Association of quality of life with laboratory measurements and lifestyle factors in community dwelling older people in Taiwan.

OBJECTIVES: Little is known about the influence of routine laboratory measurements and lifestyle factors on generic quality of life (QOL) at older ages. We aimed to study the relationship between generic QOL and laboratory measurements and lifestyle factors in community dwelling older Chinese people. METHODS: We conducted a cross-sectional analysis. Six hundred and ninety nine elders were randomly selected from the examinees of the annual health examination in Taipei City, Taiwan. Blood, urine and stool of the participants were examined and lifestyle data were collected. Participants completed the CASP-19 (control, autonomy, self-realization, pleasure) questionnaire, a 19-item QOL scale. The relationship between QOL and laboratory results and lifestyle factors was explored, using multiple linear regression and profile analysis. RESULTS: The mean age of the participants was 75.5 years (SD = 6.5), and 49.5% were female. Male gender standardized ß coefficients (ß = 0.122) and exercise habit (ß = 0.170) were associated with a better QOL, whereas advanced age (ß = -0.242), blurred vision (ß = -0.143), depression (ß = -0.125), central obesity (ß = -0.093), anemia (ß = -0.095), rheumatoid arthritis (ß = -0.073), Parkinsonism (ß = -0.079), malignancy (ß = -0.086) and motorcycle riding (ß = -0.086) were associated with a lower QOL. Profile analysis revealed that young-old males, social drinkers, regular exercisers and car drivers had the best QOL (all p < 0.001). CONCLUSION: Of the many laboratory measurements, only anemia was associated with the lower QOL. By contrast, several lifestyle factors, such as social drinking, exercise habit and car driving, were associated with better QOL, whereas abdominal obesity and motorcycle riding were associated with lower QOL.

An approximate approach to sample size determination in bioequivalence testing with multiple pharmacokinetic responses.

The approval of generic drugs requires the evidence of average bioequivalence (ABE) on both the area under the concentration-time curve and the peak concentration Cmax . The bioequivalence (BE) hypothesis can be decomposed into the non-inferiority (NI) and non-superiority (NS) hypothesis. Most of regulatory agencies employ the two one-sided tests (TOST) procedure to test ABE between two formulations. As it is based on the intersection-union principle, the TOST procedure is conservative in terms of the type I error rate. However, the type II error rate is the sum of the type II error rates with respect to each null hypothesis of NI and NS hypotheses. When the difference in population means between two treatments is not 0, no close-form solution for the sample size for the BE hypothesis is available. Current methods provide the sample sizes with either insufficient power or unnecessarily excessive power. We suggest an approximate method for sample size determination, which can also provide the type II rate for each of NI and NS hypotheses. In addition, the proposed method is flexible to allow extension from one pharmacokinetic (PK) response to determination of the sample size required for multiple PK responses. We report the results of a numerical study. An R code is provided to calculate the sample size for BE testing based on the proposed methods.

Applications of the bayesian prior information to evaluation of equivalence of similar biological medicinal products.

In 2014, there are a number of patents of best-selling biotech drugs around the world about to expire. Large commercial interests will inevitably set off international competition among major pharmaceutical companies. The European Union, the United States, and other countries with advanced medical techniques have paid a high degree of attention to biological similarity (biosimilarity) for drug development and market management and have started to take corresponding measures. Because of the diverse definitions of biosimilarity between countries, which can determine whether the medicines on sale are in the review of the base grant, the competent authority will also encounter varying degrees of standards. Governments should review the corresponding guidelines as soon as possible because many countries around the world have actively amended the law in response to management need of biological similarity. The similarity of biological drugs should be assessed by clinical trial under current regulations. Pharmaceutical companies try to lower the cost of generic drugs to increase the competitiveness of their products. To reduce the number of subjects in the clinical trials for development of generic drugs, we refer to the clinical trial for evaluation of bridging studies. Hsiao et al. (2007) has proposed the use of Bayes method in evaluation of bridging studies. Prior information on the original region is used to reduce the number of subjects to lower the cost of biosimilar drug development. Take the results of the approval biologics as a priori information, and the information is appropriate to be embedded into the model evaluating the similar products in order to reduce the sample size required for the assessment of the test sample.

