Thus, in addition to indexes of left ventricular function determined on initial evaluation, serial long-term changes in systolic function identify patients likely to develop symptoms and require operation. Patients have a higher risk of symptomatic deterioration if there is progressive change in end-systolic dimension or resting ejection fraction during the course of serial studies.
The log-rank test is frequently used to compare survival curves. While sample size estimation for comparison of binomial proportions has been adapted to typical clinical trial conditions such as noncompliance, lag time, and staggered entry, the estimation of sample size when the log-rank statistic is to be used has not been generalized to these types of clinical trial conditions. This paper presents a method of estimating sample sizes for the comparison of survival curves by the log-rank statistic in the presence of unrestricted rates of noncompliance, lag time, and so forth. The method applies to stratified trials in which the above conditions may vary across the different strata, and does not assume proportional hazards. Power and duration, as well as sample sizes, can be estimated. The method also produces estimates for binomial proportions and the Tarone-Ware class of statistics.
A surrogate endpoint in a cardiovascular clinical trial is defined as endpoint measured in lieu of some other so-called 'true' endpoint. A surrogate is especially useful if it is easily measured and highly correlated with the true endpoint. Often the 'true' endpoint is one with clinical importance to the patient, for example, mortality or a major clinical outcome, while a surrogate is one biologically closer to the process of disease, for example, ejection fraction. Use of the surrogate can often lead to dramatic reductions in sample size and much shorter studies than use of the true endpoint. We discuss several problems common in trials with surrogate endpoints. Most important is the effect of missing data, especially in the face of informative censoring. Possible solutions are the assignment of scores or formal penalties to missing data.
The relationship between energy intake, physical activity, and body fat was investigated in the baseline visit of 2379 black and white girls aged 9-10 y enrolled in the National Heart, Lung, and Blood Institute Growth and Health Study. Three-day food records, three-day physical activity diaries, physical-activity-patterns questionnaires, and an assessment of the number of hours of television and video watched were obtained. Multivariate-regression analyses showed that age, the number of hours of television and video watched, the percent of energy from saturated fatty acids, and the activity-patterns score best explained the variation in body mass index and sum of three skin-fold-thickness measurements for black girls. The best model for white girls included age, the number of hours of television and video watched, and the percent of energy from total fat. These results indicate that body fatness is related to energy intake and expenditure in both black and white girls. Longitudinal studies will help assess the value of these variables in predicting changes in body fat.
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