Get Statistical Monitoring of Clinical Trials: A Unified PDF

By Michael A. Proschan

ISBN-10: 0387300597

ISBN-13: 9780387300597

ISBN-10: 0387449701

ISBN-13: 9780387449708

The process taken during this e-book is, to reports monitored over the years, what the primary restrict Theorem is to reviews with just one research. simply because the imperative restrict Theorem indicates that try out records regarding very sorts of scientific trial results are asymptotically common, this ebook exhibits that the joint distribution of the attempt facts at varied research instances is asymptotically multivariate basic with the correlation constitution of Brownian movement (``the B-value") regardless of the attempt statistic. The so-called B-value method of tracking permits us to take advantage of, for various different types of trials, a similar obstacles and an analogous uncomplicated formulation for computing conditional energy. even though Brownian movement may well sound complex, the authors make the strategy effortless via beginning with an easy instance and construction on it, one piece at a time, eventually displaying that Brownian movement works for plenty of types of scientific trials.

The booklet may be very worthwhile to statisticians thinking about scientific trials. the most physique of the chapters is obtainable to a person with wisdom of a customary mathematical facts textual content. extra mathematically complicated readers will locate rigorous advancements in appendices on the finish of chapters. interpreting the e-book will advance perception into not just tracking, yet energy, survival research, protection, and different statistical concerns germane to medical trials.

Michael Proschan, Gordon Lan, and Janet Wittes are elected Fellows of the yank Statistical organization. All have spent adolescence within the Biostatistics learn department of the nationwide middle, Lung, and Blood Institute (NHLBI/NIH). whereas there, they have been in detail occupied with the layout and statistical tracking of large-scale randomized scientific trials, constructing technique to help of their tracking. for instance, Lan built, with DeMets, the now widely-used spending functionality method of team sequential designs, whose houses have been extra investigated via Proschan. The B-value process utilized in the e-book used to be brought in a truly influential paper by way of Lan and Wittes. The statistical thought at the back of conditional strength was once constructed via Lan, besides Simon and Halperin, and was once the cornerstone for the conditional errors method of adaptive scientific trials brought via Proschan and Hunsberger. All 3 authors have services in adaptive method for scientific trials.

Michael Proschan is a Mathematical Statistician on the nationwide Institutes of healthiness; Gordon Lan is Senior Director of Biometrics at Johnson & Johnson Pharmaceutical learn & improvement, L.L.C.; Janet Wittes is President of records Collaborative, a statistical consulting corporation she based in 1990.

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Example text

As noted earlier, the least squares estimator in this transformed model is complete and sufficient. The arguments of the preceding subsection imply ˆ j ) and β(τ ˆ j ) − β(τ ˆ i ) are independent. 7 holds for the mixed model if the covariance between every pair of Y observations is known. A similar result holds in the unknown covariance case provided that the number of distinct covariances to be estimated is small compared to the number of participants. 8 means that the null joint distribution of z-statistics (treatment effect estimates divided by their estimated standard errors) at different infor−1 ˆ i )}]−1/[var{δ(1)}] ˆ mation fractions ti = [var{δ(τ in a trial analyzed with a mixed model is the same as for a simple t-test.

2. Thus, we can treat vˆn as if it were vn ; ˆ ≈ v−2 var(Sn ) = v−2 vn = 1/vn, var(δ) n n and information is approximately I = vn , estimated by vˆn . d. observations with expectation δ and variance 1; δˆ has mean δ and variance 1/I. Furthermore, for Ii = vni ≤ Ij = vnj , nj ni cov(δˆi , δˆj ) = cov (1/Ii ) Dr , (1/Ij ) r=1 ni = (Ii Ij )−1 cov ni Dr , r=1 ni = (Ii Ij )−1 Dr r=1 var r=1 Dr r=1 ≈ (Ii Ij )−1 {vni + 0} = (Ii Ij )−1 Ii = 1/Ij . 13) shows that the covariance of log hazard ratio estimators computed at two different times is the same as for a sample mean, namely the inverse of the information at the later time.

If our aim for that trial were simply to declare, or not declare, statistical significance, we could choose to stop now. Stopping a trial because the final result is completely determined at an interim analysis is called curtailment. As in this example, curtailment can only happen near the end of the trial. Though the result was completely determined only very close to the end of the trial, it must have been “almost” determined earlier. 2. A statistically significant benefit at the end is possible only if all 20 remaining control patients have events and all 20 remaining treatment patients are event-free.

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Statistical Monitoring of Clinical Trials: A Unified Approach by Michael A. Proschan


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