Finding associations in a heterogeneous setting: Statistical test for aberration enrichment

Mezlini, Aziz M.; Das, Sudeshna; Goldenberg, Anna

doi:10.1101/2020.03.23.002972

Cited by 3 publications

(3 citation statements)

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“…The baseline model mean absolute error (MAE) was 2375 steps per day for the baseline period it was trained on, and 2477 for the unseen control periods. The statistical significance of differences in step differentials between cases and controls was assessed by means of the Wilcoxon signed rank test, the t-test, and a novel statistical test for heterogeneous effects 22 .…”

Section: Discussionmentioning

confidence: 99%

Estimating the Burden of Influenza on Daily Activity at Population Scale Using Commercial Wearable Sensors

Mezlini

Shapiro

et al. 2021

Preprint

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The severity of viral infections can vary widely, from asymptomatic cases to complications leading to hospitalizations and death. Milder cases, despite being more prevalent, often go undocumented and their public health impact unaccounted for. We estimated the burden of influenza-like-illness (ILI) by leveraging the widespread use of commercial activity trackers. Analysing data from 15,382 US participants who reported ILI symptoms during the 2018–2019 flu season (before the COVID-19 pandemic) and who had high-density wearable sensor data at symptom onset, we estimated an overall nationwide reduction in mobility equivalent to 257 billion steps lost due to ILI symptoms. This finding reflects significant changes in routines, mobility, and employment and is equivalent to 15% of the active US population becoming completely immobilized for 1 day. Moreover, ~60% of this impact occurred among individuals who sought no medical care, who would otherwise be invisible to healthcare and public health reporting systems. We validated our measure against self-reported measures of disease severity. We believe this method has applications for public health, healthcare, and clinical research, from estimating costs of lost productivity at population scale, to measuring effectiveness of anti-ILI treatments, to monitoring recovery after acute viral syndromes, such as during long COVID.

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Section: Discussionmentioning

confidence: 99%

Estimating the Burden of Influenza on Daily Activity at Population Scale Using Commercial Wearable Sensors

Mezlini

Shapiro

et al. 2021

Preprint

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“…Estimating the Burden of Influenza-like Illness on Daily Activity Using Wearable Sensors effects. 21 All tests were 2-tailed; statistical significance was assessed after correction for multiple hypotheses (at false discovery rate-corrected P < .05). We also assessed the potential association of covariates, such as age, sex, race and Hispanic ethnicity, and disease symptoms, with ILI burden.…”

Section: Jama Network Open | Infectious Diseasesmentioning

confidence: 99%

Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors

et al. 2022

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IMPORTANCEThe severity of viral infections can vary widely, from asymptomatic cases to complications leading to hospitalizations and death. Milder cases, despite being more prevalent, often go undocumented, and their public health burden is not accurately estimated. OBJECTIVETo estimate the true burden of influenza-like illness (ILI) in the US population using a surrogate measure of daily steps lost as measured by commercial wearable sensors. DESIGN, SETTING, AND PARTICIPANTSThis cohort study modeled data from 15 122 US adults who reported ILI symptoms during the 2018-2019 influenza season (before the COVID-19 pandemic) and who had a sufficient density of wearable sensor data at symptom onset. Participants' minute-level step data as measured by commercial wearable sensors were collected from October 1, 2018, through June 30, 2019. Minute-level activity time series were transformed into day-level time series per user, indicating the total number of steps daily. MAIN OUTCOMES AND MEASURESThe primary end point was the number of steps lost during the period of 4 days before symptom onset (the latent phase) through 11 days after symptom onset (the symptomatic phase). The association between covariates and steps lost during this interval was also examined. RESULTSOf the 15 122 participants in this study, 13 108 (86.7%) were women, and the median age was 32 years (IQR, 27-38 years). For their ILI event, 2836 of 15 080 participants (18.8%) sought medical attention, and only 61 (0.4%) were hospitalized. Over the course of an ILI lasting 10 days, the mean cumulative loss was 4437 steps (95% CI, 4143-4731 steps). After weighting, there was an estimated overall nationwide reduction in mobility equivalent to 255.2 billion steps (95% CI, 232.9-277.6 billion steps) lost because of ILI symptoms during the study period. This finding reflects significant changes in routines, mobility, and employment and is equivalent to 15% of the active US population becoming completely immobilized for 1 day. Moreover, 60.6% of this reduction in steps (154.6 billion steps [95% CI, 138.1-171.2 billion steps]) occurred among persons who sought no medical care. Age and educational level were positively associated with steps lost. CONCLUSIONS AND RELEVANCEThese findings suggest that most of the burden of ILI in this study would have been invisible to health care and public health reporting systems. This approach has applications for public health, health care, and clinical research, from estimating costs of lost productivity at population scale, to measuring effectiveness of anti-ILI treatments, to monitoring recovery after acute viral syndromes such as during long COVID-19.

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“…In particular, the definition must keep differential expression as a feature of and only of tumors. Conversely, notice that we do not need to demand that the differential expression for any gene be a common feature of all or most of the tumors, since a tumor instance may be characterized by a gene expression that is peculiar just of it or a specific subset of tumors it belongs to (Mezlini, Das and Goldenberg, 2021).…”

Section: Gene Dysregulationmentioning

confidence: 99%

Perfect genetic biomarkers for cancer from a fresh view of gene dysregulation

Perez

Gonzalez

2022

Preprint

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Over the last decades, a host of gene expression profiles of tumor and normal tissue samples have been recorded by many microarray and RNA-Seq projects. Much of this big data awaits a full understanding and exploitation for translational cancer research. In particular, the pressing need to discover gene panels for diagnosis and therapy have not received yet a definitive answer. Here, we tackle such a question through rigorous mining of some of the currently available data. Our mining scheme rests on formal concept analysis and rough set theory and allows us to identify perfect gene panels for twelve of the solid tumors reported in the TCGA database. We dub them 'perfect gene panels' because they perfectly discriminate between normal and tumor samples. To wit, testing the gene expression profiles against a tumor or normal pattern provides no false positive and no false negative cases (i.e., 100% sensitivity and 100% specificity). Hence, perfect gene panels might be useful genetic markers for cancer diagnosis. Furthermore, we stress that such panels come in many flavors depending on the gene expression levels we choose as a pattern to check. For instance, there are perfect panels where a single gene over-expression signals a tumor and others where a single non-silenced gene is an indication of a tumor-free sample, just to mention two out of eight possible cases. Remarkably, some panels also suggest suitable genetic targets for therapeutic interventions, since they define normal samples by tuning the expression level of a single gene.

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Finding associations in a heterogeneous setting: Statistical test for aberration enrichment

Cited by 3 publications

References 79 publications

Estimating the Burden of Influenza on Daily Activity at Population Scale Using Commercial Wearable Sensors

Estimating the Burden of Influenza on Daily Activity at Population Scale Using Commercial Wearable Sensors

Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors

Perfect genetic biomarkers for cancer from a fresh view of gene dysregulation

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