Table 52.1  Levels of interventional evidence

Level Description

1Randomized clinical trial with type 1 error ≤ 0.05 and type 2 error ≤ 0.20

2Randomized clinical trial with higher type 1 and/or type 2 error

3Nonrandomized clinical trial

4Case series

5Case report

COST–BENEFIT ANALYSIS

Cost–benefit analysis compares the dollars expended for an intervention with the dollars gained from the intervention. For example, let us assume that a VEGF inhibitor treatment for neovascular AMD has direct medical costs of $10 000/year. The treatment may greatly decrease the human burden of suffering, but the key factor here is the costs expended and the costs saved. On the gain side, the treatment may allow 40% of treated individuals to reach a vision of 20/40, thereby allowing many who might be on disability to obviate a disability scenario. Thus, the weighted disability gain per patient might be $600 per month, or $7200/year. Furthermore, let us assume that the treatment allows 20% of treated individuals to work and contribute to the gross domestic product (GDP: the sum of all final products and services produced annually in the USA), resulting in a weighted gain per patient of $700/month, or $8400/year. With this societal cost perspective, these disability and unemployment costs saved ($15 600/year) exceed the cost of treatment ($10 000/year) by $5600 a year, indicating a positive cost–benefit for the intervention.

Most often, cost–benefit analysis fails to take into account the quality of life associated with a health state, although a methodology of utility analysis, known as willingness to pay, does allow quantification of quality-of-life changes by assessing how much of a person’s salary, savings, retirement plan, and so forth, a person would be willing to pay in return for perfect health. Nonetheless, this form of utility analysis to measure quality of life is not often used since those who have greater assets are often willing to pay proportionately more for the same therapeutic intervention than those with minimal assets.5

COST-EFFECTIVENESS ANALYSIS

Cost-effectiveness analysis measures the resources expended for a given endpoint. The endpoint may be years of life gained, years of good vision gained, disability-free years, level of vision gained (such as 20/25), or any other endpoint.

Confusion has arisen with this term since certain authors use costeffectiveness analysis synonymously with cost–utility analysis.11–13 Others reserve cost–utility analysis for those studies which measure the improvement in length of life and/or quality of life for the resources expended using dollars expended per quality-adjusted life-year ($/ QALY). The present authors agree that the term cost–utility analysis should be used to describe the latter, rather than cost-effectiveness analysis.

Cost-effectiveness analysis

Tengs et al.14 have produced an excellent treatise on 500 life-saving treatments published in the form of cost-effectiveness analysis. In each of these instances the analysis was performed using cost per year of life saved. Adjusting the 1993 US dollars from the study of Tengs et al.14 to year-2007 dollars reveals that the median cost to save a year of life with a medical intervention is approximately $28,350, while that for injury reduction is $72,500, and that for toxin control is $4 .2 million. Injury

reduction includes entities such as seat belt use and setting speed limit, while toxin control includes setting maximum levels of arsenic in water and benzene emissions in tire plants. Needless to say, medical interventions appear to be far more cost-effective than injury reduction interventions and efforts at toxin control.

COST–UTILITY ANALYSIS

Cost–utility analysis is the most sophisticated form of pharmacoeconomic analysis in that it takes into account the improvement in quality of life and/or length of life conferred by an intervention for the resources expended. The increase in longevity conferred by an intervention can generally be ascertained from evidence-based data in the literature. Measurement of the quality of life conferred by an intervention is more difficult.

Quality of life: Function-based instruments

Numerous instruments have been used to assess the quality of life associated with a health state. Among the general medical instruments are the Medical Outcomes Short-Form-36 (SF-36),15 the Sickness Impact Profile (SIP),16 and the Quality of Well-Being Scale.17 Included among the ophthalmic instruments are the VF-1418 and the NEI-25.19 The general medical instruments are generally not applicable to ophthalmic disease health states and the ophthalmic tests are not applicable to medical disease health states.5 All of these tests primarily measure the function associated with a given health state.5

Quality of life: Preference-based instruments

Utility analysis provides a measure of the quality of life associated with a health state and is believed to be more all-encompassing than function-based quality-of-life measures in that it incorporates function, psychological overlay, caregiver availability, socioeconomic conditions, occupational aspects, and virtually every other aspect that comprises quality of life.20–22 The time tradeoff methodology appears to have the best construct validity and correlates most closely with visual acuity, particularly that of the better-seeing eye.5,8,23–27

A time tradeoff utility value is calculated by asking two questions:

1.  How many additional years do you expect to live?

2.  How many of those remaining years, if any, would you be willing to trade for an intervention that would immediately return your health state to a normal health state on a permanent basis?

