Range and Variability of Outcomes Reported in Randomized Trials Conducted in Patients With Polycystic Kidney Disease: A Systematic Review.

RATIONALE & OBJECTIVE: Trials in autosomal dominant polycystic kidney disease (ADPKD) have increased, but their impact on decision making has been limited. Because heterogeneity in reported outcomes may be responsible, we assessed their range and variability in ADPKD trials. STUDY DESIGN: Systematic review. SETTING & STUDY POPULATION: Adult participants in clinical trials in ADPKD. SELECTION CRITERIA FOR STUDIES: We included trials that studied adults and were published in English. For trials that enrolled patients without ADPKD, only those enrolling ≥50% of participants with ADPKD were included. DATA EXTRACTION: We extracted information on all discrete outcome measures, grouped them into 97 domains, and classified them into clinical, surrogate, and patient-reported categories. For each category, we choose the 3 most frequently reported domains and performed a detailed analysis of outcome measures. ANALYTICAL APPROACH: Frequencies and characteristics of outcome measures were described. RESULTS: Among 68 trials, 1,413 different outcome measures were reported. 97 domains were identified; 41 (42%) were surrogate, 30 (31%) were clinical, and 26 (27%) were patient reported. The 3 most frequently reported domains were in the surrogate category: kidney function (54; 79% of trials; using 46 measures), kidney and cyst volumes (43; 63% of trials; 52 measures), and blood pressure (27; 40% of trials, 30 measures); in the clinical category: infection (10; 15%; 21 measures), cardiovascular events (9; 13%; 6 measures), and kidney failure requiring kidney replacement therapy (8; 12%; 5 measures); and in the patient-reported category: pain related to ADPKD (16; 24%; 26 measures), pain for other reasons (11; 16%; 11 measures), and diarrhea/constipation/gas (10; 15%; 9 measures). LIMITATIONS: Outcome measures were assessed for only the top 3 domains in each category. CONCLUSIONS: The outcomes in ADPKD trials are broad in scope and highly variable. Surrogate outcomes were most frequently reported. Patient-reported outcomes were uncommon. A consensus-based set of core outcomes meaningful to patients and clinicians is needed for future ADPKD trials.

A Multinomial Regression Approach to Model Outcome Heterogeneity.

When a risk factor affects certain categories of a multinomial outcome but not others, outcome heterogeneity is said to be present. A standard epidemiologic approach for modeling risk factors of a categorical outcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation. In this paper, we show that standard polytomous regression is ill equipped to detect outcome heterogeneity and will generally understate the degree to which such heterogeneity may be present. Specifically, nonsaturated polytomous regression will often a priori rule out the possibility of outcome heterogeneity from its parameter space. As a remedy, we propose to model each category of the outcome as a separate binary regression. For full efficiency, we propose to estimate the collection of regression parameters jointly using a constrained Bayesian approach that ensures that one remains within the multinomial model. The approach is straightforward to implement in standard software for Bayesian estimation.

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data.

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each "virtual individual", which we define as each fixed set of model parameters (1) and (2) above. The "virtual population" and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated "virtual population database" of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus-cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.