Patient adherence and response time in electronic patient-reported outcomes: insights from three longitudinal clinical trials.

PURPOSE: Patient-reported outcome measures (PROMs) are used to collect data on disease symptoms in support of clinical trial endpoints. Clinical studies can last a year or more, and the patients' adherence and response time to daily at-home questionnaires may vary significantly over time. The aim of this study was to understand patterns and changes in patients' completion of daily PROMs during longitudinal clinical studies. METHODS: Data were collected from 1342 patients randomized into three respiratory clinical trials (NCT03401229, NCT03347279, and NCT03406078). PROMs were completed by patients using electronic handheld devices that collected the starting and completion times. A Bayesian generalized linear mixed-effects model was used to identify unbiased coefficients associated with PROM adherence and response time using patient, site, and calendar features as covariates. RESULTS: Adherence decreased over time after randomization, and the rate of decrease was higher in younger patients. The 14-day pre-randomization adherence was correlated with adherence throughout the study. Patients were also more adherent during working days compared to non-working days. Oldest patients took twice as long to complete PROMs throughout the study; however, the response time for all patients decreased during the first month of the study regardless of age. Response time increased 7 days before and after the date of a scheduled clinic visit and when a patient-reported higher symptom burden. CONCLUSION: Detailed analyses of adherence and response time for daily PROMs in clinical trials can provide significant insights about trends of patient behavior in longitudinal clinical studies with high baseline adherence.

Regulating the many to benefit the few: role of weak small RNA targets.

Small regulatory RNAs are central players in the regulation of many cellular processes across all kingdoms of life. Experiments in mouse and human have shown that a typical small RNA may regulate the expression of many different genes, suggesting that small RNAs act as global regulators. It is noted though that most targets respond only weakly to the presence of the small RNA. At the same time, evidence in bacteria and animals suggest that the phenotypes associated with small RNA mutants are only due to a few of their targets. Here we assume that targets regulated by a small RNA to control function is in fact small, and propose that the role of the many other weak targets is to confer robustness to the regulation of these few principal targets. Through mathematical modeling we show that auxiliary targets may significantly buffer both number and kinetic fluctuations of the principal targets, with only minor slowdown in the kinetics of response. Analysis of genomic data suggests that auxiliary targets experience a nonspecific evolutionary pressure, playing a role at the system level. Our work is of importance for studies on small RNA functions, and impacts on the understanding of small RNA evolution.

Small RNA biology is systems biology.

During the last decade small regulatory RNA (srRNA) emerged as central players in the regulation of gene expression in all kingdoms of life. Multiple pathways for srRNA biogenesis and diverse mechanisms of gene regulation may indicate that srRNA regulation evolved independently multiple times. However, small RNA pathways share numerous properties, including the ability of a single srRNA to regulate multiple targets. Some of the mechanisms of gene regulation by srRNAs have significant effect on the abundance of free srRNAs that are ready to interact with new targets. This results in indirect interactions among seemingly unrelated genes, as well as in a crosstalk between different srRNA pathways. Here we briefly review and compare the major srRNA pathways, and argue that the impact of srRNA is always at the system level. We demonstrate how a simple mathematical model can ease the discussion of governing principles. To demonstrate these points we review a few examples from bacteria and animals.