Statistical analysis
MCI
INFLAMMATORY RESPONSES, ADVERSE EVENTS, RECRUITMENT, SECONDARY, REGRESSION
VINCI-AD will assess the safety and feasibility of tVNS in older adults with MCI. All participants will be included in final analysis in an “intention-to-treat” fashion. Statistical analysis will be conducted using MATLAB (The MathWorks, Inc., Natick, MA, USA), SPSS (IBM SPSS Statistics for Windows, Armonk, NY: IBM Corp) and GraphPad Prism (GraphPad Software, Inc., San Diego, CA, USA). We anticipate that TSI analysis will require cleaning to remove “noisy” channels i.e. removing large spikes (changes in oxy-Hb larger than 0.5 mM/mm in amplitude); noisy channels will be excluded from analyses and data analyses will utilise software Platform for Optical Topography Analysis Tools [In the first instance, descriptive statistics will be described for the cohort, reported as means (with standard deviations) or medians (with inter-quartile ranges) and proportions (percentages) as appropriate.The primary aim of the current study is to assess the safety and feasibility of tVNS in MCI. To assess safety, a chi-square test will compare the number of adverse events reported during the active vs. sham condition. Further safety assessment will elicit symptoms experienced during active stand (using a chi-square statistic), peripheral (beat-to-beat blood pressure response to orthostasis – using mixed-effects linear regression with study condition as the independent variable, with random intercepts for study participant) and central (TSI – also assessed using a linear mixed-effects model). Feasibility will be reported via recruitment rates, successful enrolment, and study completion in proportions and percentages. Tolerability will be assessed using Likert scales, which will be compared between active and sham conditions using paired between-condition statistics (t-test if normally distributed or non-parametric equivalent). Descriptive statistics will be used to report acceptability using the modified Kettle test.Exploratory secondary aims of this study (cognitive testing, HRV analysis, inflammatory responses) will be analysed using a repeated three – way analysis of variance (ANOVA) if parametric or Kruskal-Wallis test if non-parametric analysis required, with Bonferroni corrections for multiple comparisons. We will explore the effects of concentrations of pro-inflammatory plasma cytokines on cognitive and neurocardiovascular characteristics. Missing data will be handled by applicable statistical methods including multiple imputation.