IMPORTANCE While guidance on statistical principles for clinical trials exists, there is an absence of guidance covering the required content of statistical analysis plans (SAPs) to support transparency and reproducibility.OBJECTIVE To develop recommendations for a minimum set of items that should be addressed in SAPs for clinical trials, developed with input from statisticians, previous guideline authors, journal editors, regulators, and funders. DESIGN Funders and regulators (n = 39) of randomized trials were contacted and the literature was searched to identify existing guidance; a survey of current practice was conducted across the network of UK Clinical Research Collaboration-registered trial units (n = 46, 1 unit had 2 responders) and a Delphi survey (n = 73 invited participants) was conducted to establish consensus on SAPs. The Delphi survey was sent to statisticians in trial units who completed the survey of current practice (n = 46), CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guideline authors (n = 16), pharmaceutical industry statisticians (n = 3), journal editors (n = 9), and regulators (n = 2) (3 participants were included in 2 groups each), culminating in a consensus meeting attended by experts (N = 12) with representatives from each group. The guidance subsequently underwent critical review by statisticians from the surveyed trial units and members of the expert panel of the consensus meeting (N = 51), followed by piloting of the guidance document in the SAPs of 5 trials.FINDINGS No existing guidance was identified. The registered trials unit survey (46 responses) highlighted diversity in current practice and confirmed support for developing guidance. The Delphi survey (54 of 73, 74% participants completing both rounds) reached consensus on 42% (n = 46) of 110 items. The expert panel (N = 12) agreed that 63 items should be included in the guidance, with an additional 17 items identified as important but may be referenced elsewhere. Following critical review and piloting, some overlapping items were combined, leaving 55 items.CONCLUSIONS AND RELEVANCE Recommendations are provided for a minimum set of items that should be addressed and included in SAPs for clinical trials. Trial registration, protocols, and statistical analysis plans are critically important in ensuring appropriate reporting of clinical trials.

Rare kidney diseases encompass at least 150 different conditions, most of which are inherited. Although individual rare kidney diseases raise specific issues, as a group these rare diseases can have overlapping challenges in diagnosis and treatment. These challenges include small numbers of affected patients, unidentified causes of disease, lack of biomarkers for monitoring disease progression, and need for complex care. To address common clinical and patient issues among rare kidney diseases, the KDIGO Controversies Conference entitled, Common Elements in Rare Kidney Diseases, brought together a panel of multidisciplinary clinical providers and patient advocates to address five central issues for rare kidney diseases. These issues encompassed diagnostic challenges, management of kidney functional decline and progression of chronic kidney disease, challenges in clinical study design, translation of advances in research to clinical care, and provision of practical and integrated patient support. Thus, by a process of consensus, guidance for addressing these challenges was developed and is presented here.

Research in clinical pharmacology covers a wide range of experiments, trials and investigations: clinical trials, systematic reviews and meta-analyses of drug usage after market approval, the investigation of pharmacokinetic-pharmacodynamic relationships, the search for mechanisms of action or for potential signals for efficacy and safety using biomarkers. Often these investigations are exploratory in nature, which has implications for the way the data should be analysed and presented. Here we summarize some of the statistical issues that are of particular importance in clinical pharmacology research. Descriptive vs. confirmatory investigationsThe development of drugs is generally based on sequential phases, aiming first to learn and subsequently to confirm the mechanisms of efficacy and safety [1]. Although early clinical pharmacology investigations often benefit from extensive preclinical results, there is still much to be learned during the first human trials when the first clinical data are obtained, described and interpreted. Learning can often be achieved by quantifying how measurements that we make are changed by the treatments we give. Such changes are best described using standard descriptive statistics of the data, such as the mean and standard deviation (as well as median, minimum, quartiles and maximum values).Inferential statistical analyses are performed to draw conclusions about general (patient) populations. Those analyses might involve statistical models (e.g. analysis of covariance), and the results would be reported using estimates of population parameters (such as means or proportions) and their standard errors or confidence intervals, which quantify the variability and uncertainty of the reported estimates. Confidence intervals vs. P-valuesWhen comparing treatment outcomes, confidence intervals for parameters of interest (such as the difference between treatment means) provide insight into the potential clinical relevance of a treatment effect in a clinical trial or observational study. Confidence intervals are on the same scale with the same units as the mean, which makes them a preferred choice for reporting results.In contrast, P-values can be used to demonstrate whether or not the difference between two treatments is statistically significant, but statements of statistical significance alone do not necessarily indicate the clinical relevance of an effect and hence might not adequately address the objectives of a clinical pharmacology trial. P-values do not measure the size of an effect or the importance of a result, and do not provide a good measure of evidence regarding a hypothesis [2]. These limitations hold in particular for comparisons that had not been adequately powered.Consequently, P-values that indicate the rejection of hypotheses should be reported only sparsely. Instead, use of confidence intervals is recommended for reporting trial results.

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