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Yi Tsong

Publications and source records attributed to Yi Tsong.

14 recordsLinked to original sources
PubMed2005

Testing superiority and non-inferiority hypotheses in active controlled clinical trials.

Switching between testing for superiority and non-inferiority has been an important statistical issue in the design and analysis of active controlled clinical trial. In practice, it is often conducted with a two-stage testing procedure. It has been assumed that there is no type I error rate adjustment required when either switching to test for non-inferiority once the data fail to support the superiority claim or switching to test for superiority once the null hypothesis of non-inferiority is rejected with a pre-specified non-inferiority margin in a generalized historical control approach. However, when using a cross-trial comparison approach for non-inferiority testing, controlling the type I error rate sometimes becomes an issue with the conventional two-stage procedure. We propose to adopt a single-stage simultaneous testing concept as proposed by Ng (2003) to test both non-inferiority and superiority hypotheses simultaneously. The proposed procedure is based on Fieller's confidence interval procedure as proposed by Hauschke et al. (1999).

Biopharmaceutics↗
PubMed2004

Group sequential design and analysis of clinical equivalence assessment for generic nonsystematic drug products.

Clinical trials with therapeutical endpoints are designed with three arms to demonstrate both the efficacy and the equivalence of the test generic treatment and the reference treatment. A generic drug product is determined to be equivalent to the reference drug product if the ratio or difference between the mean responses is bounded within the pre-specified equivalence limits. Often the trials are oversized for the placebo arm. For improvement, we propose a group sequential design with hierarchical testing for the purpose of terminating the placebo arm before testing equivalence between the test and the reference treatments. The hierarchical feature of the proposal will reduce the sample size of the placebo arm and provide treatments to patients in a more efficient manner in a clinical trial setting. After dropping the placebo arm, the option of allocating the planned but unused sample size from the placebo group to the test and reference groups will increase the sample size and power of the equivalence test without inflating the type I error rate by delaying spending it.

Clinical Trials as Topic↗
PubMed2004

Three-stage sequential statistical dissolution testing rules.

The U.S. Pharmacopoeia (USP) general monograph provides a standard for dissolution compliance with the requirements as stated in the individual USP monograph for a tablet or capsule dosage form. The USP monograph sets performance limit on dissolution in terms of a specific percentage Q that the drug product is required to be dissolved at a specified time. Japan Pharmacopoeia provides acceptance rules different from USP. However the objective of the acceptance rules was not defined in terms of the inference of the whole lot by either USP, European Pharmacopoeia (EP) or Japan Pharmacopoeia (JP). The operating characteristics' curves of these rules are all shown to be sensitive to the true mean dissolution and do not reject a lot which has a large percentage of tablets that dissolve with less than the specified limit Q. This is especially true when the mean dissolution is close to the specification value. We proposed that the goal of the dissolution test sampling plan is to accept a lot at least 90% of the tablets dissolved more than a pre-specified amount Q at the specific time. The group sequential procedure derived accordingly is shown to outperform both USP and JP in controlling the type I error rate under normality assumption.

Algorithms↗
PubMed2003

Some fundamental issues with non-inferiority testing in active controlled trials.

In an active controlled non-inferiority trial without a placebo arm, it is often not entirely clear what the primary objective is. In many cases the considered goal is to demonstrate that the experimental treatment preserves at least some fraction of the effect of the active control. The active control effect is a parameter, the value of which is unknown. To test the hypothesis of effect preservation, the classical confidence interval approach requires specification of a non-inferiority margin which is a function of the unknown active control effect. When the margin is estimated, it is also not clear what is the relevant type I error of making a false assertion about preservation of the active control effect. The statistical uncertainty of the estimated margin arguably needs to be incorporated in evaluation of the type I error. In this paper we discuss these fundamental issues. We show that the classical confidence interval approach cannot attain the target type I error exactly since this error varies as the sample size or as the values of the nuisance parameters in the active controlled trial change. In contrast, the preservation tests, as proposed in literature, can attain the target type I error rate exactly, regardless of the sample size and the values of the nuisance parameters, but can do so only at the price of several strong assumptions holding that may not be directly verifiable. One assumption is the constancy condition holding whereby the effect of the active control in the historical trial populations is assumed to carry to the population of the active control trial. When this condition is violated, both the confidence interval approach and the preservation test method may be problematic.

Confidence Intervals↗
PubMed2003

Adverse pregnancy outcomes associated with maternal enalapril antihypertensive treatment.

