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. 2019 Jan 30;57(2):e01277-18. doi: 10.1128/JCM.01277-18

TABLE 2.

Diagnostic accuracy of Visitect CD4 at a cutoff of ≤200 cells/mm3 from GLMMa

Factor Value (95% CI)
Inferenceb and heterogeneity
Model A
Model B
Model C
Sens Spec PPV NPV Sens Spec PPV NPV Sens Spec PPV NPV LR (χ2) P ICC
Overall 87.9 (76.3–99.5) 75.5 (69.9–81.2) 30.0 (17.9–42.1) 98.4 (97.2–99.7) 0.68
Operator χ2(4) = 6.65 0.156 0.69
    Lab tech 85.4 (69.0–100) 74.4 (66.8–82.1) 31.1 (17.5–44.7) 97.8 (94.8–100)
    Nurse 88.0 (74.8–100) 79.1 (73.1–85.1) 35.1 (20.9–49.3) 98.4 (96.6–100)
    Counselor 90.4 (75.4–100) 70.3 (62.3–78.3) 27.5 (15.1–39.7) 98.9 (96.7–100)
Sample type χ2(2) = 12.31 0.002 0.70
    Venous 91.1 (80.4–100) 73.1 (66.9–79.3) 28.9 (16.9–40.8) 98.9 (97.3–100)
    Finger prick 80.2 (62.0–98.3) 83.4 (76.8–90.1) 34.2 (19.1–49.3) 97.1 (93.9–100)
a

GLMM generalized through use of a logit link function and binomial distribution with a random intercept for test participant (n = 147). PPV and NPV estimates were determined from ordinary logit GLM with cluster robust standard errors. GLMM analyses would not converge reliably. Sens, test sensitivity; Spec, test specificity; PPV, positive predictive value; NPV, negative predictive value; ICC, intraclass correlation coefficient from random intercept GLMM; model A, unadjusted; model B, operator by test interaction and main effect (not shown); model C = Blood sample type by test interaction and main effect (not shown).

b

Likelihood ratio (LR) tests comparing nested less-constrained models (B and C) with model A.