Application of the parallel line assay to assessment of biosimilar products based on binary endpoints.

Biological drug products are therapeutic moieties manufactured by a living system or organisms. These are important life-saving drug products for patients with unmet medical needs. Because of expensive cost, only a few patients have access to life-saving biological products. Most of the early biological products will lose their patent in the next few years. This provides the opportunity for generic versions of the biological products, referred to as biosimilar drug products. The US Biologic Price Competition and Innovation Act passed in 2009 and the draft guidance issued in 2012 provide an approval pathway for biological products shown to be biosimilar to, or interchangeable with, a Food and Drug Administration-licensed reference biological product. Hence, cost reduction and affordability of the biosimilar products to the average patients may become possible. However, the complexity and heterogeneity of the molecular structures, complicated manufacturing processes, different analytical methods, and possibility of severe immunogenicity reactions make evaluation of equivalence between the biosimilar products and their corresponding reference product a great challenge for statisticians and regulatory agencies. To accommodate the stepwise approach and totality of evidence, we propose to apply a parallel assay to evaluate the extrapolation of the similarity in product characteristics such as doses or pharmacokinetic responses to the similarity in binary efficacy endpoints. We also report the results of simulation studies to evaluate the performance, in terms of size and power, of our proposed methods. We present numerical examples to illustrate the suggested procedures.

An approximate approach to sampling size determination for the equivalence hypothesis.

The equivalence hypothesis is the correct hypothesis to confirm whether the new test product conforms to the standard reference product. It has many applications to evaluation of generic drug products and other new clinical modalities. The two one-sided tests (TOST) procedure was proposed to test the equivalence hypothesis for two treatments. When the difference in population means between two treatments is not 0, the proportion of the type II error rate allocated to each of the two tails of the central t-distribution cannot be analytically determined. Hence, no close form of the exact sample size for the equivalence hypothesis is available. Currently only approximate formulas are proposed. The resulting sample sizes may provide either insufficient power or unnecessarily excessive power. We suggest an approximate approach with consideration of type II error rates for both one-sided hypotheses to determination of the sample size for the equivalence hypothesis. The results of a numerical study are reported. Remarks on the usage of different methods for the sample size determination for the equivalence hypothesis in practical applications are provided.

Statistical inference on censored data for targeted clinical trials under enrichment design.

For the traditional clinical trials, inclusion and exclusion criteria are usually based on some clinical endpoints; the genetic or genomic variability of the trial participants are not totally utilized in the criteria. After completion of the human genome project, the disease targets at the molecular level can be identified and can be utilized for the treatment of diseases. However, the accuracy of diagnostic devices for identification of such molecular targets is usually not perfect. Some of the patients enrolled in targeted clinical trials with a positive result for the molecular target might not have the specific molecular targets. As a result, the treatment effect may be underestimated in the patient population truly with the molecular target. To resolve this issue, under the exponential distribution, we develop inferential procedures for the treatment effects of the targeted drug based on the censored endpoints in the patients truly with the molecular targets. Under an enrichment design, we propose using the expectation-maximization algorithm in conjunction with the bootstrap technique to incorporate the inaccuracy of the diagnostic device for detection of the molecular targets on the inference of the treatment effects. A simulation study was conducted to empirically investigate the performance of the proposed methods. Simulation results demonstrate that under the exponential distribution, the proposed estimator is nearly unbiased with adequate precision, and the confidence interval can provide adequate coverage probability. In addition, the proposed testing procedure can adequately control the size with sufficient power. On the other hand, when the proportional hazard assumption is violated, additional simulation studies show that the type I error rate is not controlled at the nominal level and is an increasing function of the positive predictive value. A numerical example illustrates the proposed procedures.

Statistical methods for bridging studies.

