A proof-of-concept-to-confirmatory multiple adaptation design in the development of an anti-viral treatment.

In the clinical development of some new infectious disease drugs, early clinical pharmacology trials may predict with high confidence that the efficacious doses are well below the range of the safety margin. In this case, a dose-ranging study may be unnecessary after a proof-of-concept (PoC) study testing the highest dose. A multi-stage adaptive design spanning both PoC and confirmatory stages is proposed in this context. The design incorporates two interim analyses allowing strategies for stopping, continuing, or expanding the study. A conditional power threshold for a binary endpoint is proposed to assess futility. Additional components of early efficacy and sample size adjustment are also included to enhance the design's flexibility and robustness. Design operating characteristics are evaluated by numerical calculation. We show that the proposed streamlined trial design has the same statistical rigor as a conventional phase 3 clinical trial with adequate power and a properly controlled type 1 error rate. Additional adaptive design options are also investigated and discussed.

Inference of equivalence for the ratio of two normal means with unspecified variances.

Equivalence trials aim to demonstrate that new and standard treatments are equivalent within predefined clinically relevant limits. We focus on when inference of equivalence is made in terms of the ratio of two normal means. In the presence of unspecified variances, methods such as the likelihood-ratio test use sample estimates for those variances; Bayesian models integrate them out in the posterior distribution. These methods limit the knowledge on the extent to which equivalence is affected by variability of the parameter of interest. In this article, we propose a likelihood approach that retains the unspecified variances in the model and partitions the likelihood function into two components: F-statistic function for variances, and t-statistic function for the ratio of two means. By incorporating unspecified variances, the proposed method can help identify a numeric range of variances where equivalence is more likely to be achieved, which cannot be accomplished by current analysis methods. By partitioning the likelihood function into two components, the proposed method provides more inference information than a method that relies solely on one component. Using a published set of real example data, we show that the proposed method produces the same results as the likelihood-ratio test and is comparable to Bayesian analysis in the general case. In a special case where the ratio of two variances is directly proportional to the ratio of two means, the proposed method yields better results in inference about equivalence than either the likelihood-ratio test or the Bayesian method. Using a published set of real example data, the proposed likelihood method is shown to be a better alternative than current analysis methods for equivalence inference.

Inference of bioequivalence for log-normal distributed data with unspecified variances.

Two drugs are bioequivalent if the ratio of a pharmacokinetic (PK) parameter of two products falls within equivalence margins. The distribution of PK parameters is often assumed to be log-normal, therefore bioequivalence (BE) is usually assessed on the difference of logarithmically transformed PK parameters (δ). In the presence of unspecified variances, test procedures such as two one-sided tests (TOST) use sample estimates for those variances; Bayesian models integrate them out in the posterior distribution. These methods limit our knowledge on the extent that inference about BE is affected by the variability of PK parameters. In this paper, we propose a likelihood approach that retains the unspecified variances in the model and partitions the entire likelihood function into two components: F-statistic function for variances and t-statistic function for δ. Demonstrated with published real-life data, the proposed method not only produces results that are same as TOST and comparable with Bayesian method but also helps identify ranges of variances, which could make the determination of BE more achievable. Our findings manifest the advantages of the proposed method in making inference about the extent that BE is affected by the unspecified variances, which cannot be accomplished either by TOST or Bayesian method.

Validation of an immunodiagnostic assay for detection of 13 Streptococcus pneumoniae serotype-specific polysaccharides in human urine.

