PubMed Health⌕ Search

PubMed · 8483112

HIV vaccine trials: some design issues including sample size calculation.

Abstract

Anticipating the availability of one or more candidate HIV vaccines for efficacy testing in the next few years, public health agencies are now planning for the conduct of large-scale efficacy trials. We expect these trials to be randomized, double-blind, placebo-controlled studies with prevention of infection as the primary goal. We discuss in detail factors that influence sample size. Factors most influential are the incidence rate of HIV infection in the study population and the minimum efficacy at which a vaccine is still considered acceptable. The smaller either of these factors is, the larger the sample size will be. The desire to complete trials quickly, the gradual accrual of benefit from vaccination, the inaccuracies of assays to detect infection, the need to counsel participants to avoid exposure to HIV, and loss to follow-up all tend to drive up sample size. To illustrate, 83 subjects per study arm suffice to detect 90% efficacy in a population with a 7% annual risk of infection. This assumes a 3-year study with accrual completed in 1 year, no loss to follow-up, and Types I and II error rates of 5 and 10%, respectively. In contrast, 4,254 subjects per arm are required to identify a 60% effective vaccine in a population with a 1% annual risk. The study is also shortened to 2 years, assumes a 5% annual loss to follow-up, and supposes that the full benefit of vaccination is achieved in 6 months. The most realistic assumptions indicate that trials are very likely to require several thousand participants. Limitations of the proposed designs are also discussed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D O Dixon, W N Rida, P E Fast, D F Hoth. 1993. HIV vaccine trials: some design issues including sample size calculation.. https://pubmed.ncbi.nlm.nih.gov/8483112/

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↗