A post hoc analysis of intra-subject coefficients of variation in pharmacokinetic measures to calculate optimal sample sizes for bioequivalence studies

Because bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics. For the generics with multiple bioequivalence results, pooled intra-CVs were calculated. The estimated intra-CVs of 142 bioequivalence studies were 14.7±8.2% for AUC and 21.7±8.8% for Cmax. Intra-CVs of Cmax were larger than those of area under the concentration-time curve (AUC) in 129 studies (90.8%). For the 26 generics with multiple bioequivalence results, the coefficients of variation of intra-CVs between identical generics (mean±sd (min ~ max)) were 38.0±24.4% (1.9 ~ 105.3%) for AUC and 27.9±18.2% (4.0 ~ 70.1%) for Cmax. These results suggest that substantial variation exists among the bioequivalence results of identical generics. In this study, we presented the intra-CVs of various generics with their pooled intra-CVs. The estimated intra-CVs calculated in this study will provide useful information for planning future bioequivalence studies. (This is republication of the article 'Transl Clin Pharmacol 2017;25:179-182' retracted from critical typographic errors. See the 'Retraction and Republication section of this issue for further information)

Retraction and Republication: A post hoc analysis of intra-subject coefficients of variation in pharmacokinetic measures to calculate optimal sample sizes for bioequivalence studies

The retraction has been agreed upon due to critical typographical errors throughout the contents from accidents at the manuscript editing step.

A post hoc analysis of intra-subject coefficients of variation in pharmacokinetic measures to calculate optimal sample sizes for bioequivalence studies

Because bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics. For the generics with multiple bioequivalence results, pooled intra-CVs were calculated. The estimated intra-CVs of 142 bioequivalence studies were 14.7±8.2% for AUC and 21.7±8.8% for C(max). Intra-CVs of C(max) were larger than those of area under the concentration-time curve (AUC) in 129 studies (90.8%). For the 26 generics with multiple bioequivalence results, the coefficients of variation of intra-CVs between identical generics (mean±sd (min ~ max)) were 38.0±24.4% (1.9 ~ 105.3%) for AUC and 27.9±18.2 % (4.0 ~ 70.1%) for C(max). These results suggest that substantial variation exists among the bioequivalence results of identical generics. In this study, we presented the intra-CVs of various generics with their pooled intra-CVs. The estimated intra-CVs calculated in this study will provide useful information for planning future bioequivalence studies.

An Approach to Survey Data with Nonresponse: Evaluation of KEPEC Data with BMI / 예방의학회지

OBJECTIVES: A common problem with analyzing survey data involves incomplete data with either a nonresponse or missing data. The mail questionnaire survey conducted for collecting lifestyle variables on the members of the Korean Elderly Phamacoepidemiologic Cohort(KEPEC) in 1996 contains some nonresponse or missing data. The proper statistical method was applied to evaluate the missing pattern of a specific KEPEC data, which had no missing data in the independent variable and missing data in the response variable, BMI. METHODS: The number of study subjects was 8,689 elderly people. Initially, the BMI and significant variables that influenced the BMI were categorized. After fitting the log-linear model, the probabilities of the people on each category were estimated. The EM algorithm was implemented using a log-linear model to determine the missing mechanism causing the nonresponse. RESULTS: Age, smoking status, and a preference of spicy hot food were chosen as variables that influenced the BMI. As a result of fitting the nonignorable and ignorable nonresponse log-linear model considering these variables, the difference in the deviance in these two models was 0.0034(df=1). CONCLUSION: There is a lot of risk if an inference regarding the variables and large samples is made without considering the pattern of missing data. On the basis of these results, the missing data occurring in the BMI is the ignorable nonresponse. Therefore, when analyzing the BMI in KEPEC data, the inference can be made about the data without considering the missing data.

Comparison of Efficiency between Individual Randomization and Cluster Randomization in the Field Trial / 예방의학회지

OBJECTIVES: In large-scale field trials, randomization by cluster is frequently used because of the administrative convenience, a desire to reduce the effect of treatment contamination, and the need to avoid ethical issues that might otherwise arise. Cluster randomization trials are experiments in which intact social unit, e.g., families, schools, cities, rather than independent individuals are randomly allocated to intervention groups. The positive correlation among responses of subjects from the same cluster is in matter in cluster randomization. This thesis is to compare the results of three randomization methods by standard error of estimator of treatment effect. METHODS: We simulated cholesterol data varing the size of the cluster and the level of the correlation in clusters and analyzed the effect of cholesterol-lowering agent. RESULTS: In intra-cluster randomization the standard error of the estimator of treatment effect is smallest relative to that in inter-cluster randomization and that in individual randomization. CONCLUSIONS: Intra-cluster randomization is the most efficient in its standard error of estimator of treatment effect but other factor should be considered when selecting a specific randomization method.