In 1998, the International Conference on Harmonization (ICH) published a guidance to facilitate the registration of medicines among ICH regions including the European Union, the United States, and Japan by recommending a framework for evaluating the impact of ethnic factors on a medicine's effect, such as its efficacy and safety at a particular dosage and dose regimen (ICH E5, 1998). The purpose of ICH E5 is not only to evaluate the ethnic factor influence on safety, efficacy, dosage, and dose regimen, but also more importantly to minimize duplication of clinical data and allow extrapolation of foreign clinical data to a new region. In this article, statistical methods for evaluation of bridging studies based on the concepts of consistency (Shih, 2001), reproducibility/generalizability (Shao and Chow, 2002), the weighted Z-tests for the design of bridging studies (Lan et al., 2005), and similarity between the new and original region based in terms of positive treatment effect (Hsiao et al., 2007) are studied. The relative merits and disadvantages of these methods are compared by several examples.

An inferential procedure for the probability of passing the USP dissolution test.

Dissolution is one of the tests that is required and specified by the United States Pharmacopeia and National Formulary (USP/NF) to ensure that the drug products meet the standards of the identity, strength, quality, purity, and stability. The sponsors also establish the in-house specifications for the mean and standard deviation of the dissolution rates to guarantee a high probability of passing the USP/NF dissolution test. However, the USP/NF dissolution test is a complicated three-stage sampling plan that involves both the sample mean dissolution rate of all units and the dissolution rate of individual units. It turns out that the true probability of passing the USP/NF dissolution is formidable to compute analytically even when the population mean and variance of dissolution rates are known. It is not clear that previously proposed methods are the estimators of the true probability for passing the USP dissolution test. Therefore, we propose to employ a parametric bootstrap method in conjunction with the Monte Carlo simulation to obtain the sampling distribution of the estimated probabilities of passing the USP/NF dissolution test and hence the confidence interval for the passing probability. In addition, a procedure is proposed to test whether the true probability of passing the USP/NF dissolution test is greater than some specified value. A numerical example illustrates the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.

Predicting healthcare utilization using a pharmacy-based metric with the WHO's Anatomic Therapeutic Chemical algorithm.

BACKGROUND: Automated pharmacy claim data have been used for risk adjustment on health care utilization. However, most published pharmacy-based morbidity measures incorporate a coding algorithm that requires the medication data to be coded using the US National Drug Codes or the American Hospital Formulary Service drug codes, making studies conducted outside the US operationally cumbersome. OBJECTIVE: This study aimed to verify that the pharmacy-based metric with the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) algorithm can be used to explain the variations in health care utilization. RESEARCH DESIGN: The Longitudinal Health Insurance Database of Taiwan's National Health Insurance enrollees was used in this study. We chose 2006 as the baseline year to predict the total cost, medication cost, and the number of outpatient visits in 2007. The pharmacy-based metric with 32 classes of chronic conditions was modified from a revised version of the Chronic Disease Score. RESULTS: The ordinary least squares (OLS) model and log-transformed OLS model adjusted for the pharmacy-based metric had a better R in concurrently predicting total cost compared with the model adjusted for Deyo's Charlson Comorbidity Index and Elixhauser's Index. The pharmacy-based metric models also provided a superior performance in predicting medication cost and number of outpatient visits. For prospectively predicting health care utilization, the pharmacy-based metric models also performed better than the models adjusted by the diagnosis-based indices. CONCLUSIONS: The pharmacy-based metric with the WHO ATC algorithm and the matching ATC codes were tested and found to be valid for explaining the variation in health care utilization.

A consistency approach to evaluation of bridging studies and multi-regional trials.

Recently, global drug developments have attracted much attention from sponsors as well as regulatory authorities. 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. On the other hand, a multi-regional trial may incorporate subjects from many regions around the world under the same protocol so that after showing the overall efficacy of a drug in all global regions, we can simultaneously evaluate the possibility of applying the overall trial results to each region and consequently support drug registration in each region. In this paper, we develop a consistency approach for assessment of similarity between a bridging study conducted in a new region and studies conducted in the original region. A statistical criterion is also established to assess the consistency between the region of interest and overall results in a multi-regional trial. The method for sample size determination for the bridging study is also proposed. Numerical examples illustrate applications of the proposed approaches in different scenarios.

