Scientific approaches to the deposit insurance scheme classification

У статті систематизовано існуючі підходи до класифікації систем гарантування вкладів, визначено сутність, переваги та недоліки різних видів систем гарантування вкладів. Досліджено місце вітчизняної системи гарантування вкладів у загальній класифікаціїThe article defines the existing approaches to the deposit insurance scheme classification, the essence, advantages and disadvantages of various types of deposit insurance schemes. It is also given the place of national deposit guarantee system in the general classificatio

Power to detect treatment heterogeneity for each individual outcome within the composite outcome.

<p>Power to detect that the treatment hazard ratio for outcome is different from the remaining two outcomes, as it hazard ratio varied from 0.70 to 2.0 (horizontal axis). The hazard ratios for the other two outcomes are kept constant at 0.70. Each outcome is represented by a different power curve.</p

Estimated robust covariance matrix Σ.

<p>Estimated robust covariance matrix Σ.</p

Composite outcome treatment heterogeneity test results for the POISE trial .

<p>Results of heterogeneity tests for the actual trial data.</p

Probability of difference between the estimated and true ORR≥0.10 in the unconditional setting scenario 3.

<p> <i>ORR: odds ratio reduction; PF: prognostic factor.</i></p

Simulation scenarios for the unconditional and conditional settings.

*<p> <i>Relative risk of having an outcome event for people receiving the experimental treatment (vs. control treatment) without the prognostic factor.</i></p>†<p> <i>Relative risk of having an outcome for people with vs. without the PF in the control group.</i></p

Probability of imbalance using absolute measure (D₁) with different trial sizes.

<p>Lines correspond to Pr (D₁≥d₁), where d₁ = 0.005 (hollow circle), 0.01 (triangle), 0.025 (cross), 0.05 (X), 0.10 (diamond), 0.15 (inverted triangle), and 0.20 (filled circle), from the top to the bottom, respectively. Top left: 25/arm, top right: 50/arm, middle left: 125/arm, middle right: 500/arm, bottom left: 1000/arm, bottom right: 2000/arm.</p

Probability of difference between the estimated and true ORR (deviation in either direction) in scenario 1, the unconditional setting, with 2000 patients per arm.

<p>Within each graph, lines correspond to Pr (|D_ORR|≥d₂), where d₂ = 0 (solid circle), 0.05 (bullet), 0.10 (little circle), 0.15 (square), 0.2 (diamond) and 0.25 (triangle), from top to bottom, respectively.</p

Probability of difference between the estimated and true ORR (deviation in either direction) in scenario 1, the unconditional setting, with 125 patients per arm.

<p>Within each graph, lines correspond to Pr (|D_ORR|≥d₂), where d₂ = 0 (solid circle), 0.05 (bullet), 0.10 (little circle), 0.15 (square), 0.2 (diamond) and 0.25 (triangle), from top to bottom, respectively.</p

Bias, simulation standard deviation (SD), coverage proportion and statistical power for the unadjusted and adjusted logOR, in scenario 1, the unconditional setting, with 2000 patients per arm.

<p>The unadjusted model is indicated by the dotted line with hollow circles, and the adjusted model is indicated by the solid line with filled circles.</p