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22.1 INTRODUCTION

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To select the optimal treatment for patients, clinical oncologists need to be skilled at critically evaluating data from clinical studies and interpreting these appropriately. Clinicians should also be proficient in the application of diagnostic tests, assessment of risk, and the estimation of prognosis. Equally, scientists involved in translational research should be aware of the problems and pitfalls in undertaking clinical studies. This chapter provides a critical overview of methods used in clinical research.

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22.2 TREATMENT

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22.2.1 Purpose of Clinical Trials

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Clinical trials are used to assess the effects of specific interventions on the health of individuals. Possible interventions include treatment with drugs, radiation, or surgery; modification of diet, behavior, or environment; and surveillance with physical examination, blood tests, or imaging tests. This section focuses on trials of treatment.

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Clinical trials may be separated conceptually into explanatory trials, designed to evaluate the biological effects of treatment, and pragmatic trials, designed to evaluate the practical effects of treatment (Schwartz et al, 1980). This distinction is crucial, because treatments that have desirable biological effects (eg, the ability to kill cancer cells and cause tumor shrinkage) may not have desirable effects in practice (ie, may not lead to improvement in duration or quality of life). Table 22–1 lists the major differences between explanatory and pragmatic trials.

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TABLE 22–1Classification of clinical trials.
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The evaluation of new cancer treatments usually involves progression through a series of clinical trials. Phase I trials are designed to evaluate the relationship between dose and toxicity and aim to establish a tolerable schedule of administration. Phase II trials are designed to screen treatments for their antitumor effects in order to identify those worthy of further evaluation. Phase I and Phase II trials are explanatory—they assess the biological effects of treatment on host and tumor in small numbers of subjects ...

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