At home adaptive dual target deep brain stimulation in Parkinson's disease with proportional control.

Continuous deep brain stimulation (cDBS) of the subthalamic nucleus (STN) or globus pallidus is an effective treatment for the motor symptoms of Parkinson's disease. The relative benefit of one region over the other is of great interest but cannot usually be compared in the same patient. Simultaneous DBS of both regions may synergistically increase the therapeutic benefit. Continuous DBS is limited by a lack of responsiveness to dynamic, fluctuating symptoms intrinsic to the disease. Adaptive DBS (aDBS) adjusts stimulation in response to biomarkers to improve efficacy, side effects, and efficiency. We combined bilateral DBS of both STN and globus pallidus (dual target DBS) in a prospective within-participant, clinical trial in six patients with Parkinson's disease (n = 6, 55-65 years, n = 2 females). Dual target cDBS was tested for Parkinson's disease symptom control annually over 2 years, measured by motor rating scales, on time without dyskinesia, and medication reduction. Random amplitude experiments probed system dynamics to estimate parameters for aDBS. We then implemented proportional-plus-integral aDBS using a novel distributed (off-implant) architecture. In the home setting, we collected tremor and dyskinesia scores as well as individualized ß and DBS amplitudes. Dual target cDBS reduced motor symptoms as measured by Unified Parkinson's Disease Rating Scale (UPDRS) to a greater degree than either region alone (P < 0.05, linear mixed model) in the cohort. The amplitude of ß-oscillations in the STN correlated to the speed of hand grasp movements for five of six participants (P < 0.05, Pearson correlation). Random amplitude experiments provided insight into temporal windowing to avoid stimulation artefacts and demonstrated a correlation between STN ß amplitude and DBS amplitude. Proportional plus integral control of aDBS reduced average power, while preserving UPDRS III scores in the clinic (P = 0.28, Wilcoxon signed rank), and tremor and dyskinesia scores during blinded testing at home (n = 3, P > 0.05, Wilcoxon ranked sum). In the home setting, DBS power reductions were slight but significant. Dual target cDBS may offer an improvement in treatment of motor symptoms of Parkinson's disease over DBS of either the STN or globus pallidus alone. When combined with proportional plus integral aDBS, stimulation power may be reduced, while preserving the increased benefit of dual target DBS.

Determining the extent and frequency of on-site monitoring: a bayesian risk-based approach.

BACKGROUND: On-site monitoring is a crucial component of quality control in clinical trials. However, many cast doubt on its cost-effectiveness due to various issues, such as a lack of monitoring focus that could assist in prioritizing limited resources during a site visit. Consequently, an increasing number of trial sponsors are implementing a hybrid monitoring strategy that combines on-site monitoring with centralised monitoring. One of the primary objectives of centralised monitoring, as stated in the clinical trial guidelines, is to guide and adjust the extent and frequency of on-site monitoring. Quality tolerance limits (QTLs) introduced in ICH E6(R2) and thresholds proposed by TransCelerate Biopharma are two existing approaches for achieving this objective at the trial- and site-levels, respectively. The funnel plot, as another threshold-based site-level method, overcomes the limitation of TransCelerate's method by adjusting thresholds flexibly based on site sizes. Nonetheless, both methods do not transparently explain the reason for choosing the thresholds that they used or whether their choices are optimal in any certain sense. Additionally, related Bayesian monitoring methods are also lacking. METHODS: We propose a simple, transparent, and user-friendly Bayesian-based risk boundary for determining the extent and frequency of on-site monitoring both at the trial- and site-levels. We developed a four-step approach, including: 1) establishing risk levels for key risk indicators (KRIs) along with their corresponding monitoring actions and estimates; 2) calculating the optimal risk boundaries; 3) comparing the outcomes of KRIs against the optimal risk boundaries; and 4) providing recommendations based on the comparison results. Our method can be used to identify the optimal risk boundaries within an established risk level range and is applicable to continuous, discrete, and time-to-event endpoints. RESULTS: We evaluate the performance of the proposed risk boundaries via simulations that mimic various realistic clinical trial scenarios. The performance of the proposed risk boundaries is compared against the funnel plot using real clinical trial data. The results demonstrate the applicability and flexibility of the proposed method for clinical trial monitoring. Moreover, we identify key factors that affect the optimality and performance of the proposed risk boundaries, respectively. CONCLUSION: Given the aforementioned advantages of the proposed risk boundaries, we expect that they will benefit the clinical trial community at large, in particular in the realm of risk-based monitoring.