An alternative phase II/III design for continuous endpoints.

The success rate of drug development has been declined dramatically in recent years and the current paradigm of drug development is no longer functioning. It requires a major undertaking on breakthrough strategies and methodology for designs to minimize sample sizes and to shorten duration of the development. We propose an alternative phase II/III design based on continuous efficacy endpoints, which consists of two stages: a selection stage and a confirmation stage. For the selection stage, a randomized parallel design with several doses with a placebo group is employed for selection of doses. After the best dose is chosen, the patients of the selected dose group and placebo group continue to enter the confirmation stage. New patients will also be recruited and randomized to receive the selected dose or placebo group. The final analysis is performed with the cumulative data of patients from both stages. With the pre-specified probabilities of rejecting the drug at each stage, sample sizes and critical values for both stages can be determined. As it is a single trial with controlling overall type I and II error rates, the proposed phase II/III adaptive design may not only reduce the sample size but also improve the success rate. An example illustrates the applications of the proposed phase II/III adaptive design.

Statistical assessment of biosimilar products.

Biological products or medicines are therapeutic agents that are produced using a living system or organism. Access to these life-saving biological products is limited because of their expensive costs. Patents on the early biological products will soon expire in the next few years. This allows other biopharmaceutical/biotech companies to manufacture the generic versions of the biological products, which are referred to as follow-on biological products by the U.S. Food and Drug Administration (FDA) or as biosimilar medicinal products by the European Medicine Agency (EMEA) of the European Union (EU). Competition of cost-effective follow-on biological products with equivalent efficacy and safety can cut down the costs and hence increase patients' access to the much-needed biological pharmaceuticals. Unlike for the conventional pharmaceuticals of small molecules, the complexity and heterogeneity of the molecular structure, complicated manufacturing process, different analytical methods, and possibility of severe immunogenicity reactions make evaluation of equivalence (similarity) between the biosimilar products and their corresponding innovator product a great challenge for both the scientific community and regulatory agencies. In this paper, we provide an overview of the current regulatory requirements for approval of biosimilar products. A review of current criteria for evaluation of bioequivalence for the traditional chemical generic products is provided. A detailed description of the differences between the biosimilar and chemical generic products is given with respect to size and structure, immunogenicity, product quality attributed, and manufacturing processes. In addition, statistical considerations including design criteria, fundamental biosimilar assumptions, and statistical methods are proposed. The possibility of using genomic data in evaluation of biosimilar products is also explored.

Statistical test for evaluation of biosimilarity in variability of follow-on biologics.

As more biologic products are going off patent protection, the development of follow-on biologics products has received much attention from both biotechnology industry and the regulatory agencies. Unlike small-molecule drug products, the development of biologic products is very different and variable via the manufacture process and environment. Thus, Chow et al. (2010) suggested that the assessment of biosimilarity between biologic products focus on variability rather than average biosimilarity. In addition, it is also suggested that a probability-based criterion, which is more sensitive to variability, should be employed. In this article, we propose a probability-based asymptotic statistical testing procedure to evaluate biosimilarity in variability of two biologic products. A numerical study is conducted to investigate the relationship between the probability-based criterion in variability and various study parameters. Simulation studies were also conducted to empirically investigate the performance of the proposed probability-based asymptotic statistical testing procedure in term of empirical sizes and powers. A numerical example is provided to illustrate the proposed methods.

Sample size determination for a specific region in a multiregional trial.

