Statistical planning to address strongly correlated endpoints for inferential subgroups: An innovative approach for an illustrative clinical trial with complex multiplicity issues.

Multiplicity is an important statistical issue that arises in clinical trials when the efficacy of the test treatment is evaluated in multiple ways. The major concern for multiplicity is that uncontrolled multiple assessments lead to inflated family-wise Type I error, and they thereby undermine the integrity of the statistical inferences. Multiplicity comes from different sources, for example, making inferences either on the overall population or some pre-specified sub-populations, while multiple endpoints need to be evaluated for each population. Therefore, a sound statistical strategy that controls the family-wise Type I error rate in a strong sense, without excessive loss of power from over-control, is crucial for the success of the trial. For a recent phase III cardiovascular trial with such complex multiplicity, we illustrate the use of a closed testing strategy that begins with a global test; and subsequent testing only proceeds when the global test is rejected. Also, we discuss a simulation study based on this trial to compare the power of the illustrated closed testing strategy to some well-known alternative approaches. We found that this strategy can comprehensively meet most of the primary objectives of the trial effectively with reasonably high overall power.

Multi-regional clinical trial design and consistency assessment of treatment effects.

We can apply both fixed and random effects models to multi-regional clinical trial (MRCT) design and data analysis. Thoroughly, understanding the features of these models in an MRCT setting will help assessing their applicability to an MRCT. In this paper, we discuss the interpretations of trial results from these models. We also evaluate the impact of the number of regions and the sample size configuration across the regions on the required total sample size for the overall treatment effect assessment. For quantifying treatment effects of individual regions, the empirical shrinkage estimator and the James-Stein type shrinkage estimator associate with smaller variability compared with the regular sample estimator. We conduct computation and simulation to compare the performance of these estimators when they are applied to assess consistency of treatment effects across regions. We use a multinational trial example to illustrate the application of these methods.

Graphical assessment of consistency in treatment effect among countries in multi-regional clinical trials.

BACKGROUND: One key objective of a multi-regional clinical trial (MRCT) is to use the trial results to 'bridge' from the global level to local region in support of local registrations. However, data from each individual country are typically limited and the large number of countries will increase the chance of false positive findings. PURPOSE: Graphical tools to facilitate identification of potential outlying countries could be useful for country-level assessment. Existing methods such as funnel plot and expected range of treatment effect can substantially increase the false positive rate. The expected range approach can also have a very low power when there are a large number of small countries, which is typical in a MRCT. METHODS: In this article, we apply normal probability plots, commonly used as a diagnostic tool in linear regression analysis, to assess the differences among countries. Evidence of possible inconsistency, which incorporates both the estimated treatment effect and sample size, is plotted against its expected order statistic. RESULTS: A simulation study is conducted to assess the impact of the negative correlation among residuals due to unequal sample sizes among countries and the performance of the proposed methods compared to existing approaches. The proposed methods tend to have a balanced consideration with substantially smaller false positive rate and reasonable probability to identify outlying countries in realistic scenarios. LIMITATIONS: While much lower than that of commonly used methods, the false positive rates of the proposed methods are not strictly controlled. This may be acceptable for these graphical tools with intention to flag potential outliers for investigation. CONCLUSIONS: We recommend routine use of normal probability plots in MRCTs as a tool to identify potential outliers. If the normal probability plot is approximately linear but has heavy tails with a few outlying countries, these potential outliers should be examined carefully to understand the possible reasons.

An adaptive strategy for assessing regional consistency in multiregional clinical trials.

BACKGROUND: Unexpected regional difference in treatment effect has been reported in recent multiregional clinical trials (MRCTs). This may cause difficulty in interpreting results and can have regulatory implications such as marketing approvals and/or product labels in various markets. Careful consideration of consistency across regions and appropriate plans to address potential regional difference are necessary at the design stage. However, assessment of consistency in treatment effect is generally not the primary objective, and therefore, when there is no strong a priori reason to expect a regional difference, a MRCT is not usually designed to address the regional consistency. Unexpected regional finding may arise and increase the risk of ambiguous or controversial results at the end of the study. PURPOSE: To mitigate this risk, we propose an adaptive strategy for regional assessment based upon accumulated blinded data. METHODS: If review of accumulated blinded data shows unexpectedly severe imbalance in an intrinsic or extrinsic factor, and further assessment indicates that this factor could be a potential effect modifier as supported by biological plausibility or blinded correlation analysis, a stratified regional analysis controlled for this factor may be specified and documented before database lock. RESULTS: The proposed adaptive strategy can help with the interpretation of unexpected regional finding. A recent trial is used to illustrate the approach. LIMITATIONS: Even if the imbalanced factor may appear to explain away the regional difference, establishment of causal effect is usually difficult and requires more involved effort. CONCLUSIONS: This approach, by prespecifying the stratified analysis, can reduce the risk of post hoc exaggerated emphases across many possible exploratory analyses and provide greater confidence in the validity of the conclusions. If a causal effect can be established that the apparent regional difference is likely caused by this intrinsic or extrinsic factor, this prespecified analysis can also help guide clinical practice.