An Adaptive Pragmatic Randomized Controlled Trial of Emergency Department Acupuncture for Acute Musculoskeletal Pain Management.

STUDY OBJECTIVE: Acute musculoskeletal pain in emergency department (ED) patients is frequently severe and challenging to treat with medications alone. The purpose of this study was to determine the feasibility, acceptability, and effectiveness of adding ED acupuncture to treat acute episodes of musculoskeletal pain in the neck, back, and extremities. METHODS: In this pragmatic 2-stage adaptive open-label randomized clinical trial, Stage 1 identified whether auricular acupuncture (AA; based on the battlefield acupuncture protocol) or peripheral acupuncture (PA; needles in head, neck, and extremities only), when added to usual care was more feasible, acceptable, and efficacious in the ED. Stage 2 assessed effectiveness of the selected acupuncture intervention(s) on pain reduction compared to usual care only (UC). Licensed acupuncturists delivered AA and PA. They saw and evaluated but did not deliver acupuncture to the UC group as an attention control. All participants received UC from blinded ED providers. Primary outcome was 1-hour change in 11-point pain numeric rating scale. RESULTS: Stage 1 interim analysis found both acupuncture styles similar, so Stage 2 continued all 3 treatment arms. Among 236 participants randomized, demographics and baseline pain were comparable across groups. When compared to UC alone, reduction in pain was 1.6 (95% confidence interval [CI]: 0.7 to 2.6) points greater for AA+UC and 1.2 (95% CI: 0.3 to 2.1) points greater for PA+UC patients. Participants in both treatment arms reported high satisfaction with acupuncture. CONCLUSION: ED acupuncture is feasible and acceptable and can reduce acute musculoskeletal pain better than UC alone.

On the use of RWD in support of regulatory submission in drug development.

For the approval of a drug product, the United States Food and Drug Administration requires substantial evidence (SE) regarding effectiveness and safety of the test drug to be provided. In recent years, the use of real-world data in support of regulatory submission of pharmaceutical development has received much attention, and real-world evidence (RWE) is treated as complementary to SE by evaluating the real-world performance of the test treatment. In this article, we start by summarizing current regulatory perspectives on drug evaluation and some potential challenges in using RWE. To test for superiority in co-primary endpoints, a two-stage hybrid RCT/RWS adaptive design that combines randomized control trial for providing SE and real-world study for generating RWE is proposed. We use superiority in effectiveness and non-inferiority in safety as an example to illustrate how to implement this design. Numerical studies have shown that the proposed design has merits in reducing the required sample size compared with traditional co-primary endpoint tests while maintaining statistical power and controlling type I error inflation. The proposed design can be implemented in drug development considering co-primary endpoints, especially for oncology and rare disease drug development.

Analysis of innovative two-stage seamless adaptive design with different endpoints and population shift.

In recent years, clinical trials utilizing a two-stage seamless adaptive trial design have become very popular in drug development. A typical example is a phase 2/3 adaptive trial design, which consists of two stages. As an example, stage 1 is for a phase 2 dose-finding study and stage 2 is for a phase 3 efficacy confirmation study. Depending upon whether or not the target patient population, study objectives, and study endpoints are the same at different stages, Chow (2020) classified two-stage seamless adaptive design into eight categories. In practice, standard statistical methods for group sequential design with one planned interim analysis are often wrongly directly applied for data analysis. In this article, following similar ideas proposed by Chow and Lin (2015) and Chow (2020), a statistical method for the analysis of a two-stage seamless adaptive trial design with different study endpoints and shifted target patient population is discussed under the fundamental assumption that study endpoints have a known relationship. The proposed analysis method should be useful in both clinical trials with protocol amendments and clinical trials with the existence of disease progression utilizing a two-stage seamless adaptive trial design.