Recently, geotherapeutics have attracted much attention from sponsors as well as regulatory authorities. A bridging study defined by the International Conference on Harmonisation (ICH) E5 is usually conducted in the new region after the test product has been approved for commercial marketing in the original region due to its proven efficacy and safety. However, extensive duplication of clinical evaluation in the new region not only requires valuable development resources but also delays availability of the test product to the needed patients in the new regions. To shorten the drug lag or the time lag for approval, simultaneous drug development, submission, and approval in the world may be desirable. On September 28, 2007, the Ministry of Health, Labour and Welfare (MHLW) in Japan published the "Basic Principles on Global Clinical Trials" guidance related to the planning and implementation of global clinical studies. The 11th question and answer for the ICH E5 guideline also discuss the concept of a multiregional trial. Both guidelines have established a framework on how to demonstrate the efficacy of a drug in all participating regions while also evaluating the possibility of applying the overall trial results to each region by conducting a multiregional trial. In this paper, we focus on a specific region and establish statistical criteria for consistency between the region of interest and overall results. More specifically, four criteria are considered. Two criteria are to assess whether the treatment effect in the region of interest is as large as that of the other regions or of the regions overall, while the other two criteria are to assess the consistency of the treatment effect of the specific region with other regions or the regions overall. Sample size required for the region of interest can also be evaluated based on these four criteria.

Proposals of statistical consideration to evaluation of results for a specific region in multi-regional trials--Asian perspective.

In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and to shorten approval time, the design of multi-regional trials incorporates subjects from many countries around the world under the same protocol. After showing the overall efficacy of a drug in all global regions, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each of them. Recently, the trend for simultaneous clinical development in Asian countries being undertaken simultaneously with clinical trials conducted in Europe and the United States has been rapidly rising. In this paper, proposals of statistical consideration to multi-regional trials are provided. More specifically, three aspects are addressed: the definition of the 'Asian region,' the consistency criterion between the 'Asian region' and the overall regions, and the sample size determination for the multi-regional trial.

Statistical inference for the within-device precision of quantitative measurements in assay validation.

Intermediate precision is one of the most important characteristics for evaluation of precision in assay validation. The current methods for evaluation of within-device precision recommended by the Clinical Laboratory Standard Institute (CLSI) guideline EP5-A2 are based on the point estimator. On the other hand, in addition to point estimators, confidence intervals can provide a range for the within-device precision with a probability statement. Therefore, we suggest a confidence interval approach for assessment of the within-device precision. Furthermore, under the two-stage nested random-effects model recommended by the approved CLSI guideline EP5-A2, in addition to the current Satterthwaite's approximation and the modified large sample (MLS) methods, we apply the technique of generalized pivotal quantities (GPQ) to derive the confidence interval for the within-device precision. The data from the approved CLSI guideline EP5-A2 illustrate the applications of the confidence interval approach and comparison of results between the three methods. Results of a simulation study on the coverage probability and expected length of the three methods are reported. The proposed method of the GPQ-based confidence intervals is also extended to consider the between-laboratories variation for precision assessment.

Inference on treatment effects for targeted clinical trials under enrichment design.

After completion of a human genome project, the disease targets at molecular level can be identified. As a result, treatment modality for molecular targets can be developed. In practice, targeted clinical trials are usually conducted for evaluation of the possibility and feasibility of the individualized treatment of patients. However, the accuracy of diagnostic devices for identification of such molecular targets is usually not perfect. Therefore, some of the patients enrolled in targeted clinical trials with a positive result by the diagnostic device might not have the specific molecular targets and hence the treatment effects of the targeted drugs estimated from targeted clinical trials could be biased for the patient population truly with the molecular targets. Under an enrichment design for targeted clinical trials, we propose to use the EM algorithm and bootstrap method for obtaining the inference of the treatment effects of the targeted drugs in the patient population truly with molecular targets. A simulation study was conducted to empirically investigate the bias and variability of the proposed estimator and the size and power of the proposed testing method. Simulation results demonstrate that the proposed estimator is unbiased with adequate precision and the confidence interval can provide satisfactory coverage probability. In addition, the proposed testing procedure can adequately control the size with sufficient power. A practical example illustrates the utility of the proposed method.

Statistical issues on the diagnostic multivariate index assay for targeted clinical trials.

