Small Clinical Trials Issues and Challenges (2001) / Chapter Skim
Currently Skimming:
Appendix A Study Methods
Appendix A Study Methods
Pages 103-129
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 103... ...
Appendixes
|
From page 105... ...
The basic subject search terms included Bayesian, sequential analysis, decision analysis, meta-analysis, and confidence profile method. These are the methods of analysis that the committee had been charged with assessing as they relate to the conduct of clinical trials with small sample sizes.
|
From page 106... ...
During the first committee conference call, committee members agreed to identify and submit to Institute of Medicine staff references (included or not included in the preliminary list) that they believe should be reviewed by the entire committee.
|
From page 107... ...
Methods including randomized clinical trials, sequential clinical trials, meta-analysis, and decision analysis were considered in terms of their potentials and their problems. Ethical considerations and statistical evaluations and comparisons were also covered.
|
From page 108... ...
The conference was carried on a live audiocast for the benefit of those who were unable to attend. Two weeks before the conference date, a postcard announcement of the live audiocast together with the conference agenda and a one-page description of the conference were sent to invited individuals who had not registered to attend and a wider national audience unlikely to attend.
|
From page 109... ...
The clinical trials group is primarily responsible for the content of Chapter 2, which focused on the design of clinical trials with various numbers of small sample sizes. The biostatistical group is primarily responsible for Chapter 3, which focused on the statistical analyses applied to clinical trials with small sample sizes.
|
From page 110... ...
CONFERENCE AGENDA "Future Directions for Small n Clinical Research Trials" National Academy of Sciences Lecture Room 2101 Constitution Avenue, N
|
From page 111... ...
Zucker, New England Medical Center Break Development and Monitoring of Small n Clinical Trials 3:15 3:45 4:15 4:45 5:00 111 Research Needs in Developing Small n Clinical Trials Helena Mishoe, National Heart, Lung and Blood Institute, National Institute of Health Regulatory Issues with Small n Clinical Research Trials lay Siegel, Office of Therapeutics Research and Review, Food and Drug Administration Ethical and Patient Information Concerns: Who Goes First? Lawrence K
|
From page 112... ...
C Falls Church, VA Lakshmi Vishnuvajjala Food and Drug Administration Rockville, MD Teng Weng Food and Drug Administration Rockville, MD Gang Zheng National Heart, Lung, and Blood Institute Bethesda, MD
|
From page 113... ...
The Type I error describes the long-run proportion of times that the null hypothesis will be so rejected, assuming in each case that it is actually true. The Neyman-Pearson approach also considers various explicit alternative hypotheses and for each one quantifies the Type II error probability, which is the long-run proportion of times that the decision rule will fail to reject the null hypothesis when the given alternative is true.
|
From page 114... ...
The statistical power of a hypothesis test is the probability complementary to the Type II error probability, evaluated at a given alternative, and generally depends on the decision criterion for rejection of the null hypothesis, the probability model for the alternative hypothesis, and the sample size. The Type II error probability (or, equivalently, the statistical power)
|
From page 115... ...
In the simplest case, the fundamental result of Bayes's theorem states that the posterior odds in favor of one hypothesis relative to another hypothesis is given by the prior odds multiplied by the likelihood ratio. Thus, the Bayesian paradigm reveals the role of the weight of evidence as measured by the likelihood ratio (or the so-called Bayes factor in more complicated settings)
|
From page 116... ...
If the patient or his physician believes that there is a 10 percent chance of disease before the test results are known, then the prior odds of 0.10/0.90 = 1/9 multiplied by the likelihood ratio of 18 yields posterior odds of 2, or an a posterior) degree of belief of 2/3 in favor of disease.
|
From page 117... ...
For simplicity of notation, we do not index the p sets of measurements for each of the k endpoints, only for a single endpoint. Note that in practice, the endpoints may be correlated and the actual confidence level provided by the method will be somewhat higher than the estimated value.
|
From page 118... ...
If si is the median of the ni experimental measurements in spaceflight i, then the upper prediction limit can be computed with probability approximately oc=/2, and the lower limit is simply the value of the l = (n - ~ + lath ordered measurement.
|
From page 119... ...
Normal Distribution For the case of a normally distributed endpoint, one can derive an approximate prediction limit by noting that the experiment-wise rate of false positive results oh can be achieved by setting the individual endpoint rate of false-positive results or equal to 0c = [1-~1-~*
|
From page 120... ...
The approximate prediction limits presented above, which assume independence, will slightly overestimate the true values that incorporate the dependence, but are much easier to compute. tognormal Prediction Limits Note that it is often tempting to attempt to bring about normality by transforming the raw data and then applying the above method to the transformed data.
|
From page 121... ...
percentage point of the normal distribution. In this context, the prediction limit represents an upper bound on the sum of the measurements of the Experimental subjects in replicate i and or is defined in the first equation in the section Normal Distribution.
|
From page 122... ...
The mixed-effects regression model for the ni x 1 response vector y for Level - 2 unit i (subject or cluster) can be written as Yi = WiOt + Xipi + Pi i =1,...N, where Wi is a known ni x p design matrix for the fixed effects, or is the p x 1 vector of unknown fixed regression parameters, Xi is a known ni x r design matrix for the random effects, and ,13i is the r x 1 vector of random individual effects, and pi is the ni x 1 error vector.
|
From page 123... ...
Let a be a prespecified nonnegative integer, and let X be a count variable observed pretreatment. Given an unobservable nonnegative random variable 0, which varies in an arbitrary and unknown manner in the population of subjects under study, assume that X has a Poisson distribution with mean 0.
|
From page 124... ...
Unless there is an empirical basis for assuming the conjugate prior, however, the posterior mean of ~ given X will not be linear. (This simple fact is what separates empirical Bayes methods from subjective Bayes methods, in which a gamma prior would often be assumed, gratuitously in many cases.)
|
From page 125... ...
For example, the temporal effect on accident proneness for those drivers with one or more accidents in the baseline year can be estimated by the observed total number of accidents in the next year among those with one or more accidents in the baseline year divided by the total number of accidents suffered by all those with two or more accidents in the year baseline (and the denominator is an unbiased predictor of the numerator under the null hypothesis c1 is equal to 11. The above theory is called semiparametric because of the parametric Poisson assumption and the nonparametric assumption concerning the distribution of 0.
|
From page 126... ...
, the allocation variable X was a baseline measurement of the total serum cholesterol level. The auxiliary variable X' was another measurement of the total serum cholesterol level taken 4 months after all subjects were given dietary recommendations but before randomization to cholestyramine or placebo.
|
From page 127... ...
Two fundamental empirical Bayes identities for Poisson random variables due to Robbins can be stated: First-order empirical Bayes identity for Poisson variables: EM= E~Xu(X-1~] , where the expectation is taken with respect to the joint distribution of X and ~ determined by (i)
|
From page 128... ...
For the 95 percent prediction interval, the key result is that El EYu(X)
|
From page 129... ...
1991. Some additional nonparametric prediction limits for ground-water detection monitoring at waste disposal facilities.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.