Biomedical subjects

Hsiao-Hui Tsou

Publications and source records attributed to Hsiao-Hui Tsou.

4 recordsLinked to original sources

PubMed2007

Use of prior information for Bayesian evaluation of bridging studies.

The ICH E5 guideline defines a bridging study as a supplementary study conducted in the new region to provide pharmacodynamic or clinical data on efficacy, safety, dosage, and dose regimen to allow extrapolation of the foreign clinical data to the population of the new region. Therefore, a bridging study is usually conducted in the new region only after the test product has been approved for commercial marketing in the original region based on its proven efficacy and safety. In this paper we address the issue of analysis of clinical data generated by the bridging study conducted in the new region to evaluate the similarity for extrapolation of the foreign clinical data to the population of the new region. Information on efficacy, safety, dosage, and dose regimen of the original region cannot be concurrently obtained from the local bridging studies but available in the trials conducted in the original region. Liu et al. (2002) have proposed a Bayesian approach to synthesize the data generated by the bridging study and foreign clinical data generated in the original region for assessment of similarity based on superior efficacy of the test product over a placebo control. However, the results of the bridging studies using their approach will be overwhelmingly dominated by the results of the original region due to an imbalance of sample sizes between the regions. Therefore, in this paper we propose a Bayesian approach with the use of a mixture prior for assessment of similarity between the new and original region based on the concept of positive treatment effect. Methods for sample size determination for the bridging study are also proposed. Numerical examples illustrate applications of the proposed procedures in different scenarios.

Algorithms↗

PubMed2006

Impaired phagocytosis of capsular serotypes K1 or K2 Klebsiella pneumoniae in type 2 diabetes mellitus patients with poor glycemic control.

CONTEXT: Diabetes mellitus (DM) and capsular serotypes K1 and K2 Klebsiella pneumoniae have been identified as risk factors for liver abscess and complicated endophthalmitis. OBJECTIVE: The objective of this study was to determine whether poor glycemic control contributes to the development of capsular serotype K1 or K2 K. pneumoniae liver abscess. DESIGN AND SETTING: Neutrophil phagocytosis in patients with type 2 DM and nondiabetic controls was compared with isolates from liver abscess. Phagocytic rates of 18 K1/K2 and nine non-K1/K2 K. pneumoniae strains were evaluated by flow cytometry and electron microscopy. PATIENTS OR STUDY PARTICIPANTS: Forty patients with type 2 diabetes, 14 with good glycemic control, 26 with poor glycemic control, and 13 age-matched healthy normal subjects, were studied. MAIN OUTCOME MEASURES: Phagocytic rate of K. pneumoniae was measured. RESULTS: Phagocytosis of serotype K1/K2 isolates by neutrophils from diabetics was significantly less than normal controls (P < 0.01). Further analysis revealed that, in type 2 DM patients with poor glycemic control, phagocytosis of K1/K2 was remarkably impaired at 10 min (25.2 +/- 1.7 vs. 42.4 +/- 1.8%) and persisted until 60 min (51 +/- 1.2 vs. 59.4 +/- 1.4%; P < 0.01), but in type 2 DM patients with good glycemic control were similar at 10 min (38.2 +/- 1.7% vs. 42.4 +/- 1.8%) and at 60 min (57 +/- 0.3% vs. 59.4 +/- 1.4%; P = 0.2). No significant difference in the phagocytosis of non-K1/K2 K. pneumoniae among all subjects was observed. CONCLUSIONS: Poor glycemic control plays a role in impairing neutrophil phagocytosis of K1/K2 K. pneumoniae, but does not significantly affect the phagocytosis of non-K1/K2 K. pneumoniae. This study identifies poor glycemic control as a risk factor for susceptibility to serotype K1/K2 K. pneumoniae liver abscess and complicated endophthalmitis.

Adult↗

PubMed2003

Design and analysis of non-inferiority mortality trials in oncology.

The recent revision of the Declaration of Helsinki and the existence of many new therapies that affect survival or serious morbidity, and that therefore cannot be denied patients, have generated increased interest in active-control trials, particularly those intended to show equivalence or non-inferiority to the active-control. A non-inferiority hypothesis has historically been formulated in terms of a fixed margin. This margin was historically designed to exclude a 'clinically meaningful difference', but has become recognized that the margin must also be no larger than the assured effect of the control in the new study. Depending on how this 'assured effect' is determined or estimated, the selected margin may be very small, leading to very large sample sizes, especially when there is an added requirement that a loss of some specified fraction of the assured effect must be ruled out. In cases where it is appropriate, this paper proposes non-inferiority analyses that do not involve a fixed margin, but can be described as a two confidence interval procedure that compares the 95 per cent two-sided CI for the difference between the treatment and the control to a confidence interval for the control effect (based on a meta-analysis of historical data comparing the control to placebo) that is chosen to preserve a study-wide type I error rate of about 0.025 (similar to the usual standard for a superiority trial) for testing for retention of a prespecified fraction of the control effect. The approach assumes that the estimate of the historical active-control effect size is applicable in the current study. If there is reason to believe that this effect size is diminished (for example, improved concomitant therapies) the estimate of this historical effect could be reduced appropriately. The statistical methodology for testing this non-inferiority hypothesis is developed for a hazard ratio (rather than an absolute difference between treatments, because a hazard ratio seems likely to be less population dependent than the absolute difference). In the case of oncology, the hazard ratio is the usual way of comparing treatments with respect to time to event (time to progression or survival) endpoints. The proportional hazards assumption is regarded as reasonable (approximately holding). The testing procedures proposed are conditionally equivalent to two confidence interval procedures that relax the conservatism of two 95 per cent confidence interval testing procedures and preserve the type I error rate at a one-sided 0.025 level. An application of this methodology to Xeloda, a recently approved drug for the treatment of metastatic colorectal cancers, is illustrated. Other methodologies are also described and assessed - including a point estimate procedure, a Bayesian procedure and two delta-method confidence interval procedures. Published in 2003 by John Wiley & Sons, Ltd.

Antimetabolites, Antineoplastic↗

PubMed2003

On non-inferiority analysis based on delta-method confidence intervals.

For many indications where there is an effective standard therapy, active controlled trials are generally conducted when it is unethical to use a placebo. The efficacy objective of most such trials is the demonstration that the experimental therapy has superior efficacy to the active-control. The efficacy objective of a non-inferiority trial may be to rule out that the experimental treatment loses some prespecified fraction of the active-control effect. The size of the active-control effect may be based on previous trials comparing this active-control with a placebo--for example, through a meta-analysis. Delta-method 95% confidence interval procedures are among the testing procedures that have been proposed to test a non-inferiority hypothesis that an experimental treatment retains more than some prespecified fraction of the active-control effect. For time-to-event endpoints using hazard ratios, we will examine the type I error probability of such testing procedures under the assumption that the current active-control effect has been correctly modeled. Conditions are discussed for when such testing procedures maintain a desired approximate type I error rate and when such testing procedures will not. Two applications (one in Cardiorenalogy and one in Oncology) are given--one maintains the desired approximate type I error probability and the other does not. The delta-method 95% confidence interval procedures will also be contrasted with Fieller 95% confidence intervals. Testing based on Fieller 95% confidence intervals will maintain a desired approximate type I error rate.

Confidence Intervals↗