Why we need a small data paradigm

Abstract Background There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed. By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit. Main body The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality. Conclusion Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.https://deepblue.lib.umich.edu/bitstream/2027.42/152218/1/12916_2019_Article_1366.pd

Improving Adherence and Clinical Outcomes in Self-Guided Internet Treatment for Anxiety and Depression: A 12-Month Follow-Up of a Randomised Controlled Trial

Background: A recent paper reported the outcomes of a study examining a new self-guided internet-delivered treatment, the Wellbeing Course, for symptoms of anxiety or depression. This study found the intervention resulted in significant symptom reductions. It also found that automated emails increased treatment completion and clinical improvements in a subsample with elevated anxiety and depression. Aims: To examine the clinical outcomes and the effect of automated emails at 12 months post-treatment. Method: Participants, who were randomly allocated to a Treatment Plus Automated Emails Group (TEG; n = 100), a standard Treatment Group (TG; n = 106) or delayed-treatment Waitlist Control Group (Control; n = 51), were followed up at 12 months post-treatment. Eighty-one percent, 78% and 87% of participants in the TEG, TG and treated Waitlist Control Group provided symptom data at 12-month follow-up, respectively. The primary outcome measures were the Patient Health Questionnaire-9 Item Scale (PHQ-9) and the Generalized Anxiety Disorder-7 Item Scale (GAD-7).Results: Significant improvements in symptoms of anxiety and depression were observed over time in both the TEG and TG (Fs >69, ps .05), and were associated with large effect sizes. No statistically significant differences in symptoms were found between the TEG and TG at post-treatment, 3-month or 12-month follow-up. Previously reported symptom differences between TEG and TG participants with comorbid symptoms were no longer present at 12-month follow-up (ps >.70).Conclusions: The overall benefits of the Wellbeing Course were sustained at 12-month follow-up. Although automated emails facilitated Course completion and reductions in symptoms for participants with comorbid anxiety and depression from pre-post treatment, these differences were no longer observed at 12-month follow-up. The results indicate that automated emails promote more rapid treatment response for people with elevated and comorbid symptoms, but may not improve longer term outcomes

Identification and thermochemical analysis of high-lignin feedstocks for biofuel and biochemical production

Background - Lignin is a highly abundant biopolymer synthesized by plants as a complex component of plant secondary cell walls. Efforts to utilize lignin-based bioproducts are needed. Results - Herein we identify and characterize the composition and pyrolytic deconstruction characteristics of high-lignin feedstocks. Feedstocks displaying the highest levels of lignin were identified as drupe endocarp biomass arising as agricultural waste from horticultural crops. By performing pyrolysis coupled to gas chromatography-mass spectrometry, we characterized lignin-derived deconstruction products from endocarp biomass and compared these with switchgrass. By comparing individual pyrolytic products, we document higher amounts of acetic acid, 1-hydroxy-2-propanone, acetone and furfural in switchgrass compared to endocarp tissue, which is consistent with high holocellulose relative to lignin. By contrast, greater yields of lignin-based pyrolytic products such as phenol, 2-methoxyphenol, 2-methylphenol, 2-methoxy-4-methylphenol and 4-ethyl-2-methoxyphenol arising from drupe endocarp tissue are documented. Conclusions - Differences in product yield, thermal decomposition rates and molecular species distribution among the feedstocks illustrate the potential of high-lignin endocarp feedstocks to generate valuable chemicals by thermochemical deconstruction

The eClinical Care Pathway Framework: A novel structure for creation of online complex clinical care pathways and its application in the management of sexually transmitted infections.

Despite considerable international eHealth impetus, there is no guidance on the development of online clinical care pathways. Advances in diagnostics now enable self-testing with home diagnosis, to which comprehensive online clinical care could be linked, facilitating completely self-directed, remote care. We describe a new framework for developing complex online clinical care pathways and its application to clinical management of people with genital chlamydia infection, the commonest sexually transmitted infection (STI) in England.Using the existing evidence-base, guidelines and examples from contemporary clinical practice, we developed the eClinical Care Pathway Framework, a nine-step iterative process. Step 1: define the aims of the online pathway; Step 2: define the functional units; Step 3: draft the clinical consultation; Step 4: expert review; Step 5: cognitive testing; Step 6: user-centred interface testing; Step 7: specification development; Step 8: software testing, usability testing and further comprehension testing; Step 9: piloting. We then applied the Framework to create a chlamydia online clinical care pathway (Online Chlamydia Pathway).Use of the Framework elucidated content and structure of the care pathway and identified the need for significant changes in sequences of care (Traditional: history, diagnosis, information versus Online: diagnosis, information, history) and prescribing safety assessment. The Framework met the needs of complex STI management and enabled development of a multi-faceted, fully-automated consultation.The Framework provides a comprehensive structure on which complex online care pathways such as those needed for STI management, which involve clinical services, public health surveillance functions and third party (sexual partner) management, can be developed to meet national clinical and public health standards. The Online Chlamydia Pathway's standardised method of collecting data on demographics and sexual behaviour, with potential for interoperability with surveillance systems, could be a powerful tool for public health and clinical management.UKCRC Translational Infection Research (TIR) Initiative supported by the Medical Research Council, eSTI2 Consortium (Grant Number G0901608)

Statistical design and analysis in trials of proportionate interventions: a systematic review

Background: In proportionate or adaptive interventions, the dose or intensity can be adjusted based on individual need at predefined decision stages during the delivery of the intervention. The development of such interventions may require an evaluation of the effectiveness of the individual stages in addition to the whole intervention. However, evaluating individual stages of an intervention has various challenges, particularly the statistical design and analysis. This review aimed to identify the use of trials of proportionate interventions and how they are being designed and analysed in current practice. Methods: We searched MEDLINE, Web of Science and PsycINFO for articles published between 2010 and 2015 inclusive. We considered trials of proportionate interventions in all fields of research. For each trial, its aims, design and analysis were extracted. The data synthesis was conducted using summary statistics and a narrative format. Results: Our review identified 44 proportionate intervention trials, comprising 28 trial results, 13 protocols and three secondary analyses. These were mostly described as stepped care (n=37) and mainly focussed on mental health research (n=30). The other studies were aimed at finding an optimal adaptive treatment strategy (n=7) in a variety of therapeutic areas. Further terminology used included adaptive intervention, staged intervention, sequentially multiple assignment trial or a two-phase design. The median number of decision stages in the interventions was two and only one study explicitly evaluated the effect of the individual stages. Conclusions: Trials of proportionate staged interventions are being used predominantly within the mental health field. However, few studies consider the different stages of the interventions, either at the design or the analysis phase, and how they may interact with one another. There is a need for further guidance on the design, analyses and reporting across trials of proportionate interventions