Addressing researcher degrees of freedom through minP adjustment.

When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers' analytical choices, an issue also referred to as "researcher degrees of freedom". Combined with selective reporting of the smallest p-value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the "minP" adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal p-value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative p a O 2 on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error-and thus the risk of publishing false positive results that may not be replicable.

The RAPID-score: Risk Assessment and PredIction of Delirium in acute stroke patients based on very early clinical parameters.

Background and objective: Post-stroke delirium (PSD) is a common complication in acute stroke patients, and guidelines recommend routine screening and various preventive and treatment measures. However, there is a substantial lack of standardized approaches in diagnostic and therapeutic management of PSD. Here, we aimed to develop a new pragmatic and easily assessable screening tool to predict PSD based on early parameters, which are already integral to acute stroke diagnostics. Methods: We enrolled acute stroke patients admitted to our stroke unit or intensive care unit and developed the scoring system using retrospective single-center patient data. The Confusion Assessment Method for the Intensive Care Unit was used for prospective score validation. Logistic regression models were employed to analyze the association of early clinical and paraclinical parameters with PSD development. Results: N = 525 patients (median age: 76 years; 45.7% female) were enrolled, with 29.7% developing PSD during hospitalization. The resulting score comprises 6 items, including medical history, clinical examination findings, and non-contrast computed tomography results at admission. Scores range from -15 to +15 points, with higher values indicating a higher likelihood of PSD, ranging from 4% to 79%. The accuracy was 0.85, and the area under the curve was 0.89. Conclusion: The new RAPID (Risk Assessment and PredIction of Delirium in acute stroke patients)-score shows high accuracy in predicting PSD among acute stroke patients and offers precise odds of PSD for each corresponding score value, utilizing routine early clinical and paraclinical parameters. It can identify high-risk populations for clinical study interventions and may be suitable to guide prophylactic PSD measures.