The utility is then calculated by subtracting the proportion of years traded from 1.0. For example, if a person with diabetes and 20 theoretical remaining years of life is willing to trade 3 of the 20 years in return for a normal health state, the utility is 1.0 − 3/20 = 0.85. Because patients can have a preference to trade time for better health or trade no time and remain in the same health state, utility analysis is referred to as a preference-based quality-of-life instrument.

Utilities associated with visual acuity in the better-seeing eye are shown in Table 52.2, while utilities associated with general medical conditions are shown in Table 52.3. The ocular utilities are directly comparable with the general medical utilities. Of note is the fact that ocular utilities appear to correlate more with the visual acuity in the better-seeing eye, rather than the underlying cause of visual loss.24

Utility gain

The improvement from one health state to another after an intervention can also be measured using utility analysis. For example, if the vision in the better-seeing eye improves from 20/200 (utility of 0.66) to 20/25 (utility of 0.87), the utility improvement is 0.21 (0.87 − 0.66). Decision analysis is often helpful in amalgamating the benefits of a drug with its adverse events, as well as the incidences of adverse events, to demonstrate more effectively the utility associated with use of a drug (Figure 52.1).

Words Last The • 6 section

and medicine based• 52-Valuechapter

pharmacoeconomics

Table 52.2  Time tradeoff utilities associated with visual acuity in the better-seeing eye8,23,27

OU = both eyes

HM = hand motions

LP = light perception

NLP = no light perception

Table 52.3  Time tradeoff utilities for general medical health states8

TURP, transurethral resection of the prostate.

Value gain

The improvement in utility can be multiplied by the years of duration of treatment benefit to arrive at the total value conferred by an inter­ vention.5,10 This value is measured in QALYs gained. A utility improvement­ of 0.21 for 10 years (0.21 × 10) equals 2.1 QALYs gained. Value, however, can also be measured in percentage gain. For most ophthalmologic interventions value is conferred by improvement in quality of life rather than gain in length of life. Thus, the per­ centage gain for ophthalmic interventions is equivalent to quality- of-life gain.

Figure 52.1  Example of decision analysis showing the resultant utilities associated with the use of a drug (utility = 0.932) and the control group not treated with the drug (utility = 0.893). Utilities for each of the adverse event outcomes are located to the right of the triangular terminal nodes and the incidence of each outcome is listed, below its respective branch. Treatment reduces the incidence of macular edema from 40% to 20%, resulting in a utility gain of (0.9320 − 0.8920) = 0.04.

The tree is read from left to right. The square is a decision node when one must decide whether to treat or not treat. The O is a chance node, for which the incidences are listed under the arms of the subtrees. The triangle indicates a terminal node, to the right of which are located the utilities associated with each of the possible outcomes.

Value trumps cost

All patients should want and deserve the intervention which confers the greatest value. Only when the value conferred by interventions is the same should cost be a consideration; in this instance the intervention which is least expensive is the preferred intervention. At the current time, the VEGF inhibitors appear to deliver the greatest value among interventions for neovascular AMD (Table 52.4).

A good example of the clinical benefit of value-based medicine, pharmacoeconomic analyses is demonstrated by the head-to-head comparison of intravitreal pegaptanib and photodynamic therapy (PDT) with verteporfin for the treatment of classic, subfoveal, neovascular AMD. The final vision in the Treatment of AMD with PDT (TAP) trial was 20/160 + 1 in the treatment cohort versus 20/320 + 1 in the control group, while in the pegaptanib VEGF inhibition study (IS) trial the final vision was 20/126 − 1 in the treatment cohort versus 20/200 + 1 in the control group.28 Comparing these results, much less the associated adverse events and incidences of the adverse events, is virtually impossible without a value-based medicine analysis. Value-based data clearly demonstrate that PDT confers the greatest value with an 8.1% improvement in quality of life, while pegaptanib confers inferior value with a 5.9% improvement in quality of life (Table 52.4).

Cost–utility ratio

The total number of QALYs gained from an intervention is then amalgamated with the costs associated with the intervention to arrive at a cost–utility ratio, which is measured using dollars expended per QALY gained. The cost–utility of an intervention can be compared with that of any other intervention in health care. Thus, the value of all health care interventions given for the resources expended can be compared on an equal playing field.

Cost-effectiveness standards

An intervention is typically thought to be cost-effective if its cost–utility ratio is < $100 000/QALY and very cost-effective if it costs <$50 000/ QALY.5 World Health Organization standards suggest <$40 000/QALY as very cost-effective and < $120 000/QALY as cost-effective.29 In general, despite the fact that we use the cost–utility ratio to assess costeffectiveness, we speak of an intervention as being cost-effective, rather