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## CRC Press - books from this publisher (ISBNs begin with ) (1 of 15)

It then covers anti-drug antibody ADA assay development, optimization, validation, and transfer as well as the analysis of cut point, a key assay performance parameter in ADA assay development and validation. The authors illustrate how to apply statistical modeling approaches to establish associations between ADA and clinical outcomes, predict immunogenicity risk, and develop risk mitigation strategies.

They also present various strategies for immunogenicity risk control. The book concludes with an explanation of the computer codes and algorithms of the statistical methods. It could be a measure of efficacy e. In Quadrants 2 and 4, the decision as to which treatment is more or less cost-effective is relatively easy. This is the ideal scenario in which pharmaceutical companies would like to have their new drugs positioned. On the other hand, a less desirable scenario is where the new treatment is worse, but also more costly Quadrant 4. The values of the ICER can, however, be altered by changing parameters such as the price.

Health Economic Evaluation Concepts Reducing the price or increasing efficacy, if possible might be a strategy adopted so that the ICER can move into a different, more favourable quadrant, possibly at a reduced profit. A new treatment which has an ICER that falls into Quadrant 4 is unlikely to be considered as having a high chance of demonstrating value.

Even if the price was changed, the fact that the new treatment has poorer efficacy still needs to be addressed. Example 2. The new treatment is said to dominate the standard treatment. Most decision problems relating to the value of the ICER are concerned with Quadrants 1 and 3, and in particular Quadrant 1, where justification of value is often sought. This is the threshold ratio or amount in pounds or other currency that a payer would be prepared to pay for a new drug. Any ICER values calculated from data which are to the right of this line e.

Had the new treatment showed poorer efficacy compared with the standard e. At this new threshold, Treatment A is no lon- ger considered cost-effective because the point Z is above the line. There were several problems associated with assess- ing the uncertainty of the ICER.

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First, Y is a ratio and has some awkward statistical properties which can result in difficult statistical inference. The main approaches to addressing how to provide a measure of uncer- tainty around the ICER are. The INMB approach, which removes the difficulties of statistical inference. This is in itself not uncommon in clinical trials with efficacy end points. The negative value does not cause a problem for the interpretation of the clinical effect it just means that the treatment is worse.

However, cost-effectiveness ratios are not straightforward, and often point estimates average ratios are not close to the value 1 equivalent to mean differences of zero. The ratios could be negative, as stated in the previous paragraph. The interpretation can be even more difficult if there are several possible treatment comparisons.

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Moreover, it is also possible to have two cost-effectiveness ratios with the same values, both negative, but with completely opposite meanings. On the one hand, in Quadrant 2, the ICER tells us that the new treatment clearly dominates cheaper and more effective ; on the other hand, in Quadrant 3, it could be interpreted as being more expensive with worse effective- ness. One should therefore exercise caution when interpreting CIs for cost- effectiveness ratios. For these reasons, among others, the INMB is a preferred way of presenting the results of economic evaluation and the uncertainty around the estimates.

A ratio on its own is not interpreted as a net monetary benefit NMB the number is just a ratio of two quantities. The INMB approach avoids the need for conducting statistical tests for a ratio. The INMB appears to be the preferred choice for some countries to base a decision on cost-effectiveness.

This means that if one is not prepared to pay anything for a new treatment, the new treatment has no benefit valued in pounds.

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The slope of each line EA, EB is a measure of effectiveness e. QALYs or some other unit. Increasing costs are represented by the line shifting downwards and decreasing costs by the line shifting upwards. As in most regression models, the steepness of the slope has an interpre- tation. In this example, the steeper slope Treatment A has greater effec- tiveness compared with Treatment B. At the intersection of these two lines, the NMB for both treatments is equal:. The costs Chapter 4 are taken from several sources in the study cap- tured at the patient level. The main point here is that the cost for each patient is obtained from different compo- nent costs added together.

We will assume that Treatment A is the new treatment and B is the standard. TABLE 2. We use a fixed effects model for now.

## Sample size calculations in clinical research

A more complex model such as including centre effects in the case of a multicentre trial can be included, where centre effects are random i. An example of a mixed effects model will be shown later using an example from Manca et al. The two NMB models can be written for the two treatments as. The main objective in this example was to estimate the INMB. The approach to estimating the remaining quantities requires fitting a sepa- rate model for costs regress total costs or against treatment to estimate mean costs for each treatment and effects.

The INMB model is therefore. In this example, we first estimate all mean costs and effects using least squares means LSmeans and not the raw means.

The LSmeans may be different from the raw means, particularly in cases of missing data and imbalances in the number of patients per treatment group. First generate bootstrap samples, where each bootstrap sample consists of 12 observations. It is possible that the same patient may be randomly selected again.

Once we have sampled 12 patients with their entire data, we then run a regression model to estimate the LSmean costs and effects for that bootstrap sample.

Since we have bootstrap samples, we will have mean costs and effects for each of Treatments A and B. Therefore, we will have ICERs. Now, from the ICERs we can generate the required bootstrap statistics. For our purposes, we present the bootstrap mean ICER the mean of all bootstrap means with the empirical quan- tiles.

The quantiles are obtained by sorting the ICERs in order and reading off the values at the lower 2. The 2. Table 2. The data show considerable variability in the bootstrap estimates.