Patient journey through cases of depression from claims database using machine learning algorithms.

Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the analysis of real-world data has been hitherto under debate and research on visualizing the patient journey is still inconclusive. So far, to classify diseases based on medical histories and patient demographic background and to predict the patient prognosis for each disease, the correlation structure of real-world data has been estimated by machine learning. Therefore, we applied association analysis to real-world data to consider a combination of disease events as the patient journey for depression diagnoses. However, association analysis makes it difficult to interpret multiple outcome measures simultaneously and comprehensively. To address this issue, we applied the Topological Data Analysis (TDA) Mapper to sequentially interpret multiple indices, thus obtaining a visual classification of the diseases commonly associated with depression. Under this approach, the visual and continuous classification of related diseases may contribute to precision medicine research and can help pharmaceutical companies provide appropriate personalized medical care.

Ethical considerations in industry-sponsored multiregional clinical trials.

During the last several decades, the scientific and ethics communities have addressed important ethical issues in medical research, resulting in the elaboration and adoption of concepts, guidelines, and codes. Ethical issues in the conduct of Multiregional Clinical Trials have attracted significant attention mainly in the last two decades. With the globalization of clinical research and the rapid expansion to countries with a limited tradition of biomedical research, sponsors must proactively address local ethical issues, the adequacy of oversight as well as the applicability and validity of data, and scientific conclusions drawn from diverse patient populations. This paper highlights some core ethical principles and milestones in medical research, and, from an industry perspective, it discusses ethical issues that the clinical trial team may face when conducting Multiregional Clinical Trials (MRCT, clinical trials conducted at sites located across multiple geographic regions of the world). This paper further highlights the areas of consensus and controversies and proposes points to consider.

Assessing consistent treatment effect in a multi-regional clinical trial: a systematic review.

A key issue in multi-regional clinical trials (MRCTs) is how to assess the consistency of treatment effect across regions, although there is no a priori reason to believe that the treatment effect should vary across the regions. In this article, we define the research question as an assessment of overall consistency across all regions for which all regions are considered equally important. This is different from the region/country-specific analyses (e.g. US vs Non-US), which are frequently requested by local regulatory agencies and usually performed for multiple agencies. We provide a systematic review of methods that may potentially be used for assessing consistency across regions, including commonly used quantitative/qualitative interaction tests, Japanese Pharmaceutical Medical Device Agency (PMDA) Methods 1 & 2, and those proposed for different purposes (e.g. bridging studies, meta-analysis, and vaccine lot consistency, among others). These methods are classified into three groups: global methods, multivariate quantitative methods, and multivariate qualitative methods. A case study is used to illustrate these methods. We also provide recommendations on how to choose appropriate methods and incorporate them in the study design.

Lessons Learned From Multi-regional Trials With Signals of Treatment Effect Heterogeneity.

Inconsistent results across regions have been reported in a number of recent large trials. In this research, by reviewing results from studies that showed inconsistent treatment effects, and summarizing lessons learned, we provide some recommendations for minimizing the chance of inconsistency and allowing more accurate interpretation when such signs of heterogeneity arise, for example: keep the number of regions for consistency evaluation at a minimum to avoid observing false inconsistency signals; proactively address in the protocol the differences in culture, medical practices, and other factors that are potentially different across regions; closely monitor the blinded data from early-enrolled patients to more effectively identify and address issues such as imbalance of baseline covariates or inconsistency of primary outcome rates across regions. For treatments of life-threatening conditions, the stakes for accurate interpretation of MRCT results are high; the criteria for decisions warrant careful consideration.

Design of the DEFINE trial: determining the EFficacy and tolerability of CETP INhibition with AnacEtrapib.

