Author's response to reviews

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1 Author's response to reviews Title:Control of fluid balance guided by body composition monitoring in patients on peritoneal dialysis (COMPASS): study protocol for a randomized controlled trial Authors: Seon Ha Baek (haya2001@hanmail.net) Kook-Hwan Oh (ohchris@hanmail.net) Sejoong Kim (imsejoong@hanmail.net) Dong Ki Kim (dkkim73@gmail.com) Yun Kyu Oh (yoonkyuoh@gmail.com) Byoung Geun Han (neptune@yonsei.ac.kr) Jae Hyun Chang (jhchang@gilhospital.com) Wookyung Chung (jwkpsj79@hanmail.net) Yon Su Kim (yonsukim@snu.ac.kr) Ki Young Na (kyna@snubh.org) Version:4Date:2 October 2014 Author's response to reviews: see over

2 [REVIEWER 1 ] Major Compulsory Revisions 1. There needs to be more specificity on the analysis of the primary outcome. Specifically, the current plan is to calculate the GFR slope (from baseline until 12 months after treatment has begun) for each of the two treatments groups and compare the two slopes with a t-test. This test, however, presumes roughly linear GFR trajectories for each of the two groups. Is this a reasonable assumption? If unknown, then there needs to be a contingency plan for comparing the trajectories in case of any nonlinearities observed over time in either of the two groups. We think that the expression GFR slope may cause misunderstanding. The primary outcome is the change in GFR between month 0 and 12 after starting treatment. It is not the trajectories of GFR. We will only measure GFR twice at 0 and 12 th months. We corrected the primary outcome in the section of abstract and methods/design. Therefore, it would be suitable to analyze the primary outcome using Student s T test. Furthermore, we will use the analysis of covariance (ANCOVA) for the primary outcome as the secondary analysis to adjust the baseline value. We described ANCOVA in the section of statistical analyses. - Page 3, 6, and 9 2. Under Outcomes Measures, there is a long list of secondary outcomes provided. These should be tiered in some fashion, so that just a few important secondary outcomes are considered and others are listed as tertiary outcomes. The reason for this is that given the plethora of secondary outcomes listed, at least some differences between the two groups will be determined as statistically significant (especially given the planned.05 significance level for all tests) even if the truth is that there are no differences. There may be a temptation to emphasize the positive results on a few tests

3 on the secondary outcomes and ignore the other results, even (and maybe especially so) if the primary outcome test is negative. If fewer secondary outcomes (the most important ones under the long list currently provided) are listed, then potential criticism of backing the positive ones will be minimized. You could still perform tests for the tertiary outcomes, but these would be considered less important compared to the others in the primary (one) and secondary tiers (a few). We agree with the reviewer s opinion. We corrected the secondary and tertiary outcomes as the reviewer has pointed out. - Page 3 and 6 3. There was lack of specificity on recruitment institutions, other than that they will be used as strata for randomization. There may be a need to consider institution during the analysis phase of the study if patients within the same institution are potentially more correlated than patients between institutions. If this is the case, your power calculations may be compromised, since not everyone in the study would be independent. This issue needs to be addressed in the analysis plan. Thanks for the good comments. We listed specific recruitment institutions in the section of study participants and measurements. Furthermore, we added the reviewer s comments and our responses in section of Statistical analyses, and Discussion. Our considerations for the clustering effect are as follows. First, the size of participants needs to be increased for maintaining the statistical power. As the drop-out rate has been less than 5% according to the survey up to now, the larger number of subjects will be collected at the end of this study. We expect that it will compensate the unsuspected correlation. Second, we expect that study participants will be homogenous because our participating institutions are located within the large cities such as Seoul and Inchon. Third, though the institutions are mainly located in large cities, thereby limiting the

4 likelihood of any important cluster effect, we will be sure to accommodate such clustering in our models and analysis as required. - Page 5, 9, and Is there anything about the trajectory of the primary outcome that could be distinct when participants are under treatment vs. when they are not? That is, your primary outcome appears to be based on looking at the slope between baseline (-1 month, i.e., one month before treatment begins) and 12 months after treatment has begun. This is distinct from some studies where baseline measures are taken immediately before treatment begins. And this one month gap between baseline and start of treatment could play a role in the lack of a linear trajectory of GFR over time (see (1.) above). We think that the expression baseline may cause misunderstanding. Actually, at one month before treatment, we will screen the patients according to inclusion/exclusion criteria and collect clinical information. We will perform laboratory tests, echocardiography, and PWV at 0 month. We will measure GFR twice at month 0 and 12 in this study. We changed baseline to 0 month. Also, we revised table 2. - Page 3, 6, 8, 9, and 18 Minor Essential Revisions 1. Under Safety Issues, there is an apparent typo that looks like it should be corrected to say approved by the United States Food and Drug Administration. We corrected typographic error. - Page 8 Discretionary Revisions

5 1. Curious why BCM is measured every two months for main treatment group patients and only measured twice for control group patients. For the control group, clinical information obtained by the physician s physical examination is the standard of judgment. We measure the BCM twice for this group only to evaluate the objective volume status at the beginning and end of the study. In a similar study performed in hemodialysis patients [ref, 9], the interval for BCM measurement were different between the intervention and control groups.

6 [REVIEWER 2 ] Minor comments; Is there any rational basis to include patients with a daily urine output of more than 500 ml? The previous report proposed that definition of residual renal function (RRF) as urine output (U/O) 500 ml/day and renal glomerular filtration rate (rgfr) 2 ml/min/1.73m 2, rapid declining RRF as U/O < 500 ml/day and rgfr < 2 ml/min/1.73m 2 occurring within 6 months (Herget-Rosenthal et al, Kidney Blood Press Res. 2012;35(4): ). We adopted the definition of RRF on the basis of U/O in this study.