BACKGROUND: Adverse pregnancy outcomes following the use of angiotensin-converting enzyme (ACE) inhibitors, including enalapril, have been reported in descriptive studies. However, no analytical studies on the relationship between the adverse outcomes and enalapril gestational exposures are available. OBJECTIVES: To explore the association between enalapril exposure and adverse outcomes in pregnancy, taking into account other possible risk factors. METHODS: We analyzed a series of all usable cases reported to the FDA between 1986 and 2000 in which enalapril was a suspect drug for the observed adverse outcomes (N = 110). Parameters of exposure and reported outcomes as well as information on potentially confounding variables were systematically abstracted from this series by a single physician. Because exposure to ACE inhibitors after the first trimester of pregnancy had been associated with adverse outcomes in the existing literature, we divided the cases into those exposed in the first trimester only (considered as the baseline group) and cases exposed beyond or after this time. Frequency of reported adverse outcomes in the second group was compared with those in the baseline group; odds ratios were computed, taking account of potentially confounding variables by logistic regression where appropriate. RESULTS: Exposure to enalapril after the first trimester of pregnancy was strongly associated with oligohydramnios and specific adverse outcomes thought to be secondary to reduced amniotic fluid volume (limb deformities, cranial ossification deficits, lung hypoplasia), as well as with neonatal renal failure. The relationship did not change after taking numerous potential confounders into account, including duration of exposure, concomitant drug use, maternal age, concurrent disease, neonatal gender, and gestational age at birth. Such a pattern of abnormalities is considered to be a consequence of the effect of ACE inhibition on fetal renal function that develops after the first trimester. CONCLUSION: The specificity and temporality of the observed adverse manifestations suggest a causal relationship to enalapril exposure.

Abnormalities, Drug-Induced↗
PubMed2003

Improving the quality of adverse drug reaction reporting by 4th-year medical students.

PURPOSE: Evaluate whether a 15-minute lecture intervention will improve adverse drug reaction reporting quality on standard MedWatch forms. METHODS: Seventy-eight 4th-year medical students were randomized to intervention 'Group-A' or non-intervention 'Group-B' on the first day of a required five-day clinical pharmacology rotation. Group-A participants attended a 15-minute lecture on completing a MedWatch form with quality information considered by the Food and Drug Administration as critical to adequate adverse drug reaction reporting. Group-B participants did not attend this lecture. Both groups then watched a standardized patient interview of a recognizable adverse drug reaction and completed MedWatch forms. Four Safety Evaluators from the Food and Drug Administration (FDA) rated student responses in a blinded fashion for the primary efficacy variable of Overall Impression and six informational domins using a standardized data quality analysis form that was developed within the Office of Postmarketing Drug Risk Assessment of the FDA. RESULTS: Seventy-eight MedWatch forms were evaluated (Group-A = 40, Group B = 38). Overall MedWatch information quality scores for the intervention group were significantly higher than the non-intervention group (p < 0.004). CONCLUSIONS: As little as a 15-minute intervention can significantly improve the quality of adverse drug reaction reporting by 4th-year medical students. Academic medical centers should consider incorporating adverse drug reaction reporting curriculum into the clinical training of medical students.

Adverse Drug Reaction Reporting Systems↗
PubMed2003

Statistical issues on objective, design, and analysis of noninferiority active-controlled clinical trial.

In practice, "noninferiority" active-controlled trials have been designed for three different objectives: establishing evidence of efficacy over placebo, preserving a specific percentage of the effect size of the active control, or demonstrating the test treatment is "not much inferior" to the active control. All three objectives can be represented by the same set of statistical hypotheses with the parameters defined differently. The various designs and statistical analysis procedures for active-controlled trials proposed in the literature can be group into two basic types: the historical-controlled trial approach and the cross-study comparison approach. These approaches require some unverifiable constancy assumptions. Under the constancy assumptions, the cross-study comparison uses the estimate effect of active-control treatment as the unbiased estimate of the active/placebo difference in the current noninferiority trial. A normalized Z-statistic is used to test the hypotheses. On the other hand, the historical controlled trial approach uses a conservative confidence limit as if it were a constant to replace the active/placebo difference in the current trial. The two approaches may lead to consistent conclusions only when the constancy assumptions can be supported by a large number of historical studies giving a consistent active-control treatment effect over placebo and that the active-control effect does not change over time.

Controlled Clinical Trials as Topic↗
PubMed2003

Significance levels for stability pooling test: a simulation study.