BACKGROUND: Residual cardiovascular (CV) risk often remains high despite statin therapy to lower low-density lipoprotein cholesterol (LDL-C). New therapies to raise high-density lipoprotein cholesterol (HDL-C) are currently being investigated. Anacetrapib is a cholesteryl ester transfer protein (CETP) inhibitor that raises HDL-C and reduces LDL-C when administered alone or with a statin. Adverse effects on blood pressure, electrolytes, and aldosterone levels, seen with another drug in this class, have not been noted in studies of anacetrapib to date. METHODS: Determining the EFficacy and Tolerability of CETP INhibition with AnacEtrapib (DEFINE) is a randomized, double-blind, placebo-controlled trial to assess the efficacy and safety profile of anacetrapib in patients with coronary heart disease (CHD) or CHD risk equivalents (clinical trials.gov NCT00685776). Eligible patients at National Cholesterol Education Program-Adult Treatment Panel III LDL-C treatment goal on a statin, with or without other lipid-modifying medications, are treated with anacetrapib, 100 mg, or placebo for 18 months, followed by a 3-month, poststudy follow-up. The primary end points are percent change from baseline in LDL-C and the safety and tolerability of anacetrapib. Comprehensive preplanned interim safety analyses will be performed at the 6- and 12-month time points to examine treatment effects on key safety end points, including blood pressure and electrolytes. A preplanned Bayesian analysis will be performed to interpret the CV event distribution, given the limited number of events expected in this study. RESULTS: A total of 2,757 patients were screened at 153 centers in 20 countries, and 1,623 patients were randomized into the trial. Lipid results, clinical CV events, and safety outcomes from this trial are anticipated in 2010.

Assessing Treatment Effects That Capture Disease Burden in Serious Chronic Diseases.

Serious and chronic health conditions such as cardiovascular diseases (CVDs) are posing challenges to the health system. Recently clinical trials in these fields have focused on composite endpoints that take into account both disease-related mortality and major disease-related morbidity events. It is the time to the first component of the composite endpoint experienced by a patient that is the traditional study endpoint and treatment aims are to delay the time to the first event and to reduce its frequency. As the name implies, the time-to-first composite event analysis approach focuses only on the first composite event and ignores subsequent events. For a chronic disease, this can lead to a substantial loss of potentially important information. For instance, in chronic heart failure (HF) studies, the traditional composite endpoint of HF-related hospitalizations and CVD death will ignore CVD deaths that are preceded by HF-related hospitalizations. This paper explores the limitations of the traditional time-to-first event approach and discusses the potential value of incorporating all events. The authors argue that endpoints capturing recurrent event information can lead to interpretable measures of treatment effect that better reflect disease burden than traditional time-to-first event endpoints by using the available information beyond the first event. This paper aims to raise awareness of the value and potential pitfalls of alternative treatment effect measures to facilitate meaningful cross-functional conversations among trialists and other stakeholders such as regulators, payers, and treating physicians who all are striving to the same goal-to deliver the most effective treatments to patients.

Designing A Pathogen-Focused Study To Address The High Unmet Medical Need Represented By Carbapenem-Resistant Gram-Negative Pathogens - The International, Multicenter, Randomized, Open-Label, Phase 3 CREDIBLE-CR Study.

Carbapenem-resistant (CR) Gram-negative infections, including those caused by Enterobacteriaceae and the non-fermenters, represent the greatest unmet need for new effective treatments. The clinical development of new antibiotics for the treatment of CR infections is challenging and should focus on the individual pathogens irrespective of the infection site. However, the drug approval pathway is generally infection-site specific and rarely includes such drug-resistant pathogens. To overcome this limitation, a streamlined clinical development program may include a pathogen-focused clinical study, such as the CREDIBLE-CR study, to meet the expectations of some health authorities (ie, the European Medicines Agency [EMA]) and the medical community. Cefiderocol is a novel siderophore cephalosporin designed to target CR pathogens, including CR strains of Enterobacteriaceae (CRE), Pseudomonas aeruginosa, Acinetobacter baumannii, and also Stenotrophomonas maltophilia, which is intrinsically CR. The CREDIBLE-CR study was planned to evaluate cefiderocol in patients with CR Gram-negative infections regardless of species or infection-site source. Rapid diagnostic testing and/or selective media were provided to facilitate detection of CR pathogens to rapidly enroll patients with nosocomial pneumonia, bloodstream infection/sepsis, or complicated urinary tract infection. Patients were randomized 2:1 to receive cefiderocol or best available therapy. There were no pre-specified statistical hypotheses for this study, as the sample size was driven by enrollment feasibility and not based on statistical power calculations. The objective of the CREDIBLE-CR study was to provide descriptive evidence of the efficacy and safety of cefiderocol for the target population of patients with CR infections, including the non-fermenters. The CREDIBLE-CR study is currently the largest pathogen-focused, randomized, open-label, prospective, Phase 3 clinical study to investigate a new antibiotic in patients with CR Gram-negative infections. Here we describe the design of this pathogen-focused study and steps taken to aid patient enrollment into the study within an evolving regulatory environment. CLINICALTRIALSGOV REGISTRATION: NCT02714595. EUDRA-CT REGISTRATION: 2015-004703-23.

Example-based illustrations of design, conduct, analysis and result interpretation of multi-regional clinical trials.

