PubMed Health⌕ Search

PubMed · 790878

Clinical trials in surgery.

Abstract

Scientific knowledge is reliable because it has been built on accurate measurements. Clinical knowledge is not. The science of biometry was started in 1835 by the Belgian astronomer Adolphe Quetelet. In the last fifty years a precise set of rules have been invented for this science. The method is suitable for a wide range of problems in clinical medicine and as the result of its application in recent years a firm foundation has been laid upon which scientific clinical knowledge can be built. Surge-ns, particularly, have been slow to apply biometrical methods to their clinical problems; but a start has now been made. The outcome of a particular type of treatment is determined by a complex set of inter-acting factors. The basis of the appropriate biometrical method is the elimination of all bias in favour of one of the rival techniques being compared. If this precaution is taken the observed results of the trial can be fitted onto Quetelet's distribution graph, and the probability of the observed difference in the results being due to chance, and nothing else, can be measured mathematically. The accuracy of this measurement deteriorates the greater are the number of the patients who can not be found and examined at the end of the trial. To make this point two trials conducted by the author are used. In the trial designed to discover if postoperative deep vein thrombosis and pulmonary embolism could be prevented by electrically stimulating the patients legs during the operation there was no difficulty. All the patients were examined at the end of the trial, because it ended before any of the patients left the hospital. The trial to discover if the primary treatment of varicose veins ought to be by Fegan's sclerotherapy or by operation was the exact opposite. At the end of the first year of the two year trial 15% of the patients could not be traced. The results in these patients, therefore, was unknown. Because of this, elaborate mathematical calculations had to be made to try and reach a reliable conclusion to the trial. The loss of patients before the end of the trial is a very serious practical difficulty which besets all long-term clinical trials.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

F S Doran. 1976. Clinical trials in surgery.. https://pubmed.ncbi.nlm.nih.gov/790878/

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↗