Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them

Funnel plots, and tests for funnel plot asymmetry, have been widely used to examine bias in the results of meta-analyses. Funnel plot asymmetry should not be equated with publication bias, because it has a number of other possible causes. This article describes how to interpret funnel plot asymmetry, recommends appropriate tests, and explains the implications for choice of meta-analysis model This article recommends how to examine and interpret funnel plot asymmetry (also known as small study effects 2 ) in meta-analyses of randomised controlled trials. The recommendations are based on a detailed MEDLINE review of literature published up to 2007 and discussions among methodologists, who extended and adapted guidance previously summarised in the Cochrane Handbook for Systematic Reviews of Interventions. 7 What is a funnel plot?A funnel plot is a scatter plot of the effect estimates from individual studies against some measure of each study's size or precision. The standard error of the effect estimate is often chosen as the measure of study size and plotted on the vertical axis 8 with a reversed scale that places the larger, most powerful studies towards the top. The effect estimates from smaller studies should scatter more widely at the bottom, with the spread narrowing among larger studies. 9 In the absence of bias and between study heterogeneity, the scatter will be due to sampling variation alone and the plot will resemble a symmetrical inverted funnel (fig 1). A triangle centred on a fixed effect summary estimate and extending 1.96 standard errors either side willCorrespondence to: J A C Sterne jonathan.sterne@bristol.ac.ukTechnical appendix (see

This review discusses recent developments in the areas of fabrication, certain types of optical characterization, and applications of a selected class of chemically assembled nanomaterials, namely i) gold and silver nanoparticles deposited onto optically transparent glass substrates; ii) thiol‐functionalized self‐assembled monolayers (SAMs); iii) chemically stabilized gold and silver nanoparticles (monolayer protected clusters, MPCs); and iv) MPCs linked to metallic substrates and adsorbates. Six linear optical techniques for the characterization of these materials are discussed: transmission localized surface plasmon resonance spectroscopy, T‐LSPR; propagating surface plasmon resonance spectroscopy, P‐SPR; polarization‐selective Fourier transform infrared reflection absorption spectroscopy, PS‐FTIRRAS; polarization‐modulation Fourier transform infrared reflection absorption spectroscopy, PM‐FTIRRAS; surface‐enhanced infrared reflection absorption spectroscopy, SEIRRAS; and infrared ellipsometry. The review focuses particularly on providing a unified treatment of these six optical techniques by using a relatively simple stratified multilayer model.

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Background: The heterogeneity statistic I 2 , interpreted as the percentage of variability due to heterogeneity between studies rather than sampling error, depends on precision, that is, the size of the studies included.

Loss to follow-up is often hard to avoid in randomised trials. This article suggests a framework for intention to treat analysis that depends on making plausible assumptions about the missing data and including all participants in sensitivity analyses