Statistical analysis
REGRESSION
The co-primary objectives of the study were to (1) estimate the cCR rate with gemcitabine, cisplatin, plus nivolumab and (2) assess the positive predictive value of cCR for a composite outcome measure of (1) 2-year metastasis-free survival in patients achieving a cCR and opting to not undergo immediate cystectomy or (2) <pT1N0 in patients with a cCR who opted for immediate cystectomy. Secondary objectives included assessing the association between genomic alterations in a pre-specified panel of genes detected in pre-treatment TURBT tissue (The sample size was based on the following assumptions: (1) patients without a cCR would not be suitable to forgo cystectomy; (2) ~40% of enrolled patients would achieve a cCR; and (3) ~35% of enrolled patients would achieve the composite outcome measure. Therefore, our assumption implied that the negative predictive value of a cCR would be 1. The sample size was based on the CI width of the positive predictive value of cCR for the composite outcome measure and generated such that the lower bound of the 95% one-sided CI exceeded 80%. This required enrollment of 68 patients, and the sample size was increased to 76 to account for potential missing data.Rates were calculated using percentages and compared among different groups using Fisher’s exact test. Time-to-event outcomes were analyzed using the Kaplan–Meier method and log-rank test. When comparing time-to-event outcomes for restaging cCR status and restaging VI-RADS, landmark analyses were conducted using the restaging times as the landmark time (that is, time 0). For analysis of multiplex protein immunoassay (Olink) data, the data were normalized with the reference samples using R software. The data distribution per sample was compared, and samples were inspected with warnings after NPX conversion. For CyTOF analysis, the data were normalized to percent of cell abundance and 95th percentile of surface protein expression. For Olink and CyTOF, differential protein expression and differential cell abundance, respectively, were calculated using a mixed effect linear model strategy to adjust for relevant clinical variables (ECOG performance status) and demographics (age, race and gender). First, the variance profiles and data distributions were explored to identify potential biases and assess the effect of relevant covariates in the analysis using the packages lme4, variancePartition and Dream. For Olink and CyTOF, quality control analysis served to identify biases such as low detection and poor sample quality. The filters included removing variables with more than 40–70% not available or under the limit of detection values. The variables were verified as linearly independent such that there was no redundancy in the data. After quality control, individual expression or abundance was modeled as a function of relevant endpoints and covariates. Differential expression or abundance analyses were performed applying a contrast matrix to each regression model (one per endpoint) and using the moderated t-statistic or log odds when appropriate. False discovery rate adjustment was performed on resulting 