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Original Research

Cost–Utility Analysis of a Latanoprost Cationic Emulsion (STN1013001) versus Other Latanoprost in the Treatment of Open-Angle Glaucoma or Ocular Hypertension and Concomitant Ocular Surface Disease in Germany

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Pages 323-337 | Published online: 09 Feb 2022

Figures & data

Figure 1 Markov model.

Abbreviation: OAG/OHT, open-angle glaucoma or ocular hypertension.
Figure 1 Markov model.

Table 1 Unit Cost for Healthcare Resources, Utility and Disutility Values (Costs in €2020)

Figure 2 (A) Markov trace for the hypothetical cohort of patients on STN1013001. (B) Markov trace for the hypothetical cohort of patients on latanoprost.

Abbreviation: OAG/OHT, open-angle glaucoma or ocular hypertension.
Figure 2 (A) Markov trace for the hypothetical cohort of patients on STN1013001. (B) Markov trace for the hypothetical cohort of patients on latanoprost.

Table 2 Costs per Patient and Cost–Utility Analysis (Costs in €2020)

Figure 3 One-way sensitivity analysis: results concerning the first 10 parameters of the Markov model that causes the widest variations in base case ICUR (€2020).a,b

Notes: aBase case ICUR STN1013001: strongly dominant (SE sector of the cost-effectiveness plane); bY and X-axes intersect at the baseline ICUR.
Abbreviations: ICUR, Incremental Cost–Utility Ratio; LL 95% CI, lower limit 95% confidence interval; OAG/OHT, open-angle glaucoma or ocular hypertension; SE, South-East; UL 95% CI, uUpper limit 95% confidence interval.
Figure 3 One-way sensitivity analysis: results concerning the first 10 parameters of the Markov model that causes the widest variations in base case ICUR (€2020).a,b

Figure 4 Probabilistic sensitivity analysis. Cost-effectiveness plane (10,000 out of 10,000 Monte Carlo iterations reported) (ΔC in €2020).a,b

Notes: aBase case ICUR STN1013001: strongly dominant (SE sector of the cost-effectiveness plane); bNumber of Monte Carlo iterations (%) for each sector of the cost-effectiveness plane: SE=10,000 (100.00%).
Abbreviations: ΔC, incremental cost; ΔQALYs, incremental quality-adjusted life years; ICUR, Incremental Cost–Utility Ratio; SE, South-East.
Figure 4 Probabilistic sensitivity analysis. Cost-effectiveness plane (10,000 out of 10,000 Monte Carlo iterations reported) (ΔC in €2020).a,b

Figure 5 Probabilistic sensitivity analysis. Cost-effectiveness acceptability curve (1000 out of 1000 threshold values reported) (€2020).a

Notes: aBase case ICUR STN1013001: strongly dominant (SE sector of the cost-effectiveness plane).
Abbreviations: CEAC, cost-effectiveness acceptability curve; ICUR, Incremental Cost–Utility Ratio; SE, South-East.
Figure 5 Probabilistic sensitivity analysis. Cost-effectiveness acceptability curve (1000 out of 1000 threshold values reported) (€2020).a

Figure 6 Probabilistic sensitivity analysis. Cost-effectiveness acceptability frontier (1000 out of 1000 threshold values reported) (€2020).a,b

Notes: aBase case ICUR STN1013001: strongly dominant (SE sector of the cost-effectiveness plane); bCEAF: STN1013001 is the optimal strategy from a threshold value of €0.00 onwards.
Abbreviations: CEAF, cost-effectiveness acceptability frontier; ICUR, Incremental Cost–Utility Ratio; SE, South-East.
Figure 6 Probabilistic sensitivity analysis. Cost-effectiveness acceptability frontier (1000 out of 1000 threshold values reported) (€2020).a,b