Frank, yes, they did adjust for baseline BP as a covariate in their change-from-baseline-BP model. I’ve marked what I see as discussion-worthy sections with bold-face text.

" STATISTICAL ANALYSIS

The sample size of 702 patients was based on the detection of a minimal clinically important difference of 3.0 mm Hg in the ambulatory systolic blood pressure on the assumption of a standard deviation of 9.0 mm Hg.21 To allow for a 10% dropout rate, we required the enrollment of 210 patients who could be evaluated in each group so that the trial would have a power of at least 84% at a significance level of 0.05, using a conservative adjustment for the three comparisons. **Since the P values are not adjusted for multiple comparisons among the three groups, the values should be interpreted in the context of the planned P value threshold of 0.017 (0.05÷3) after Bonferroni adjustment.**

For the primary end point, we used a linear mixed-effects model to estimate the mean difference in the ambulatory systolic blood pressure between the groups. **We used the restricted maximum-likelihood method to fit the model, which included adjustment for the baseline ambulatory systolic blood pressure, age (<55 or ≥55 years), and trial site as a random effect.**

**Two sensitivity analyses were performed.** First, in order to increase the precision of the estimate of the treatment effect, **we adjusted the model for clinically important and other variables: sex, the presence of diabetes or dyslipidemia (total cholesterol, >5.2 mmol per liter [200 mg per deciliter] or the receipt of statins), body-mass index (BMI), pulse rate, and duration of hypertension.** Second, we performed multiple-imputation analyses using chained equations for patients who had a missing primary end-point value. We generated five imputed-data sets with a maximum number of 1000 iterations, with linear imputation for continuous variables and logistic or multinomial regression for categorical variables. Variables that were included in the imputation model were treatment group, ambulatory systolic and diastolic blood pressures, office blood pressure measurements, age, sex, trial site, BMI, presence of diabetes or dyslipidemia, duration of hypertension, and pulse rate. We used the same framework to analyze ambulatory systolic and diastolic blood pressures.

The mean between-group difference in **office blood pressure** was estimated for each time point with the use of a linear mixed-effects model that included the patient as a random effect, along with age, baseline office blood pressure, follow-up duration, and interaction between time and treatment. We used logistic-regression analysis to compare the between-group response rate in office blood pressure after adjustment for age and site. All estimates of treatment effect were calculated with a 95% confidence interval. We used P values interpreted as a continuous measure to evaluate the strength of the evidence against the null hypothesis of no between-group difference.

**We did not plan for multiple-comparison adjustments for secondary outcomes, so the results are reported with point estimates and 95% confidence intervals only. The widths of the confidence intervals have not been adjusted for multiple comparisons, which should be taken into consideration when interpreting these results.**

We performed an efficacy analysis using the intention-to-treat principle and included all the patients for whom primary end-point data were available. Adverse events were assessed in all the patients until the end of follow-up and were tabulated according to trial group. All analyses were performed with Stata software, version 15 (StataCorp), and SPSS software, version 20 (IBM). **The statistical methods are detailed in the Supplementary Appendix, available at NEJM.org."**

An aside: the Supplementary Appendix states who did the statistical analysis & reports some additional results, but I could not find any other details of the statistical methods in the appendix.