Binding Potassium to Improve Treatment With Renin-Angiotensin-Aldosterone System Inhibitors: Results From Multiple One-Stage Pairwise and Network Meta-Analyses of Clinical Trials.

This manuscript presents findings from the first dichotomous data pooling analysis on clinical trials (CT) regarding the effectiveness of binding potassium. The results emanated from pairwise and network meta-analyses aiming evaluation of response to commercial potassium-binding polymers, that is, to achieve and maintain normal serum potassium (n = 1,722), and the association between this response and an optimal dosing of renin-angiotensin-aldosterone system inhibitors (RAASi) needing individuals affected by heart failure (HF) or resistant hypertension, who may be consuming other hyperkalemia-inducing drugs (HKID) (e.g., β-blockers, heparin, etc.), and frequently are affected by chronic kidney disease (CKD) (n = 1,044): According to the surface under the cumulative ranking area (SUCRA), sodium zirconium cyclosilicate (SZC) (SUCRA >0.78), patiromer (SUCRA >0.58) and sodium polystyrene sulfonate (SPS) (SUCRA <0.39) were different concerning their capacity to achieve normokalemia (serum potassium level (sK+) 3.5-5.0 mEq/L) or acceptable kalemia (sK+ ≤ 5.1 mEq/L) in individuals with hyperkalemia (sK+ >5.1 mEq/L), and, when normokalemia is achieved, patiromer 16.8-25.2 g/day (SUCRA = 0.94) and patiromer 8.4-16.8 g/day (SUCRA = 0.41) can allow to increase the dose of spironolactone up to 50 mg/day in subjects affected by heart failure (HF) or with resistant hypertension needing treatment with other RAASi. The potential of zirconium cyclosilicate should be explored further, as no data exists to assess properly its capacity to optimize dosing of RAASi, contrarily as it occurs with patiromer. More research is also necessary to discern between benefits of binding potassium among all type of hyperkalemic patients, for example, patients with DM who may need treatment for proteinuria, patients with early hypertension, etc. Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42020185614, CRD42020185558, CRD42020191430

Manipulating the Alpha Level Cannot Cure Significance Testing

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable

Manipulating the alpha level cannot cure significance testing – comments on "Redefine statistical significance"

We argue that depending on p-values to reject null hypotheses, including a recent call for changing the canonical alpha level for statistical significance from .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable criterion levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and determining sample sizes much more directly than significance testing does; but none of the statistical tools should replace significance testing as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, or implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable