Compound endpoints in clinical trials

Compound endpoints in clinical trials

250 Abstracts with the laser treatment protocol. The task of the MPS Reading Center is to interpret the stereo fundus photographs and transform these...

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Abstracts with the laser treatment protocol. The task of the MPS Reading Center is to interpret the stereo fundus photographs and transform these visual images of complicated spatial and color patterns into a meaningful set of summary gradings suitable for computer assisted data analysis. The MPS Reading Center has developed a set of Standard Photographs and measurement techniques that not only ascertain compliance with the protocol, but also provide important descriptive information during the treatment and follow-up phases of the study. The principles behind these practices may be easily extended to apply to other opthalmologic and medical conditions in which shapes and patterns are evaluated and monitored over time.

Compound Endpoints in Clinical Trials Michael Terrin a n d Sandra F o r m a n

The TIM! Study Group, Maryland Medical Research Institute, Baltimore, Maryland (P08M) The vast majority of diseases result in not one but a variety of undesirable outcomes. Death has no equal as a definitive endpoint, but mortality is often low in clinical trial populations and often not the greatest cause of human suffering. To improve power to recognize treatment benefit, and to better identify morbidity as well as mortality reduction, clinical trial designers may be attracted to compound endpoints composed of the union of a variety of morbid events and death. Depending upon the correlation of each morbid event with other morbid events and death, combining endpoints to increase event rates may paradoxically rob a study of power. This pitfall in design is illustrated with projections developed to aid selection of study endpoints in planning for Thrombolysis in Myocardial Infarction (TIMI). Criteria for selecting endpoints for combination to avoid this pitfall are recommended. Systolic Hypertension in the Elderly Program (SHEP) Pilot Study: Predictors of Cardiovascular (CV) Events D a v i d Siegel, N a n c y Lazarus, D e n n i s Black, S t e p h e n Hulley, Lewis Kuller, a n d H. Mitchell Perry UC San Francisco, San Francisco, California (P-09M) We report on the predictors of disease events in the pilot study for SHEP, a randomized, doubleblind, placebo-controlled trial of drug therapy for isolated systolic hypertension (ISH) in men and women ~ 60 years old. We studied 551 participants with pretreatment systolic BP ~ 160 and diastolic BP <: 90 mm Hg. Their mean age was 72 years, 37% were male, 82% were white, and 24% had attended college. The vital status of all 551 participants was known at the end of the follow-up, an average of 34 months after entry; there were 39 deaths and 66 CV events, including 18 strokes and 20 episodes of myocardial infarction or sudden death (MI/SD). Logistic analysis revealed that all cause mortality was significantly (p < 0.05) associated with age [standardized odds ratio (OR) = 1.86], less than college education (OR = 2.94) and pulse pressure (OR = 1.39); CV events with age (OR = 1.34), less than college education (OR = 2.56) and smoking (OR = 2.73); stroke with age (OR = 1.62); M1/SD with smoking (OR = 3.63). Diastolic BP had a "U" shaped association with mortality, CV events, and MI/SD. Many risk factors remain important predictors of CV events in the elderly. A larger trial of the efficacy of treatment for ISH is currently underway.

A Technique for Evaluating Clinical Equivalence A n n S u m m e r f e l t , Frank F u n d e r b u r k , J e r o m e Levine, a n d William T. C a r p e n t e r

Maryland Psychiatric Research Center, Baltimore, Maryland (P-10M) A common question in clinical research is whether two or more treatment regimens are equally effective. Unfortunately, the typical null hypothesis is not designed to test for equivalence. In particular, under the usual approach one can never accept the hypothesis of no difference between treatments. One approach to this problem is to specify a clinically meaningful difference between treatments and to test for such a difference. The alternative hypothesis in such a case is that the groups are clinically equivalent. Thus, the null hypothesis may be rejected in favor of the alternative hypothesis of clinical equivalence (i.e., that the treatment groups differ by less than a specified amount). Our article presents a simple data analytic technique that allows the clinical