In the past decade, pharmacogenomics and microarrays are considered two of the most important scientific breakthroughs for detection and treatment of diseases with many other applications. After completion of the Human Genome Project (HGP), the importance of diagnostic tests for identification of molecular targets increases as more targeted clinical trials are conducted for the individualized treatment of patients in the post-genomic era. As a result, the co-development of drug-device has become the foundation for achieving the ultimate goal of personalized medicine. One of the diagnostic devices for detection of molecular targets is the in vitro diagnostic multivariate index assays (IVDMIA) based on the genomic composite biomarker (GCB) classifiers. Thus, the quality of the IVDMIA, innovative designs, and the evaluation of efficacy for targeted clinical trials are vital for achieving this goal. However, before personalized medicine becomes a reality, many challenges for assurance of the accuracy and precision of the IVDMIA and the estimates of the treatment effect in the population with the molecular targets are to be resolved. In this paper, we identify the following issues on the IVDMIA and targeted clinical trials: (i) the selection of the differentially expressed genes, (ii) the optimal representation and algorithm for the genomic composite biomarker (GCB) classifier for the best diagnostic accuracy of the molecular target, (iii) the validation of the IVDMIA, and (iv) the evaluation of effectiveness and sample size estimation for targeted clinical trials. For each issue, the problem and possible resolutions are discussed. An overall assessment and some concluding remarks are also provided.

On statistical evaluation of the linearity in assay validation.

Linearity is one of the most important characteristics for evaluation of the accuracy in assay validation. The current statistical method for evaluation of the linearity recommended by the Clinical Laboratory Standard Institute (CLSI) guideline EP6-A is reviewed. The method directly compares the point estimates with the pre-specified allowable limit and completely ignores the sampling error of the point estimates. An alternative method for evaluation of linearity, proposed by Kroll et al. (2000), considers the statistical test procedure based on the average deviation from linearity (ADL). However this procedure is based on an inappropriate formulation of hypotheses for the evaluation of linearity. Consequently, the type I error rates of both current methods may be inflated for inference of linearity. To claim the linearity of analytical methods, we propose that the hypothesis of proving the linearity should be formulated as the alternative hypothesis. Furthermore, any procedures for assessment of linearity should be based on the sampling distributions of the proposed test statistics. Therefore, we propose a two one-sided test (TOST) procedure and a corrected Kroll's procedure. The simulation studies were conducted to empirically compare the size and power between current and proposed methods. The simulation results show that the proposed methods not only adequately control size but also provide sufficient power. A numeric example illustrates the proposed methods.

A permutation two one-sided tests procedure to detect minimal fold changes of gene expression levels.

Current approaches to identifying differentially expressed genes are based either on the fold changes or on the traditional hypotheses of equality. However, the fold changes do not take into consideration the variation in estimation of the average expression. In addition, the use of fold changes is not in the frame of hypothesis testing, and hence the probability associated with errors for decision making of identification of differentially expressed genes cannot be quantified and evaluated. On the other hand, the traditional hypothesis of equality fails to take into consideration the magnitudes of the biologically meaningful fold changes that truly differentiate the genes between populations. Because of the large number of genes tested and small number of samples available for microarray experiments, the false positive rate for differentially expressed genes is quite high and requires further multiplicity adjustments, or use of an arbitrary cutoff for the p-values. However, all these adjustments do not have any biological justification. Hence, based on the interval hypothesis, Liu and Chow proposed a two one-sided tests procedure by consideration of both the minimal biologically meaningful fold changes and statistical significance simultaneously. To incorporate the correlation structure of expression levels among different genes and possible violation of normality assumption, we propose to apply a permutation method to the two one-sided tests procedure. A simulation study is conducted to empirically compare the type I error rate and power of the procedures based on the traditional hypothesis and the proposed permutation two one-sided tests procedure based on the interval hypothesis under various combinations of fold changes, variability, and sample sizes. Simulation results show that the proposed permutation two one-sided tests procedure based on the interval hypothesis not only can control the type I error rate at the nominal level but also provide adequate power to detect differentially expressed genes. Numerical data from public domains illustrate the proposed methods.