Drug Information Journal最新文献

We consider analysis of clustered binary data from multiple observations for each subject in which any two observations from a subject are assumed to have a common correlation coefficient. In the weighted sign test on proportion in clustered binary data, three weighting schemes are considered: equal weights to observations, equal weights to clusters and the optimal weights that minimize the variance of the estimator. Since the distribution of cluster sizes may not be exactly specified before the trial starts, the sample size is usually determined using an average cluster size without taking into account any potential imbalance in cluster size even though cluster size usually varies among clusters. In this paper we investigate the relative efficiency (RE) of unequal versus equal cluster sizes for clustered binary data using the weighted sign test estimators. The REs are computed as a function of correlation among observations within each subject and the various cluster size distributions. The required sample size for unequal cluster sizes will not exceed the sample size for an equal cluster size multiplied by the maximum RE. It is concluded that the maximum RE for various cluster size distributions considered here does not exceed 1.50, 1.61 and 1.12 for equal weights to observations, equal weights to clusters and optimal weights, respectively. It suggests sampling 50%, 61% and 12% more clusters depending on the weighting schemes than the number of clusters computed using an average cluster size.

Participation of adult cancer patients in US based clinical trials has remained near 3% for decades. Traditional research methodology reaches a small fraction of the target population with a fixed number of predetermined sites. Solutions are needed to ethically increase patient participation and accelerate cancer trial completion. We compared enrollment outcomes of traditional and patient focused research methodologies. A patient prioritized method (Just-In-Time, JIT) was implemented in parallel with traditionally managed sites in three cancer trials. JIT research sites were initiated after candidate patients presented, while traditional sites were initiated in advance. JIT sites enrolled with mean rates no less than, and up to 2.75 fold greater than, traditional sites. Mean patients enrolled per site was comparable (JIT-1.82, traditional-1.78). There were fewer non-enrolling JIT sites (2/28, 7%) compared to traditional sites 19/52, 37%). This retrospective analysis supports JIT as a prospective solution to increase cancer clinical trial enrollment and the efficiency of clinical trial administrative activities.

In many settings, testing has been proposed to assess the effect of an experimental regimen within a biomarker-positive subgroup where it is biologically plausible that benefit is stronger in such patients, and in the overall population that also includes biomarker-negative subjects less likely to benefit from that regimen. A statistically favorable result in the biomarker-positive subgroup would lead to a claim for that subgroup, whereas a statistically favorable result for the overall population would lead to a claim that includes both biomarker subgroups. The latter setting is problematic when biomarker-negative patients truly do not benefit from the experimental regimen. When it is prespecified that biomarker-negative patients should not be included in the primary analysis of treatment effect in biomarker-positive patients because of the likelihood that treatment effects would differ between the 2 subgroups, it is logically inconsistent to include biomarker-positive patients in the primary analysis of treatment effect in biomarker-negative patients.

Monitoring of clinical trials includes several disciplines, stakeholders, and skill sets. The aim of the present study was to identify database changes and data entry errors to an electronic data capture (EDC) clinical trial database, and to access the impact of the changes. To accomblish the aim, Target e*CRF was used as the EDC tool for a multinational, dose-finding, multicenter, double-blind, randomized, parallel, placebo-controlled trial to investigate efficacy and safety of a new treatment in men with lower urinary tract symptoms associated with benign prostatic hyperplasia. The main errors observed were simple transcription errors from the paper source documents to the EDC database. This observation was to be expected, since every transaction has an inherant error rate. What and how to monitor must be assessed within the risk-based monitoring section of the comprehensive data monitoring plan. With the advent of direct data entry, and the elimination of the requirement to transcribe from a paper source record to an EDC system, error rates should go down dramatically. In addition, protocol violations and data outside the normal range can be identified at the time of data entry and not days, weeks, and months after the fact.

The importance of gathering and monitoring aggregate demographic data on the annual population of study volunteers in FDA-regulated clinical trials is widely acknowledged. To date, no formal mechanism exists to capture this information. The Tufts Center for the Study of Drug Development identified and tested a publicly available source of information on clinical trial participant data, NDA Reviews stored in the FDA's drugs@FDA database, to determine its accuracy, reliability, and feasibility. Thirty-seven new drug applications approved between 2006 and 2008 were evaluated and compared with published sources of demographic data. The authors conclude that the approach described here-NDA review extraction-provides reasonably reliable and conservative estimates of study volunteer demographics and can serve as a useful baseline until Clinicaltrials.gov or other, more complete, public sources become available.

We compare five closed tests for strong control of family-wide type I error (FWE) while making all pair-wise comparisons of means in clinical trials with multiple arms such as the SCORE Study. We simulated outcomes of the SCORE Study under its design hypotheses, and used p-values from chi-squared tests to compare performance of a "pairwise" closed test described below to Bonferroni and Hochberg adjusted p-values. "Pairwise" closed testing was more powerful than Hochberg's method by several definitions of multiple-test power. Simulations over a wider parameter space, and considering other closed methods, confirmed this superiority for p-values based on normal, logistic, and Poisson distributions. The power benefit of "pair-wise" closed testing begins to disappear with 5 or more arms, and with unbalanced designs. For trials with 4 or fewer arms and balanced designs, investigators should consider using "pair-wise" closed testing in preference to Shaffer's, Hommel's, and Hochberg's approaches when making all pairwise comparisons of means. If not all p-values from the closed family are available, Shaffer's method is a good choice.

We propose a sample size calculation approach for the estimation of sensitivity and specificity of diagnostic tests with multiple observations per subjects. Many diagnostic tests such as diagnostic imaging or periodontal tests are characterized by the presence of multiple observations for each subject. The number of observations frequently varies among subjects in diagnostic imaging experiments or periodontal studies. Nonparametric statistical methods for the analysis of clustered binary data have been recently developed by various authors. In this paper, we derive a sample size formula for sensitivity and specificity of diagnostic tests using the sign test while accounting for multiple observations per subjects. Application of the sample size formula for the design of a diagnostic test is discussed. Since the sample size formula is based on large sample theory, simulation studies are conducted to evaluate the finite sample performance of the proposed method. We compare the performance of the proposed sample size formula with that of the parametric sample size formula that assigns equal weight to each observation. Simulation studies show that the proposed sample size formula generally yields empirical powers closer to the nominal level than the parametric method. Simulation studies also show that the number of subjects required increases as the variability in the number of observations per subject increases and the intracluster correlation increases.

Achieving high protocol adherence is essential for ensuring the overall success and scientific merit of clinical trials. Strategies for maximizing recruitment and treatment adherence have been previously explored in the literature. There has been less focus, however, on effective methods for maintaining participants in research follow-up. This article examines factors associated with poor follow-up rates as well as strategies for facilitating research commitment and addressing sources of nonadherence. Special attention is devoted to alcohol- and substance-dependent populations, groups known to have poor adherence rates. Examples are drawn from the COMBINE Study, an NIAAA-funded, nationwide, multisite, combined behavioral and pharmacotherapy trial for alcohol problems that achieved high one-year follow-up rates. The important role of coordinating centers in facilitating research retention is also discussed.