Confirming the existence of π-allyl-palladium intermediates during the reaction of meta photocycloadducts with palladium(ii) compounds

The transient existence of π-allyl-palladium intermediates formed by the reaction of Pd(OAc)2 and anisole-derived meta photocycloadducts has been demonstrated using NMR techniques. The intermediates tended to be short-lived and underwent rapid reductive elimination of palladium metal to form allylic acetates, however this degradation process could be delayed by changing the reaction solvent from acetonitrile to chloroform

Coping with Persistent Pain, Effectiveness Research into Self-management (COPERS): statistical analysis plan for a randomised controlled trial

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?

BACKGROUND: It is often desirable to account for centre-effects in the analysis of multicentre randomised trials, however it is unclear which analysis methods are best in trials with a binary outcome. METHODS: We compared the performance of four methods of analysis (fixed-effects models, random-effects models, generalised estimating equations (GEE), and Mantel-Haenszel) using a re-analysis of a previously reported randomised trial (MIST2) and a large simulation study. RESULTS: The re-analysis of MIST2 found that fixed-effects and Mantel-Haenszel led to many patients being dropped from the analysis due to over-stratification (up to 69% dropped for Mantel-Haenszel, and up to 33% dropped for fixed-effects). Conversely, random-effects and GEE included all patients in the analysis, however GEE did not reach convergence. Estimated treatment effects and p-values were highly variable across different analysis methods. The simulation study found that most methods of analysis performed well with a small number of centres. With a large number of centres, fixed-effects led to biased estimates and inflated type I error rates in many situations, and Mantel-Haenszel lost power compared to other analysis methods in some situations. Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel. However, this was only true for GEEs with non-robust standard errors (SEs); using a robust ‘sandwich’ estimator led to inflated type I error rates across most scenarios. CONCLUSIONS: With a small number of centres, we recommend the use of fixed-effects, random-effects, or GEE with non-robust SEs. Random-effects and GEE with non-robust SEs should be used with a moderate or large number of centres

Ideologies and their points of view

© Springer International Publishing Switzerland 2016. It is well known that different arguments appeal to different people. We all process information in ways that are adapted to be consistent with our underlying ideologies. These ideologies can sometimes be framed in terms of particular axes or dimensions, which makes it possible to represent some aspects of an ideology as a region in the kind of vector space that is typical of many generalised quantum models. Such models can then be used to explain and predict, in broad strokes, whether a particular argument or proposal is likely to appeal to an individual with a particular ideology. The choice of suitable arguments to bring about desired actions is traditionally part of the art or science of rhetoric, and today’s highly polarised society means that this skill is becoming more important than ever. This paper presents a basic model for understanding how different goals will appeal to people with different ideologies, and thus how different rhetorical positions can be adopted to promote the same desired outcome. As an example, we consider different narratives and hence actions with respect to the environment and climate change, an important but currently highly controversial topic

Evidence of unexplained discrepancies between planned and conducted statistical analyses: a review of randomized trials

Evidence of unexplained discrepancies between planned and conducted statistical analyses: a review of randomised trial

Public availability and adherence to prespecified statistical analysis approaches was low in published randomized trials

BACKGROUND AND OBJECTIVE: Prespecification of statistical methods in clinical trial protocols and statistical analysis plans can help to deter bias from p-hacking but is only effective if the prespecified approach is made available. STUDY DESIGN AND SETTING: For 100 randomized trials published in 2018 and indexed in PubMed, we evaluated how often a prespecified statistical analysis approach for the trial's primary outcome was publicly available. For each trial with an available prespecified analysis, we compared this with the trial publication to identify whether there were unexplained discrepancies. RESULTS: Only 12 of 100 trials (12%) had a publicly available prespecified analysis approach for their primary outcome; this document was dated before recruitment began for only two trials. Of the 12 trials with an available prespecified analysis approach, 11 (92%) had one or more unexplained discrepancies. Only 4 of 100 trials (4%) stated that the statistician was blinded until the SAP was signed off, and only 10 of 100 (10%) stated the statistician was blinded until the database was locked. CONCLUSION: For most published trials, there is insufficient information available to determine whether the results may be subject to p-hacking. Where information was available, there were often unexplained discrepancies between the prespecified and final analysis methods

Eliminating ambiguous treatment effects using estimands

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most studies do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is fraught, as many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings where patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly

Access to unpublished protocols and statistical analysis plans of randomised trials

Background: Access to protocols and statistical analysis plans (SAPs) increases the transparency of randomised trial by allowing readers to identify and interpret unplanned changes to study methods, however they are often not made publicly available. We sought to determine how often study investigators would share unavailable documents upon request. Methods: We used trials from two previously identified cohorts (cohort 1: 101 trials published in high impact factor journals between January and April of 2018; cohort 2: 100 trials published in June 2018 in journals indexed in PubMed) to determine whether study investigators would share unavailable protocols/SAPs upon request. We emailed corresponding authors of trials with no publicly available protocol or SAP up to four times. Results: Overall, 96 of 201 trials (48%) across the two cohorts had no publicly available protocol or SAP (11/101 high-impact cohort, 85/100 PubMed cohort). In total, 8/96 authors (8%) shared some trial documentation (protocol only [n = 5]; protocol and SAP [n = 1]; excerpt from protocol [n = 1]; research ethics application form [n = 1]). We received protocols for 6/96 trials (6%), and a SAP for 1/96 trial (1%). Seventy-three authors (76%) did not respond, 7 authors responded (7%) but declined to share a protocol or SAP, and eight email addresses were invalid (8%). A total of 329 emails were sent (an average of 41 emails for every trial which sent documentation). After emailing authors, the total number of trials with an available protocol increased by only 3%, from 52% in to 55%. Conclusions: Most study investigators did not share their unpublished protocols or SAPs upon direct request. Alternative strategies are needed to increase transparency of randomised trials and ensure access to protocols and SAPs