To improve the clinical diagnosis of pneumococcal infection in bacteremic and nonbacteremic community-acquired pneumonia (CAP), a Luminex technology-based multiplex urinary antigen detection (UAD) diagnostic assay was developed and validated. The UAD assay can simultaneously detect 13 different serotypes of Streptococcus pneumoniae by capturing serotype-specific S. pneumoniae polysaccharides (PnPSs) secreted in human urine. Assay specificity is achieved by capturing the polysaccharides with serotype-specific monoclonal antibodies (MAbs) on spectrally unique microspheres. Positivity for each serotype was based on positivity cutoff values calculated from a standard curve run on each assay plate together with positive- and negative-control urine samples. The assay is highly specific, since significant signals are detected only when each PnPS was paired with its homologous MAb-coated microspheres. Validation experiments demonstrated excellent accuracy and precision. The UAD assay and corresponding positivity cutoff values were clinically validated by assessing 776 urine specimens obtained from patients with X-ray-confirmed CAP. The UAD assay demonstrated 97% sensitivity and 100% specificity using samples obtained from patients with bacteremic, blood culture-positive CAP. Importantly, the UAD assay identified Streptococcus pneumoniae (13 serotypes) in a proportion of individuals with nonbacteremic CAP, a patient population for which the pneumococcal etiology of CAP was previously difficult to assess. Therefore, the UAD assay provides a specific, noninvasive, sensitive, and reproducible tool to support vaccine efficacy as well as epidemiological evaluation of pneumococcal disease, including CAP, in adults.

ILSI-HESI cardiovascular safety subcommittee dataset: an analysis of the statistical properties of QT interval and rate-corrected QT interval (QTc).

INTRODUCTION: The Health and Environmental Sciences Institute of the International Life Sciences Institute (ILSI/HESI) Cardiovascular Safety Subcommittee outlined a set of in vivo telemetry studies to determine how well this preclinical model identified compounds known to cause torsades de pointes (TdP) and prolong QT interval in humans. In the original analysis of these data, QT, QTcB (Bazett model), QTcF (Fridericia model), and QTcQ (animal-specific model) were evaluated. We further evaluate the statistical properties of these measurements, using a method that can properly account for the sources of variability in the dataset. METHODS: The ILSI/HESI telemetry studies were conducted as a double Latin square design where eight dogs each received a vehicle control and three dose levels of a compound on four separate dosing days. We statistically analyzed the QT/QTc intervals using a repeated measures analysis of covariance and evaluate the powers for QT, QTcF and QTcQ based on simulations. RESULTS: The analyses for QTcF and QTcB intervals show that all six compounds which were known to cause TdP in humans were identified as positive and all six compounds known to be free of TdP events in their clinical use had no statistically significant treatment-related effects, while the analyses for QTcQ identified all positive compounds except pimozide. The power analysis shows that the method can detect a 7% increment of QT, a 5% increment of QTcF, and a 4% increment of QTcQ, with greater than 80% of power when n=8. DISCUSSION: We describe a repeated measures procedure to perform statistical analysis of covariance on Latin square designs and show that it can be used to detect meaningful changes in the analysis of QT/QTc intervals.

Aggregate exposures of nine preschool children to persistent organic pollutants at day care and at home.

In the summer of 1997, we measured the aggregate exposures of nine preschool children, aged 2-5 years, to a suite of organic pesticides and other persistent organic pollutants that are commonly found in the home and school environment. The children attended either of two child day care centers in the Raleigh-Durham-Chapel Hill area of North Carolina and were in day care at least 25 h/week. Over a 48-h period, we sampled indoor and outdoor air, play area soil and floor dust, as well as duplicate diets, hand surface wipes, and urine for each child at day care and at home. Our target analytes were several polycyclic aromatic hydrocarbons (PAH), organochlorine pesticides, and polychlorinated biphenyls (PCB); two organophosphate pesticides (chlorpyrifos and diazinon), the lawn herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), three phenols (pentachlorophenol (PCP), nonyl phenols, and bisphenol-A), 3,5,6-trichloro-2-pyridinol (TCP), and two phthalate esters (benzylbutyl and dibutyl phthalate). In urine, our target analytes were hydroxy-PAH, TCP, 2,4-D, and PCP. To allow estimation of each child's aggregate exposures over the 48-h sampling period, we also used time-activity diaries, which were filled out by each child's teacher at day care and the parent or other primary caregiver at home. In addition, we collected detailed household information that related to potential sources of exposure, such as pesticide use or smoking habits, through questionnaires and field observation. We found that the indoor exposures were greater than those outdoors, that exposures at day care and at home were of similar magnitudes, and that diet contributed greatly to the exposures. The children's potential aggregate doses, calculated from our data, were generally well below established reference doses (RfDs) for those compounds for which RfDs are available.