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Strengths of the present study included the design with cases identified in a large population based clinical register and the use of matched controls in order to minimize confounding of the socioeconomic variables, and also the baseline determination of outcome variables at the age of one year before breast cancer diagnosis for the cases.
Those with an EF <55%% were excluded from the analysis to minimize confounding of the relationship between HDLVEF and clinical outcomes.
To minimize confounding of potential predictive markers by clinicopathological characteristics, relapsing and non-relapsing patients were closely matched by ER/progesterone receptor (PR) status, nodal status, tumor grade, patient age at diagnosis, and tumor size.
To minimize confounding of BMI and mortality due to smoking, we restricted analyses to never smokers, although this resulted in only 54 normal weight, 109 overweight, and 84 obese participants.
On the other hand, the homogeneity of the population concerning socio-economic aspects, the supportive environment, and the good relationships between colleagues are considered to minimize confounding of the different factors.
To minimize confounding of biomarker associations with ARDS by clinical variables, such as severity of illness, cases and controls were one-to-one matched for severity of illness (APACHE II, within one point), age (within 10 years), gender (one-to-one match) and number of non-pulmonary organ failures (within one).
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The matching process was successful in achieving well-balanced study groups, and balance between the two study groups was achieved across IRP genotype subgroups (IRP+, IRP−), thus minimizing confounding of any biomarker effects (IRP*treatment interaction) by baseline differences.
To the extent possible, subjects were instructed to maintain the same dosages of established lipid-lowering agents throughout the double-blind treatment period to minimize confounding the evaluation of the lipid-lowering effects of colesevelam versus placebo.
Therefore, in order to minimize confounding by distribution of cell types, we performed case-control and survival analyses using 336 EOC cases and 398 controls, accounting for cell type associations to better understand the role of blood-based DNAm in ovarian cancer risk and survival.
Our model fitting procedure is iterative in order to minimize confounding between the biological variation of interest and the other sources of variation.
Our purpose was to establish an optimized pipeline to collect biological samples for blood transcriptomics to minimize confounding signals such as artifacts of sample preparation and processing while maximizing reproducibility and sensitivity.
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