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Third, in the absence of a gold standard, we calculated the sensitivity and specificity of each spirometric criterion, using the alternative cut-off as the reference standard.40 Fourth, regression selleck chemical analyses were performed using age, sex, pack-years of smoking and NS-SEC as independent variables with airflow obstruction as outcome. Current smoking status could not be entered in the same model as pack-years due to significant collinearity. The dependent variable based on FTs had four categories: non-obstructed, stage I, stage II and stage III+. The LLN-derived outcome had three categories: non-obstructed, stage I and stage II. In each case, multinomial logistic regression was used to estimate relative risk ratios (RRRs), with non-obstructed as the reference category. Multinomial logistic regression generalises logistic regression to outcomes with more than two possible discrete outcomes. The RRR is interpreted as the relative risk of one outcome in relation to the reference category for a specified category of an independent variable compared with the reference.41 42 Diagnosed COPD was analysed as a binary outcome (not reported/reported): TAK-632 logistic regression was therefore used to estimate ORs.39 41 The overall association for independent variables with >2 categories was computed using the adjusted Wald test. The likelihood ratio test was used to estimate the statistical significance of interaction terms: non-significant terms were excluded, Ipatasertib mouse and models refitted with only the main effects. Fifth, to examine risk factors associated with possible underdiagnosis, a four-category outcome variable was created combining diagnosed COPD and spirometric criteria as follows: (1) neither diagnosed nor spirometrically defined obstruction; (2) physician-diagnosed COPD but no obstructive spirometry; (3) spirometrically defined but no diagnosed COPD and (4) both diagnosed and obstructive spirometry.43 FT and LLN cut-offs were analysed separately. RRRs generated from multinomial logistic regressions were used to examine associations between the same set of risk factors listed above and the composite dependent variable. Participants with missing values on covariates were excluded from relevant analyses. Tests of statistical significance were based on two-sided probability (p