmc Med Res Methodol · Jan 2014
Meta AnalysisAn overview of the statistical methods reported by studies using the Canadian community health survey.
The Canadian Community Health Survey (CCHS) is a cross-sectional survey that has collected information on health determinants, health status and the utilization of the health system in Canada since 2001. Several hundred articles have been written utilizing the CCHS dataset. Previous analyses of statistical methods utilized in the literature have focused on a particular journal or set of journals to understand the statistical literacy required for understanding the published research. In this study, we describe the statistical methods referenced in the published literature utilizing the CCHS dataset(s). ⋯ Our study shows a diverse set of analysis methods being referenced in the CCHS literature, however, the literature heavily relies on only a subset of all possible statistical tools. This information can be used in identifying gaps in statistical methods that could be applied to future analysis of public health surveys, insight into training and educational programs, and also identifies the level of statistical literacy needed to understand the published literature.
-
Bmc Med Res Methodol · Jan 2014
A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB.
There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). ⋯ Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions.
-
Bmc Med Res Methodol · Jan 2014
Bayesian designs of phase II oncology trials to select maximum effective dose assuming monotonic dose-response relationship.
For many molecularly targeted agents, the probability of response may be assumed to either increase or increase and then plateau in the tested dose range. Therefore, identifying the maximum effective dose, defined as the lowest dose that achieves a pre-specified target response and beyond which improvement in the response is unlikely, becomes increasingly important. Recently, a class of Bayesian designs for single-arm phase II clinical trials based on hypothesis tests and nonlocal alternative prior densities has been proposed and shown to outperform common Bayesian designs based on posterior credible intervals and common frequentist designs. We extend this and related approaches to the design of phase II oncology trials, with the goal of identifying the maximum effective dose among a small number of pre-specified doses. ⋯ The use of Bayesian hypothesis tests and nonlocal alternative priors under ordering constraints between dose groups results in a robust performance of the design, which is thus superior to other common designs.