The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Metaanalyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses.

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The PRISMA Extension Statement for Reporting of Systematic Reviews Incorporating Network Meta-analyses of Health Care Interventions: Checklist and Explanations

Restricted maximum likelihood (REML) is now well established as a method for estimating the parameters of the general Gaussian linear model with a structured covariance matrix, in particular for mixed linear models. Conventionally, estimates of precision and inference for fixed effects are based on their asymptotic distribution, which is known to be inadequate for some small-sample problems. In this paper, we present a scaled Wald statistic, together with an F approximation to its sampling distribution, that is shown to perform well in a range of small sample settings. The statistic uses an adjusted estimator of the covariance matrix that has reduced small sample bias. This approach has the advantage that it reproduces both the statistics and F distributions in those settings where the latter is exact, namely for Hotelling T2 type statistics and for analysis of variance F-ratios. The performance of the modified statistics is assessed through simulation studies of four different REML analyses and the methods are illustrated using three examples.

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Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood

The Swift mission, scheduled for launch in 2004, is a multiwavelength observatory for gamma-ray burst (GRB) astronomy. It is a first-of-its-kind autonomous rapid-slewing satellite for transient astronomy and pioneers the way for future rapid-reaction and multiwavelength missions. It will be far more powerful than any previous GRB mission, observing more than 100 bursts yr � 1 and performing detailed X-ray and UV/optical afterglow observations spanning timescales from 1 minute to several days after the burst. The objectives are to (1) determine the origin of GRBs, (2) classify GRBs and search for new types, (3) study the interaction of the ultrarelativistic outflows of GRBs with their surrounding medium, and (4) use GRBs to study the early universe out to z >10. The mission is being developed by a NASA-led international collaboration. It will carry three instruments: a newgeneration wide-field gamma-ray (15‐150 keV) detector that will detect bursts, calculate 1 0 ‐4 0 positions, and trigger autonomous spacecraft slews; a narrow-field X-ray telescope that will give 5 00 positions and perform spectroscopy in the 0.2‐10 keV band; and a narrow-field UV/optical telescope that will operate in the 170‐ 600 nm band and provide 0B3 positions and optical finding charts. Redshift determinations will be made for most bursts. In addition to the primary GRB science, the mission will perform a hard X-ray survey to a sensitivity of � 1m crab (� 2;10 � 11 ergs cm � 2 s � 1 in the 15‐150 keV band), more than an order of magnitude better than HEAO 1 A-4. A flexible data and operations system will allow rapid follow-up observations of all types of

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The Swift Gamma-Ray Burst Mission

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1053/jhep.2002.33156

Randomized controlled trial of transarterial lipiodol chemoembolization for unresectable hepatocellular carcinoma.

Background The efficacy of arthroscopic surgery for the treatment of osteoarthritis of the knee is unknown. Methods We conducted a single-center, randomized, controlled trial of arthroscopic surgery in patients with moderate-to-severe osteoarthritis of the knee. Patients were randomly assigned to surgical lavage and arthroscopic debridement together with optimized physical and medical therapy or to treatment with physical and medical therapy alone. The primary outcome was the total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score (range, 0 to 2400; higher scores indicate more severe symptoms) at 2 years of follow-up. Secondary outcomes included the Short Form-36 (SF-36) Physical Component Summary score (range, 0 to 100; higher scores indicate better quality of life). Results Of the 92 patients assigned to surgery, 6 did not undergo surgery. Of the 86 patients assigned to control treatment, all received only physical and medical therapy. After 2 years, the mean (±SD) WOMAC score for the surgery group was 874±624, as compared with 897±583 for the control group (absolute difference [surgery-group score minus control-group score], −23±605; 95% confidence interval [CI], −208 to 161; P = 0.22 after adjustment for baseline score and grade of severity). The SF-36 Physical Component Summary scores were 37.0±11.4 and 37.2±10.6, respectively (absolute difference, −0.2±11.1; 95% CI, −3.6 to 3.2; P = 0.93). Analyses of WOMAC scores at interim visits and other secondary outcomes also failed to show superiority of surgery.