Extensive research has been conducted in the Multi-Regional Clinical Trial (MRCT) area. To effectively apply an appropriate approach to a MRCT, we need to synthesize and understand the features of different approaches. In this paper, examples are used to illustrate considerations regarding design, conduct, analysis and interpretation of result of MRCTs. We start with a brief discussion of region definitions and the scenarios where different regions have differing requirements for a MRCT. We then compare different designs and models as well as the corresponding interpretation of the results. We highlight the importance of paying special attention to trial monitoring and conduct to prevent potential issues associated with the final trial results. Besides evaluating the overall treatment effect for the entire MRCT, we also consider other key analyses including quantification of regional treatment effects within a MRCT, and assessment of consistency of these regional treatment effects.

Defining Regions in Multiregional Clinical Trials: An Analytical Approach to Considering Impact of Intrinsic and Extrinsic Factors.

BACKGROUND: This article describes follow-up work extended from Tanaka et al (2011) in defining the region in multiregional clinical trials (MRCTs). The previous paper advocated a systematic approach to defining regions and recommended the sponsor to think through carefully, prespecify, and justify any regional definitions as well as obtain regulatory concurrence prior to study conduct. Particular attention was advised for intrinsic and extrinsic factors such as race/ethnicity, disease epidemiology, medical practice, and geographic proximity. An analytical approach such as a cluster method to define region was introduced, and references on observed regional differences were provided. METHODS: A closer review of those references and others from a more comprehensive literature search was conducted to gain a deeper understanding of intrinsic and extrinsic factors in relation to the observed regional differences in the treatment effect. The application of a k-means cluster method to define region was explored, as well as the implication to regional sample size for MRCTs. RESULTS: Of extrinsic factors, diagnostic capabilities, medical/clinical practice such as aspirin use, patient care such as site's usual practice, intervention, capability, and concomitant medications were considered potential contributors to differences in clinical outcomes or measures. Commonly reported intrinsic factors were underlying etiology, epidemiology, genetics, and patient characteristics (age, ethnicity, race, risk factors). CONCLUSION: Using a checklist to identify intrinsic/extrinsic factors that might lead to differences in treatment effect allows one to scientifically define more meaningful regions from the identified factors, which will help with estimating the relative treatment outcome as well as exploring appropriate regional sample size.

Pre-study feasibility and identifying sensitivity analyses for protocol pre-specification in comparative effectiveness research.

The use of healthcare databases for comparative effectiveness research (CER) is increasing exponentially despite its challenges. Researchers must understand their data source and whether outcomes, exposures and confounding factors are captured sufficiently to address the research question. They must also assess whether bias and confounding can be adequately minimized. Many study design characteristics may impact on the results; however, minimal if any sensitivity analyses are typically conducted, and those performed are post hoc. We propose pre-study steps for CER feasibility assessment and to identify sensitivity analyses that might be most important to pre-specify to help ensure that CER produces valid interpretable results.

PCPT: Evidence that finasteride reduces risk of most frequently detected intermediate- and high-grade (Gleason score 6 and 7) cancer.

OBJECTIVES: To determine the effect of finasteride relative to placebo on prostate cancer (PCa) risk at each individual Gleason score in the Prostate Cancer Prevention Trial using a post hoc generalization of a prespecified, exploratory, biopsy sampling density-adjusted analysis. METHODS: The Prostate Cancer Prevention Trial enrolled 18 882 men aged >or=55 years with a prostate-specific antigen level of <3.0 ng/mL and normal digital rectal examination findings, and randomized them to finasteride 5 mg daily or placebo. PCa data from evaluable biopsies obtained within 7 years plus

A model-based approach in the estimation of the maximum tolerated dose in phase I cancer clinical trials.

The primary aim of a phase I cancer clinical trial is to determine the maximum tolerated dose (MTD) of a new agent. The MTD is determined as the highest dose level of a potential therapeutic agent at which the patients have experienced an acceptable level of dose limiting toxicity. Although many other types of designs have been proposed in recent years, the traditional algorithm-based designs, especially the 3+3 designs, are still widely used due to their practical simplicity. Simulation studies have shown that the traditional algorithm-based designs cannot provide reasonable estimates of the MTD due to their intrinsic design limitations. In this paper, we propose a model-based approach in the estimation of the MTD following a traditional 3+3 design. Simulation results indicate that our model-based approach produces much less biased estimates of the MTD compared to the estimates obtained from the traditional 3+3 designs. Furthermore, our model-based approach can be easily extended to any traditional A+B design.

Decision rule based multiplicity adjustment strategy.

To minimize potential controversies in determining the need for multiplicity adjustment for multiple hypotheses, we propose a decision rule based multiplicity adjustment strategy in this paper. Resorting to a predefined decision rule of a clinical trial, one may link the different hypotheses by their logical relationships and divide them into different families. A proper multiplicity adjustment procedure can then be developed by maintaining strong control of Type I error rate within each family. The paper applies the proposed multiplicity adjustment strategy to a published raloxifene clinical trial.