Statistical analysis
REGRESSION, SECONDARY
Descriptive statistics were performed to demonstrate the demographic features, health conditions, and clinic manifestations at baseline and each time point during follow-up, continuous data were presented as mean(sd), categorical data were presented as median (IQR), and binary data were presented as n (%).To analyze the differences in the subject’s characteristics between the placebo and FMT groups after the intervention, generalized estimating equation (GEE) models were used to analyze the primary and secondary outcomes (including the total score of MDS-UPDRS and Part 1, Part 2, Part 3, Part4, and the scores of IBS-SSS, GSRS, IBS-QOL, MMSE, MOCA, PHQ-9, GDS-15, GAD-7, Bristol stool scale, and stool frequency). For the GEE analysis in this work, time was set as within-subject variables, an independent correlation matrix with robust SEs was used to account for the correlation between multiple responses for the same participants. A linear regression for score outcomes (MDS-UPDRS total score, MDS-UPDRS part1/2/3/4, GSRS, IBS-SSS, IBS-QOL, MMSE, MOCA, PHQ9, GDS-15, and GAD7), was used to estimate mean differences, ordinal logistic regression for ordinal outcomes (Bristol and stool frequency). Models treated allocated intervention (Group) and time point (Time) as main effects, and Group*Time as interact effect. Time effect was the difference by comparing FMT (or placebo) arm at each timepoint and the baseline. Group×time effect was time-by-group interaction, representing the difference of the time effect between two arms at each timepoint. Statistical analysis was performed using SPSS statistical software, version 23·0 (IBM
Corporation). All statistical tests were 2-tailed with a 5% level of statistical significance.