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Calculating Sample Size (calculating + sample_size)
Selected AbstractsCalculating Sample Size for Studies with Expected All-or-None Nonadherence and Selection BiasBIOMETRICS, Issue 2 2009Michelle D. Shardell Summary We develop sample size formulas for studies aiming to test mean differences between a treatment and control group when all-or-none nonadherence (noncompliance) and selection bias are expected. Recent work by Fay, Halloran, and Follmann (2007, Biometrics63, 465,474) addressed the increased variances within groups defined by treatment assignment when nonadherence occurs, compared to the scenario of full adherence, under the assumption of no selection bias. In this article, we extend the authors' approach to allow selection bias in the form of systematic differences in means and variances among latent adherence subgroups. We illustrate the approach by performing sample size calculations to plan clinical trials with and without pilot adherence data. Sample size formulas and tests for normally distributed outcomes are also developed in a Web Appendix that account for uncertainty of estimates from external or internal pilot data. [source] Rejoinder to "Modification of the Computational Procedure in Parker and Bregman's Method of Calculating Sample Size from Matched Case,Control Studies with a Dichotomous Exposure"BIOMETRICS, Issue 4 2005Robert A. Parker No abstract is available for this article. [source] Botulinum Toxin, Physical and Occupational Therapy, and Neuromuscular Electrical Stimulation to Treat Spastic Upper Limb of Children With Cerebral Palsy: A Pilot StudyARTIFICIAL ORGANS, Issue 3 2010Gerardo Rodríguez-Reyes Abstract Spasticity has been successfully managed with different treatment modalities or combinations. No information is available on the effectiveness or individual contribution of botulinum toxin type A (BTA) combined with physical and occupational therapy and neuromuscular electrical stimulation to treat spastic upper limb. The purpose of this study was to assess the effects of such treatment and to inform sample-size calculations for a randomized controlled trial. BTA was injected into spastic upper limb muscles of 10 children. They received 10 sessions of physical and occupational therapy followed by 10 sessions of neuromuscular electrical stimulation on the wrist extensors (antagonist muscles). Degree of spasticity using the Modified Ashworth scale, active range of motion, and manual function with the Jebsen hand test, were assessed. Meaningful improvement was observed in hand function posttreatment (P = 0.03). Median spasticity showed a reduction trend and median amplitude of wrist range of motion registered an increase; however, neither of these were significant (P > 0.05). There is evidence of a beneficial effect of the combined treatment. Adequate information has been obtained on main outcome-measurement variability for calculating sample size for a subsequent study to quantify the treatment effect precisely. [source] Advanced Statistics:Statistical Methods for Analyzing Cluster and Cluster-randomized DataACADEMIC EMERGENCY MEDICINE, Issue 4 2002Robert L. Wears MD Abstract. Sometimes interventions in randomized clinical trials are not allocated to individual patients, but rather to patients in groups. This is called cluster allocation, or cluster randomization, and is particularly common in health services research. Similarly, in some types of observational studies, patients (or observations) are found in naturally occurring groups, such as neighborhoods. In either situation, observations within a cluster tend to be more alike than observations selected entirely at random. This violates the assumption of independence that is at the heart of common methods of statistical estimation and hypothesis testing. Failure to account for the dependence between individual observations and the cluster to which they belong can have profound implications on the design and analysis of such studies. Their p-values will be too small, confidence intervals too narrow, and sample size estimates too small, sometimes to a dramatic degree. This problem is similar to that caused by the more familiar "unit of analysis error" seen when observations are repeated on the same subjects, but are treated as independent. The purpose of this paper is to provide an introduction to the problem of clustered data in clinical research. It provides guidance and examples of methods for analyzing clustered data and calculating sample sizes when planning studies. The article concludes with some general comments on statistical software for cluster data and principles for planning, analyzing, and presenting such studies. [source] | ||||||||||