FDA's Foray Into Big Data Still Maturing

Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort

BACKGROUND

Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied.

METHODS

We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone-based PHQ-8 and passive measures.

RESULTS

Among the 45 individuals, 38 (84%) completed the 8-week study. The average root-mean-squared error (RMSE) in predicting the MADRS score (scale 0-60) was 4.72 using passive data alone, 4.27 using self-report measures alone, and 4.30 using both.

CONCLUSIONS

While passive measures did not improve MADRS score prediction in our cross-disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long-term via self-report.

A systematic review of smartphone-based human activity recognition methods for health research

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Transforming Data Into Evidence

BACKGROUND

Safety evaluation is a key aspect of medical product development. It is a continual and iterative process requiring thorough thinking, and dedicated time and resources.

METHODS

In this article, we discuss how safety data are transformed into evidence to establish and refine the safety profile of a medical product, and how the focus of safety evaluation, data sources, and statistical methods change throughout a medical product's life cycle.

RESULTS

Some challenges and statistical strategies for medical product safety evaluation are discussed. Examples of safety issues identified in different periods, that is, premarketing and postmarketing, are discussed to illustrate how different sources are used in the safety signal identification and the iterative process of safety assessment. The examples highlighted range from commonly used pediatric vaccine given to healthy children to medical products primarily used to treat a medical condition in adults. These case studies illustrate that different products may require different approaches, and once a signal is discovered, it could impact future safety assessments.

CONCLUSIONS

Many challenges still remain in this area despite advances in methodologies, infrastructure, public awareness, international harmonization, and regulatory enforcement. Innovations in safety assessment methodologies are pressing in order to make the medical product development process more efficient and effective, and the assessment of medical product marketing approval more streamlined and structured. Health care payers, providers, and patients may have different perspectives when weighing in on clinical, financial and personal needs when therapies are being evaluated.

Clinical Evidence Supporting US Food and Drug Administration Approval of Otolaryngologic Prescription Drug Indications, 2005-2014

Objective The US Food and Drug Administration (FDA) approves indications for prescription drugs based on premarket pivotal clinical studies designed to demonstrate safety and efficacy. We characterized the pivotal studies supporting FDA approval of otolaryngologic prescription drug indications. Study Design Retrospective cross-sectional analysis. Setting Publicly available FDA documents. Subjects Recently approved (2005-2014) prescription drug indications for conditions treated by otolaryngologists or their multidisciplinary teams. Drugs could be authorized for treatment of otolaryngologic disease upon initial approval (original indications) or thereafter via supplemental applications (supplemental indications). Methods Pivotal studies were categorized by enrollment, randomization, blinding, comparator type, and primary endpoint. Results Between 2005 and 2014, the FDA approved 48 otolaryngologic prescription drug indications based on 64 pivotal studies, including 21 original indications (19 drugs, 31 studies) and 27 supplemental indications (18 drugs, 33 studies). Median enrollment was 299 patients (interquartile range, 198-613) for original indications and 197 patients (interquartile range, 64-442) for supplemental indications. Most indications were supported by ≥1 randomized study (original: 20/21 [95%], supplemental: 21/27 [78%]) and ≥1 double-blinded study (original: 14/21 [67%], supplemental: 17/27 [63%]). About half of original indications (9/21 [43%]) and one-quarter of supplemental indications (7/27 [26%]) were supported by ≥1 active-controlled study. Nearly half (original: 8/21 [38%], supplemental: 14/27 [52%]) of all indications were approved based exclusively on studies using surrogate markers as primary endpoints. Conclusion The quality of clinical evidence supporting FDA approval of otolaryngologic prescription drug indications varied widely. Otolaryngologists should consider limitations in premarket evidence when helping patients make informed treatment decisions about newly approved drugs.