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A Randomized Trial of Arthroscopic Surgery for Osteoarthritis of the Knee

This chapter provides an overview of recent developments in the design and analysis of cross-over trials. We first consider the analysis of the trial that compares two treatments, A and B, over two periods and where the subjects are randomized to the treatment sequences AB and BA. We make the distinction between fixed and random effects models and show how these models can easily be fitted using modern software. Issues with fitting and testing for a difference in carry-over effects are described and the use of baseline measurements is discussed. Simple methods for testing for a treatment difference when the data are binary are also described. Various designs with two or more treatments but with three or four periods are then described and compared. These include the balanced and partially balanced designs for three or more treatments and designs for factorial treatment combinations. Also described are nearly balanced and nearly strongly balanced designs. Random subject-effects models for the designs with two or more treatments are described and methods for analysing non-normal data are also given. The chapter concludes with a description of the use of cross-over designs in the testing of bioequivalence.

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Design and Analysis of Cross-Over Trials

The aim of an equivalence trial is to show the therapeutic equivalence of two treatments, usually a new drug under development and an existing drug for the same disease used as a standard active comparator. Unfortunately the principles that govern the design, conduct, and analysis of equivalence trials are not as well understood as they should be. Consequently such trials often include too few patients or have intrinsic design biases which tend towards the conclusion of no difference. In addition the application of hypothesis testing in analysing and interpreting data from such trials sometimes compounds the drawing of inappropriate conclusions, and the inclusion and exclusion of patients from analysis may be poorly managed.

The design of equivalence trials should mirror that of earlier successful trials of the active comparator as closely as possible. Patient losses and other deviations from the protocol should be minimised; analysis strategies to deal with unavoidable problems should not centre on an “intention to treat” analysis but should seek to show the similarity of results from a range of approaches. Analysis should be based on confidence intervals, and this also carries implications for the estimation of the required numbers of patients at the design stage.

The gold standard in clinical research is the randomised placebo controlled double blind clinical trial. This design is favoured for confirmatory trials carried out as part of the phase III development of new medicines. Because of the number and range of medicines already available, however, new medicines are increasingly being developed for indications in which a placebo control group would be unethical. In such situations one obvious solution is to use as an active comparator an existing drug already licensed and regularly used for the indications in question. Some authors have questioned whether placebo controlled trials are used excessively and unethically, …

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Trials to assess equivalence: the importance of rigorous methods

Features

Contains new chapters on re-estimating sample size when testing for average bioequivalence, fitting a nonlinear dose response function, estimating a dose to take forward from phase two to phase three, establishing proof of concept, and recalculating the sample size using conditional power
Employs the R package Crossover, specially created to accompany the book and provide a graphical user interface for locating designs in a large catalog and for searching for new designs
Includes updates regarding the use of period baselines and the analysis of data from very small trials
Reflects the availability of new procedures in SAS, particularly proc glimmix
Presents the SAS procedure proc mcmc as an alternative to WinBUGS for Bayesian analysis
Summary

Design and Analysis of Cross-Over Trials is concerned with a specific kind of comparative trial known as the cross-over trial, in which subjects receive different sequences of treatments. Such trials are widely used in clinical and medical research, and in other diverse areas such as veterinary science, psychology, sports science, and agriculture.

The first edition of this book was the first to be wholly devoted to the subject. The second edition was revised to mirror growth and development in areas where the design remained in widespread use and new areas where it had grown in importance. This new Third Edition:

Contains seven new chapters written in the form of short case studies that address re-estimating sample size when testing for average bioequivalence, fitting a nonlinear dose response function, estimating a dose to take forward from phase two to phase three, establishing proof of concept, and recalculating the sample size using conditional power
Employs the R package Crossover, specially created to accompany the book and provide a graphical user interface for locating designs in a large catalog and for searching for new designs
Includes updates regarding the use of period baselines and the analysis of data from very small trials
Reflects the availability of new procedures in SAS, particularly proc glimmix
Presents the SAS procedure proc mcmc as an alternative to WinBUGS for Bayesian analysis
Complete with real data and downloadable SAS code, Design and Analysis of Cross-Over Trials, Third Edition provides a practical understanding of the latest methods along with the necessary tools for implementation.

Byron Jones

Papers

Design and Analysis of Cross-Over Trials

Trials to assess equivalence: the importance of rigorous methods

Design and Analysis of Cross-Over Trials, Second Edition

Statistical Methods for Engineers and Scientists

The use of Bayesian network modelling for maintenance planning in a manufacturing industry