Layman’s abstract for paper on mixed‐effects models for slope‐based endpoints in clinical trials of chronic kidney disease

Every few days, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from Statistics in Medicine and the full article, published in issue 38.22, is available to read online here.

Vonesh, E, Tighiouart, H, Ying, J, et al. Mixed‐effects models for slope‐based endpoints in clinical trials of chronic kidney disease. Statistics in Medicine. 2019; 38: 4218– 4239. doi: 10.1002/sim.8282

Chronic kidney disease (CKD) is common and harmful, causing kidney failure in its late stages, but with few therapies. One of the challenges in the development and evaluation of therapies for CKD is that kidney failure usually occurs after many years of progressive disease. In order to obtain sufficient clinical endpoints of kidney failure such as time to dialysis or transplantation, randomized controlled trials (RCTs) require substantial follow-up periods or are restricted to patients with rapidly progressive or late stage disease even though some interventions may have a greater effect when applied earlier versus later in the disease course. In March of 2018, the National Kidney Foundation, in collaboration with the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA), sponsored a workshop to present evidence for the use of surrogate endpoints in RCT’s of CKD progression which would lead to more efficient trials, especially in earlier stages of CKD. Kidney function is assessed using glomerular filtration rate (GFR). The rate of decline in GFR (GFR slope) is on the pathway to kidney failure and thus GFR slope is a candidate surrogate endpoint. In comparison to time to kidney failure, a slope based endpoint can be assessed over a shorter period of time and will often require fewer patients. As such, RCT’s could be conducted among patients in the early stages of CKD where kidney failure often does not occur until 20 years or more. There are, however, a number of challenges that can complicate analyses based on GFR slope and its use in clinical trials. These include the possibility of an immediate but short-term difference in GFR between treatment arms (called the acute effect), differences in GFR variability both within subjects over time and between subjects receiving different treatment regimens, and informative censoring resulting from patient dropout due to death or onset of kidney failure. To address these issues, a class of mixed-effects models were developed for GFR slope-based endpoints that account for these nuances and which can be applied to a specific study as required. The models and methods of analysis are described and SAS code provided using data from two CKD studies for illustrative purposes. SAS code for performing sample size/power calculations for CKD trials based on GFR slope comparisons is also provided.