Surrogate variables

Surrogate endpoints are usually used when it is not possible within the timeframe of the trial to measure true clinical benefit. Many examples exist as seen in Table 1.3. 

Unfortunately many treatments which have shown promise in terms of surrogate endpoints have been shown not to provide subsequent improvement in terms 

CHOI BASIC IDEAS IN CLINICAL TRIAL DESIGN Table 1.3 Surrogate variable and clinical endpoints 

Example 1.2 Bone Mineral Density (BMD) and Fracture Risk in Osteoporosis 

Chines and Meredith (2004) quote three clinical trials evaluating the effectiveness of alendronate, risedronate and raloxifene in increasing BMD and reducing fracture risk in osteoporosis. These treatments are seen to reduce fracture risk by similar amounts 47 per cent, 49 per cent and 46 per cent respectively, yet their effects on increasing BMD are somewhat different 6.2 per cent, 5.8 per cent and 2.7 per cent respectively. Drawing conclusions on the relative effectiveness of these treatments based solely in terms of the surrogate BMD would clearly be misleading. 

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In some situations where one or more of the latex properties are measured either directly or indirectly through their correlation with surrogate variables and where extreme nonlinearities such as the periodic generation of polymer particles does not occur, one can use much simpler modehng and control techniques. Linear transfer function-type models can he identified directly from the plant reactor data. Conventional control devices such as PID controllers or PID controllers with dead-time compensation can then be designed. If process data is also used to identify... 

Social value can be defined, in the context of humanitarian relief, as the amount of resources a community is willing to part with to save a human life. It acts as a surrogate variable standing in for price in a traditional supply chain. In contrast to price, however, it shapes the satisfied demand and not demand per se. That is, the satisfied demand increases if the social value imputed by a community increases. 

The nature of the performance metric, y, is determined by the characteristics of the specific process under analysis. Since we are particularly interested in analyzing situations where y is related to product or process quality, it is quite common to find systems where a categorical variable y is chosen to classify and evaluate their performance. This may happen due to the intrinsic nature of y (e.g., it can only be measured and assume qualitative values, such as good, high, and low ), or because y is derived from a quantization of the values of a surrogate continuous measure of performance (e.g., y = good if some characteristic z of the product has value within the range of its specifications, and y= bad, otherwise). 

DPD activity measured in PBMC is used as a surrogate for systemic DPD activity. DPD activity is normally distributed and highly variable among individuals (coefficient of variation of 33.9-46.6%) [43, 56-59]. DPD activity is undetectable in totally deficient patients. The majority of partially deficient patients had a DPD value < 30% of the mean in the normal population, and this value is considered the cut-off for patients at higher risk of toxicity. Among patients experiencing severe toxicity after 5-FU, 36-59% of them were deficient in DPD activity [43, 53, 60]. This suggests the involvement of other determinants in the susceptibility to 5-FU toxicity. The concordance between liver and PBMC DPD activity is modest [61], and normal DPD activity in PBMC was found in one patient with very depressed liver DPD activity who died because of 5-FU toxicities [44]. 

Endpoint. An indicator measured in a patient or biological sample to assess safety, efficacy, or another trial objective. Some endpoints are derived from primary endpoints (e.g., cardiac output is derived from stroke volume and heart rate). Synonyms include outcome, variable, parameter, marker, and measure. See surrogate endpoint in the text. Also defined as the final trial objective by some authors. 

If K is taken as a surrogate measure of the intensity of metal ion/surface reactions, then Jhe observation that K is not unique at adsorption densities below T suggests that surface site/adsorbate interactions are still of variable intensity ( Ga[Pg.181]

The variables used to build the necessary models need only to be sensitive to the critical processing parameters, in other words they can be surrogates of the actual variables (impurities, pH, etc.) being controlled. 

Efficacy variables are chosen according to the objectives of the trial. They may be the therapeutic effect itself (e.g. irradication of infection, healing of peptic ulcer) or a factor related to the therapeutic effect or some surrogate effect. 

There are of course practical considerations in clinical research. We may find patient recruitment difficult in single centre studies and this is one of the major drivers to multicentre and multinational trials. Alternatively, we may need to relax the inclusion/exclusion criteria or lengthen the recruitment period. Unfortunately, while each of these may indeed increase the supply of patients they may also lead to increased variability that in turn will require more patients. A second issue is the size of the CRD which, if it is too small, will require a large number of patients. In such circumstances we may need to consider the use of surrogate endpoints (Section S.3.3.2). Finally, the standard deviation may be large and this can have a considerable impact on the sample size - for example, a doubling of the standard deviation leads to a four times increase in the... 

It is useful to distinguish between variability, parameter uncertainty, and model uncertainty, since they require different treatment in risk analysis (Suter and Barnthouse 1993). Variability refers to actual variation in real-world states and processes. Parameter uncertainty refers to imprecise knowledge of parameters used to describe variability or processes in a risk model this can arise from many sources including measurement error, sampling error, and the use of surrogate measurements or expert judgment. Model uncertainty refers to uncertainty about the structure of the risk model, including what parameters should be included and how they should be combined in the model equations. 

Treatment effects on surrogate endpoints therefore do not necessarily translate into treatment effects on clinical endpoints and the validity of the surrogate depends not only on the variable itself but also on the disease area and the mode of action of the treatment. Establishing new valid surrogates is very difficult. Fleming and DeMets conclude that surrogates are extremely valuable in phase II proof of concept studies but they question their general use in phase III confirmatory trials. 

These studies suggest that the variability between tumor and germline genotype at the SNP level is minimal and the germline genome can be used as a surrogate to assess polymorphism status in the tumor. However, these studies were all performed on small sample numbers and do not represent all tumor types. 

Stresau, Development of the VARICOMP Method, Expansion of Applicability (To Determine Detonation Transfer Probabilities with Reduced Dependence Upon System Variables). Part 1. Calibration and Characterization of PBXN-5 as Design Explosive , R. Stresau Laboratory, Inc, Spooner, Wis (27 Dec 1972) [Rept RSLR 72-7 for the Naval Weapons Center, China Lake, Calif] 3) Ibid, "Ibid. Part 3. Calibration of RDX/Calcium Stearate Binary System for Use as VARICOMP Surrogates , Ibid (14 Feb 1973) [Rept RSLR 73-1 for the Naval Weapons Center, China Lake, Calif]... 

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