The Prevalence of Hypertension in the Rural Area of Korea / 대한내과학회지

OBJECTIVES: To establish prevalence of hypertension in rural area of Korea, we surveyed adult residents older than 30 years, based on the recommendation and classification of JNC-5(Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure). METHODS: From December 1996 to February 1997, we studied 4,209 persons in 41 rural areas purposely sampled nationwide. Blood pressure was checked twice at the time of the first visit and again checked twice after one week later for the person fell under hypertension criteria at the first visit. Persons fell under hypertension criteria at the first visit without second visit for recheck were categorized as suspected hypertension. For the suspected hypertension, we predicted whether fell under criteria by logistic regression model. RESULTS: 1) The distributions of blood pressures show unimodal curve, skewed to the right. The peaks of the systolic blood pressure was between 120~129mmHg, tending to move to the right for the age of 50-and-over in male, 70-and-over in female. But peaks of the diastolic blood pressure were consistent between 80~84mmHg in both sexes. The distributions of blood pressures for male were slightly deviated to the right compared with those of the female. 2) The crude prevalence rate of hypertension, defined as systolic blood pressure > or =140mmHg or diastolic blood pressure > or =90mmHg or taking anti-hypertensive medication, was 25.94%. And the prevalence rate of suspected hypertension was 5.54%. Through the logistic regression model, the prevalence of hypertension was estimated as 29.94%. Age-sex-adjusted prevalence rate for the rural area-Myon regions- was 25.94%, if adjusted to the age-sex composition of the 1995 national census population. 3) Prevalence rate was 27.76% in male and 30.03% in female, if adjusted to the age-sex composition of the base population of this study. Prevalence rate progressively increased with age, higher in men than women before about age 60. 4) Prevalence rates among eight Provinces(Do) was different. Unadjusted rates for Kyonggi Province was 24.74%, and rates for Chonnam Province was 34.18%. But there was no significant differences of the prevalence rate between inland and seaside. 5) By logistic regression model, 65.39% of stage 1 hypertension and 75.51% of stage 2 hypertension at the first visit were estimated as to be included in hypertension criteria. 6) By the JNC-5 classification, only 22.33% of the patients taking anti-hypertensive medication was being controlled. CONCLUSION: The prevalence rate of hypertension by classification of JNC-5 at rural area was 25.94%. We could not find significant differences of prevalence rate between inland and seaside. Follow-up measurement of blood pressures will be needed to establish more valid prevalence rates of hypertension.

Population-adjusted Mean Age at Incidence (PAMA) for Comparing Incidence Patterns with Age in Different Populations / 한국역학회지

Standardized incidence rates have been widely used for comparing incidence patterns between populations, adjusting for differences in demographic structure. These rates can compare overall incidence levels, but to fully understand incidence patterns, an index which links incidence with age is also needed. The authors proposed a statistical method for estimating population-adjusted mean age of incidence (PAMA), based on Poisson distribution and Fieller's theorem. The index was applied with several modifications to data relating to the incidence of breast cancer among Caucasian women living in Los Angeles.

A stochastic analysis on the force of infection by hepatitis B virus in Korea / 한국역학회지

No abstract available.

An Evaluative Analysis of the Referral System for Insurance Patients / 예방의학회지

This study examined the effects of referral requirements for insurance patients which have been enforced since July 1, 1989 when medical insurance coverage was extended to the whole population except beneficiaries of medical assistance program. The requirements are mainly aimed at discourag - ing the use of to Vii; ry care hospitals by imposing restrictions on the patient's choice of a medical service facility. The expectation is that such change in the pattern of medical care utilization would produce several desirable effects including increased efficiency in patient care and balanced development of various types of medical service facilities. In this study, these effects were assessed by the change in the number of out-patient visits and bed-days per illness episode and the share of each type of facility in the volume of services and the amount of expenditures after the implementation of the new referral system. The data for analysis were obtained from the claims to the insurance for government and school employees. The sample was drawn from the claims for the patients treated during the first six months of 1989, prior to the enforcement of referral requirements, and those of the patients treated during the first six months of 1990, after the enforcement. The 1989 sample included 299,824 claims (3.6% of total) and the 1990 sample included 332,131(3.7% of total). The data were processed to make the unit of analysis an illness episode instead of an insurance claim. The facilities and types of care utilized for a given illness episode are defined to make up the pathway of medical care uti lization. This pathway was conceived of as a Markov Chain process for further analysis. The conclusion emerged from the analysis is that the enforcement of referral requirements resulted in less use of tertiary care hospitals, and thereby decreased the volume of services and the amount of insurance expenses per illness episode. However, there are a few points that have to be taken into account in relation to the conclusion. The new referral system is likely to increase the use of medical services not covered by insurance, so that its impact on national health expenditures would be different from. that on insurance expenditures. The extension of insurance coverage must have inereased patient load for all types of medical service organizations, and this increase may be partly responsible for producing the effects attributed to the new referral system. For example, excessive patient load for tertiary care hospitals may lead to the transfer of their patients to other types of facilities. Another point is that the data for this study correspond to very early phase of the new system. But both patients and medical care providers would adapt themselves to the new system to avoid or overcome its disadvantages for them, so as that its effects could change over time. Therefore, it is still necessary to closely monitor the impact of the referral requirements.

Change in Medical Care Utilization over Time in Early Years of Insurance Coverage / 예방의학회지

The purpose of this study is to observe the pattern of change in medical care utilization over time in early years of insurance coverage. The source of data is the benefit records file of a voluntary medical insurance society for covering the four-year period, from 1982 to 1985. The measure of medical care utilization used in this study is the age-sex standardized percentage of the enrollee who have visited a physician over total analytical population during a three-month period. For six cohorts by the year of enrollment (1979-1984), the relationship between the utilization and duration of insurance coverage was examined controlling for the calender year and season. In the analysis, logistic multiple regression and residual analysis were employed. It was observed that medical care utilization rapidly increased during the early stage of insurance coverage, and after then increased at a slower rate over time to become almost stable in about twenty months.