Shelf life of a drug product is defined as the length of time under specific conditions of storage that the product will remain within specifications established to ensure its identity, strength, quality, and purity. The objective of an new drug application (NDA) stability study is to collect and evaluate the evidence in support of the sponsor-proposed shelf life. The proposed shelf life is supported when each batch of the drug products in the study has shelf life no shorter than the proposed shelf life. When the value of the batch mean changes linearly, sometimes batches of the same product may share the same slope or regression line. In practice, batches are pooled to have a common estimate of slope or regression line when there is no significant difference in slope or regression line. Such practice is often applied in pooling across levels of a design factor such as package or strength in a stability study designed with multiple factors. However, falsely pooling different slopes or intercepts may increase false positive rates on the decision of approval for the proposed shelf life. The proposed algorithm used with the simulation technique tries to reduce false pooling rates for testing slope or intercept differences in order to bring down the false positive rates on the decision of approval for the proposed shelf life to the prespecified Type-I error rate of 5 or 10%.

Algorithms↗
PubMed2003

ANCOVA approach for shelf life analysis of stability study of multiple factor designs.

For a traditional multiple batch stability design with no other factor, the conventional analysis is analysis of covariance (ANCOVA) modeling using F-tests based on type I sum of squares to determine whether the batches may be pooled for a common estimate of the linear regression line(s). In the last decade, many multiple factor designs were proposed in stability studies. With the objective of model selection, the generalization of the conventional ANCOVA model using type I sum of squares to designs with multiple factors requires a prespecified hierarchical pooling test ordering to determine whether any of the factors may be eliminated. Different shelf life estimates may be derived using different hierarchical pooling test orderings. On the other hand, setting the hierarchical ordering can be subjective and controversial. The stepwise modeling based on F-tests using type III sum of squares for model determination and factor elimination is proposed to eliminate such difficulties.

Drug Stability↗
PubMed2003

Shelf life determination based on equivalence assessment.

In a regular analysis of covariance (ANCOVA) approach to stability analysis, the decision for pooling data from different batches plays a key role in the determination of the shelf life of the drug product. Conventionally, the decision to pool data for the estimate of slope and intercept of common or individual regression lines is made by "no evidence to reject the null hypothesis of no difference." With typically limited observations, a significance level of much higher than 0.05 was recommended for the pooling tests in order to avoid inflation of type-I error rate of the shelf life testing. This logic of the pooling test decision making discouraged the use of replicates to improve power of testing and precision of estimation. The concept of pooling by equivalence test was originally proposed by Ruberg and Hsu in their 1990 article "Multiple comparison procedures for pooling batches in stability studies" Such a concept has evolved to pooling batches based on the shelf life equivalence test by Yoshioka et al. in their 1996 article "Power of analysis of variance for assessing batch-variation of stability data of pharmaceuticals." In this article, an approximation test of shelf life equivalence and a test of chemical value equivalence for the data pooling decision are proposed as an alternative to the conventional ANCOVA approach.

Confidence Intervals↗
PubMed2002

Utility and pitfalls of some statistical methods in active controlled clinical trials.

Increasingly often, the study objective in an active controlled clinical trial without a placebo arm is to show that a new treatment is no less effective than the active control treatment within some noninferiority range. Two issues behind this objective are that of whether the new treatment is efficacious relative to a putative placebo and that of whether the new treatment preserves a certain fraction of effect of the active control. To address these issues, two types of statistical analysis methods are employed in recent pharmaceutical applications. In one type of method, a noninferiority margin is determined, and then the relative effect of the new treatment versus the control is compared against the margin to test noninferiority and the efficacy of the new treatment. In the other type of method, a synthetic statistic is constructed to directly estimate or test the effect of the new treatment relative to the putative placebo without resorting to noninferiority argument. Preservation of control effect can also be estimated and tested. These methods carry some crucial assumptions. The effect of active control is often estimated from a collection of historical placebo controlled trials using the random effects modeling of DerSimonian and Laird. In this work we find that statistical validity of the latter method rests highly on the assumptions that control effect is not reduced in the current active controlled trial population compared to the historical trials and that a normal approximation is appropriate in the random effects modeling. This type of method is very sensitive to departure from these assumptions. In contrast, the former method is ultraconservative in terms of type I error when the assumptions are met and can be anticonservative when control effect is substantially less in the active controlled trial than estimated from the historical placebo controlled trials.

Controlled Clinical Trials as Topic↗
PubMed2002

Issues related to subgroup analysis in clinical trials.

Interpretation of subgroup findings is a difficult task. The attempt of this article is to clarify confusions on subgroup analysis and to give some practical suggestions on how to avoid mistakes in interpreting subgroup outcome. We believe that the correct interpretation of subgroup findings is closely related to the intrinsic statistical property and validity of the subgroup analysis. A systematic discussion on subgroup analysis from a statistical point of view will be helpful to clinical trial practitioners.

Bias↗