Useful tools for peer reviewing

Regular reviewing quickly reveals that many discussion points are common across manuscripts, and that a lot of the work can be reused. The following resources have been very helpful to me in my methodological review of medical research reports. First, Zotero, a free tool that is useful for saving references, review comments, general notations and entire review reports. The information in my Zotero library is organized in this way: I. 'Comments and References', a folder with 12 subfolders for different methodological problem areas such as study design, estimands/estimators, clinical trials, observational studies, etc. Each subfolder contains both i) what I have found to be important references to literature describing methodological issues with their solutions, and ii) my recyclable specific comments and suggestions to investigators. II. 'Editor Correspondence', a folder containing typically confidential information (comments, explanations, motivations, and suggestions) to editors and editorial offices. III. 'Review Reports', where each journal where I have been assigned as a reviewer has its own subfolder. Here I put copies of entire review reports. Second, when I review a manuscript, I often copy text from different parts of the manuscript and paste it into an editor. Then, I reorganize these text parts to give the review report a logical structure. I use text editor Sublime Text for this purpose. This editor has advanced functions for programming tasks but works well for just writing, copying, pasting, and restructuring text. Third and finally, I avoid slang words and try to write in as simple and clear a language as possible in my review reports so that there are no misunderstandings. As English is not my native language, I always check the spelling and grammar of my review reports. Various tools of this nature are accessible via the internet. I have myself used, Grammarly and LanguageTool. Both offer excellent writing support.

Comments

Post a Comment

Popular posts from this blog

Confounders, mediators, and colliders

  Confounding is a distortion of the estimated effect of an exposure on an outcome. The distortion is caused by a confounder that impacts both exposure and outcome. Exposure <- Confounder -> Outcome An observed exposure effect may then reflect the effect of the confounder rather than exposure. For example, when studying the effect of alcohol consumption on cardiovascular disease risk, prior health status, is a confounder if it affects both alcohol consumption and the risk of a cardiovascular event. Confounding can be dealt with in two ways. The first is to perform an experiment with randomised assignment of subjects to exposure and a control group. The advantage with this strategy is that it prevents against confounding from all confounders, known as well as unknown. However, the disadvantage is that it cannot be used to investigate effects of harmful exposures as that would be unethical and in many cases practically impossible. Other disadvantages are that trials are expensi...
Read more

Frequentists and Bayesians

Followers of today's two main traditions of statistical inference are known as frequentist and Bayesian. Ronald Fisher attempted during the 1930s to develop a third school called fiducial inference (1), but this was broadly considered controversial and do not play a major role today. The fundamental difference between frequentists and Bayesians is that they define probability in different ways. For a frequentist, a probability is an objective measure, a long-run relative frequency. For example, a fair coin has probability 0.5 of heads because, across a very large number of comparable tosses, half are heads, and a drug lowering blood pressure has an objectively measurable average effect among the patients taking the drug. The central limit theorem, which states that as the sample size increases, the distribution of sample means tends to approximate a normal distribution, is essential in frequentist inference. A Bayesian, on the other hand, sees probability as a subjective measure, a...
Read more

Randomisation and alternation in clinical trials

  In early clinical trials patients were allocated to treatment and control groups by alternation, i.e. patients were assigned to the treatment group and control group in a strict order. The advantage with this method was that it produced groups with equal size. The two groups would also be fairly compared if the order of the patients included in the trial was random. However, the group allocation for a patient was predictable, which could potentially introduce selection bias into the trial's outcome through differential invitations to participate. The only way to avoid this issue was, of course, to make the group assignment concealed, which necessitated a non-systematic group assignment sequence. D'Arcy Hart (1) describes the development with two trials from the 1940s. The first one investigating the effects of patulin (a mycotoxin) on the common cold in 1943-1944 with strict alternation. The second one, having a randomised group assignment, investigating the effects of strept...
Read more