Search PubMed⌕ Search

PubMed · 10317301

Project planning, investigator selection, data harmonization.

Abstract

This paper has presented certain concepts and thoughts regarding the planning process and dealt with two areas where planning and preconsideration of problems will aid in the quality, speed and efficiency of ones programs. The varied examples that have been cited are just that; they are not intended to be a comprehensive evaluation of all the aspects of the planning process or, for that matter, the selection of investigators or harmonization of data. They are intended instead to illustrate the way in which a comprehensive and thorough planning approach to project management can facilitate, ease and improve the quality of the work we do. The important point of this whole paper is not so much the individual benefits to be gained from any one of the systems described, but rather to illustrate as much as possible the value of planning in our process. It is incumbent on us to search for ways to improve the way in which we do or work. Standards have improved considerably over the years, but there is much room for further improvement; good clinical and scientific thought will inevitably continue to contribute to the improvement of those programs. The submission of this author, however, is that if we do these in advance, we will solve them a good deal faster and a good deal more efficiently than if we constantly react to new and developing problems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R J Crossley. Project planning, investigator selection, data harmonization.. https://doi.org/10.1177/009286158201600105

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

On the exact interval estimation for the difference in paired areas under the ROC curves.

An important measure for comparison of accuracy between two diagnostic procedures is the difference in paired areas under the receiver operating characteristic (ROC) curves. Non-parametric and maximum likelihood methods have been proposed for interval estimation for the difference in paired areas under ROC curves. However, these two methods are asymptotic procedures and their performance in finite sample sizes has not been thoroughly investigated. We propose to use the concept of generalized pivotal quantities (GPQs) to construct an exact confidence interval for the difference in paired areas under ROC curves. A simulation study is conducted to empirically investigate the probability coverage and expected length of the three methods for various combinations of sample sizes, values of the area under the ROC curve and correlations. Simulation results demonstrate that the exact confidence interval based on the concept of GPQs provides not only sufficient probability coverage but also reasonable expected length. Numerical examples using published data sets illustrate the proposed method.

Clinical Trials as Topic↗

An efficient test for the analysis of dichotomized variables when the reliability is known.

A difference in an outcome variable between the treatment groups in a trial does not necessarily mean that there is a difference in the number of patients who experience relevant improvement on that variable. When the relevant improvement corresponds with an outcome or change in outcome that exceeds a certain threshold, the outcome variable can be dichotomized. A responder is a patient whose outcome exceeds the threshold. Comparisons can be made between the number of responders in the two treatment groups using logistic regression, or some other method to evaluate binary outcomes. An important disadvantage of this approach is the loss of power. In general, it is more efficient to test the difference between the mean values. We developed a statistical test that compares response rates for a dichotomized variable. It requires that an estimate of the reliability of the outcome variable is available. Simulations showed that the test was valid and robust over a wide range of distributions and sample sizes. The power was greater than the power of a chi(2) test, which would enable substantial reduction in the sample size.

Clinical Trials as Topic↗

Sample size determination for logistic regression revisited.

There is no consensus on the approach to compute the power and sample size with logistic regression. Some authors use the likelihood ratio test; some use the test on proportions; some suggest various approximations to handle the multivariate case. We advocate the use of the Wald test since the Z-score is routinely used for statistical significance testing of regression coefficients. The null-variance formula became popular from early studies, which contradicts modern software, which utilizes the method of maximum likelihood estimation (MLE), when the variance of the MLE is estimated at the MLE, not at the null. We derive general Wald-based power and sample size formulas for logistic regression and then apply them to binary exposure and confounder to obtain a closed-form expression. These formulas are applied to minimize the total sample size in a case-control study to achieve a given power by optimizing the ratio of controls to cases. Approximately, the optimal number of controls to cases is equal to the square root of the alternative odds ratio. Our sample size and power calculations can be carried out online at www.dartmouth.edu/ approximately eugened.

Clinical Trials as Topic↗