Statistical analysis
LUTS
REGRESSION, SECONDARY, RECRUITMENT
Statistical analyses were conducted with all consenting participants retained in the randomised arm of their general practice. Baseline characteristics at individual and practice level were summarised using means, standard deviations, medians (interquartile ranges), or number (percentage) depending on the nature and distribution of the data.The primary analysis of IPSS at 12 months was conducted on a modified intention-to-treat (ITT) basis, and comparisons between treatment arms were made using mixed effect multilevel linear models (individuals (level 1) nested within general practices (level 2)) adjusting for individual level baseline IPSS and practice level variables used in the randomisation based on those providing non-missing data for the variables included in the model. The results are presented as the mean difference between arms, 95% confidence interval, P value, and model intraclass correlation (95% confidence interval).The secondary outcomes were also analysed on a modified ITT basis. IPSS at six months were analysed using a mixed effect multilevel linear model (individuals (level 1) nested within general practices (level 2)) adjusting for individual level baseline IPSS and practice level variables used in the randomisation. Additionally, and separately, a repeated measures analysis was conducted using a repeated measures linear mixed model (IPSS at six and 12 months (level 1), nested within participants (level 2) and nested within general practices (level 3)) adjusting for individual level baseline IPSS scores and practice level variables used in the randomisation. Minimal clinically important differences for LUTS in men are not established in the literature for the secondary outcomes included.We planned seven sensitivity analyses to assess the robustness of the primary analysis to varying assumptions. Each of these sensitivity analyses was compared with the primary analysis.• Descriptive statistics were used to assess whether baseline characteristics were balanced between the two arms, and, if differences were observed, the primary analysis would be re-run adjusting for those imbalanced variables. This analysis was not performed, however, because we found no evidence of imbalance in variables not already included in the primary analysis.• To allow for possible clustering of outcomes within nurses and healthcare assistants delivering the intervention, we grouped patient level data according to the combination of practice and nurse or healthcare assistant delivering the intervention. The primary analysis was then re-run using a single random effect for this level of clustering.• Although the target recruitment was reached before the covid-19 outbreak, we wanted to allow for any influence of the outbreak and subsequent lockdowns on participation and symptom reporting. To do this we repeated the primary analysis including a binary variable for whether or not the outcome measure was taken before or after 11 March 2020 (the date the World Health Organization declared the outbreak a pandemic).• A small number of recruited participants were subsequently found to be ineligible. They were included in the primary analysis, and a sensitivity analysis was performed excluding those individuals.• A series of per protocol analyses were performed using different definitions of protocol compliance (see supplementary table S1).• Recognising the biases inherent in per protocol analyses we also performed a complier average causal analysis. Compliers were those participants who received the intervention booklet by the time of follow-up for the primary outcome. The estimates for complier average causal analysis were obtained using instrumental variable regression with the same variables used in the primary analysis, with the randomised arm as the instrumental variable and an indicator variable for compliance.• We explored the impact of missing primary outcome data using different assumptions for missingness: “best” and “worst” case scenarios as well as multiple imputation by chained equations to impute missing data.To explore whether the effectiveness of the intervention on the primary outcome differed by participant subgroup, we performed four prespecified subgroup analyses. In each case, effect modification was assessed by including a subgroup-treatment interaction term and performing a likelihood ratio test comparing the model with and without the interaction term. A significance level of 5% was used, but as these analyses were not statistically powered, they should be interpreted with caution. Subgroup analyses assessed whether effectiveness differed by the nature of LUTS at baseline measured by the ratio of the IPSS voiding score to the storage score; whether a practice nurse, healthcare assistant, or trial nurse delivered the intervention; the participant’s preferred method of contact at baseline; and the number of contacts between the nurse or healthcare assistant and participant at intervention practices.