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- Haghighi, Mona, et al.
(författare)
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A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.
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| 2. |
- Porsbjerg, Celeste M., et al.
(författare)
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Association between pre-biologic T2-biomarker combinations and response to biologics in patients with severe asthma
- 2024
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Ingår i: Frontiers in Immunology. - : Frontiers Media S.A.. - 1664-3224. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: To date, studies investigating the association between pre-biologic biomarker levels and post-biologic outcomes have been limited to single biomarkers and assessment of biologic efficacy from structured clinical trials.Aim: To elucidate the associations of pre-biologic individual biomarker levels or their combinations with pre-to-post biologic changes in asthma outcomes in real-life.Methods: This was a registry-based, cohort study using data from 23 countries, which shared data with the International Severe Asthma Registry (May 2017-February 2023). The investigated biomarkers (highest pre-biologic levels) were immunoglobulin E (IgE), blood eosinophil count (BEC) and fractional exhaled nitric oxide (FeNO). Pre- to approximately 12-month post-biologic change for each of three asthma outcome domains (i.e. exacerbation rate, symptom control and lung function), and the association of this change with pre-biologic biomarkers was investigated for individual and combined biomarkers.Results: Overall, 3751 patients initiated biologics and were included in the analysis. No association was found between pre-biologic BEC and pre-to-post biologic change in exacerbation rate for any biologic class. However, higher pre-biologic BEC and FeNO were both associated with greater post-biologic improvement in FEV1 for both anti-IgE and anti-IL5/5R, with a trend for antiI-IL4R alpha. Mean FEV1 improved by 27-178 mL post-anti-IgE as pre-biologic BEC increased (250 to 1000 cells/mu L), and by 43-216 mL and 129-250 mL post-anti-IL5/5R and - anti- IL4R alpha, respectively along the same BEC gradient. Corresponding improvements along a FeNO gradient (25-100 ppb) were 41-274 mL, 69-207 mL and 148-224 mL for anti-IgE, anti-IL5/5R, and anti-IL4R alpha, respectively. Higher baseline BEC was also associated with lower probability of uncontrolled asthma (OR 0.392; p=0.001) post-biologic for anti-IL5/5R. Pre-biologic IgE was a poor predictor of subsequent pre-to-post-biologic change for all outcomes assessed for all biologics. The combination of BEC + FeNO marginally improved the prediction of post-biologic FEV1 increase (adjusted R-2: 0.751), compared to BEC (adjusted R-2: 0.747) or FeNO alone (adjusted R-2: 0.743) (p=0.005 and <0.001, respectively); however, this prediction was not improved by the addition of IgE.Conclusions: The ability of higher baseline BEC, FeNO and their combination to predict biologic-associated lung function improvement may encourage earlier intervention in patients with impaired lung function or at risk of accelerated lung function decline.
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