The use of real-world data for clinical investigation of effectiveness in drug development.

With the growing interest in leveraging real-world data (RWD) to support effectiveness evaluations for new indications, new target populations, and post-market performance, the United States Food and Drug Administration has published several guidance documents on RWD sources and real-world studies (RWS) to assist sponsors in generating credible real-world evidence (RWE). Meanwhile, the randomized controlled trial (RCT) remains the gold standard in drug evaluation. Along this line, we propose a hybrid two-stage adaptive design to evaluate effectiveness based on evidence from both RCT and RWS. At the first stage, a typical non-inferiority test is conducted using RCT data to test for not-ineffectiveness. Once not-ineffectiveness is established, the study proceeds to the second stage to conduct an RWS and test for effectiveness using integrated information from RCT and RWD. The composite likelihood approach is implemented as a down-weighing strategy to account for the impact of high variability in RWS population. An optimal sample size determination procedure for RCT and RWS is introduced, aiming to achieve the minimal expected sample size. Through extensive numerical study, the proposed design demonstrates the ability to control type I error inflation in most cases and consistently maintain statistical power above the desired level. In general, this RCT/RWS hybrid two-stage adaptive design is beneficial for effectiveness evaluations in drug development, especially for oncology and rare diseases.

Utility of real-world evidence in biosimilar development.

Biosimilar development refers to the process of creating a biologic drug that is similar to an existing approved biologic drug, also known as a reference drug. Due to the complex nature of biologics drugs and the inherent variability in their manufacturing process biosimilars are not identical but highly similar to the reference drug in terms of quality, safety, and efficacy. Efficacy and safety trials for biosimilars involve large numbers of patients to confirm comparable clinical performance of the biosimilar and the reference product in appropriately sensitive clinical indications and for appropriate sensitive endpoints. The objective of a biosimilar clinical data is to address slight differences observed at previous steps and to confirm comparable clinical performance of the biosimilar and the reference product. In recent years with advances in big data computing, there has been increasing interest to incorporate the totality of information from different data sources (e.g. Real World data and published literature) in design and conduct of clinical trial to support regulatory objectives. The biosimilar development is an ideal framework for utilization of Real-World Evidence in design of trials as potentially large amount of data are available for the reference dug. Hence there may be an opportunity to use RWD in establishing, improving or validating equivalence margins (EQM) for biosimilar designs, specifically in the case there is no historical published data in the intended sensitive population. In this article, we propose a variation of matching method that seems promising to identify the matched set from a real-world data for which the effect size of targeted endpoint would be comparable to historical data. We believe this is a reasonable approach because in design stage, we can view covariates and secondary endpoints as data feature that can be used in a matching method. This approach was illustrated through a case study which indicated the estimate of the primary endpoint is within 1% of published results and thus RWD may be used to justify or estimate the equivalence margin. To ensure consistent results we recommend using this approach in different indications and endpoint scenarios. Thus utilization of RWD/RWE can provide an important opportunity to increase access to biologic therapies, reducing cost by repurposing existing data.

Ratios of head circumference to ventricular size vary over time and predict eventual need for CSF diversion in intraventricular hemorrhage of prematurity.

PURPOSE: Intraventricular hemorrhage (IVH) of prematurity can lead to hydrocephalus, sometimes necessitating permanent cerebrospinal fluid (CSF) diversion. We sought to characterize the relationship between head circumference (HC) and ventricular size in IVH over time to evaluate the clinical utility of serial HC measurements as a metric in determining the need for CSF diversion. METHODS: We included preterm infants with IVH born between January 2000 and May 2020. Three measures of ventricular size were obtained: ventricular index (VI), Evan's ratio (ER), and frontal occipital head ratio (FOHR). The Pearson correlations (r) between the initial (at birth) paired measurements of HC and ventricular size were reported. Multivariable longitudinal regression models were fit to examine the HC:ventricle size ratio, adjusting for the age of the infant, IVH grade (I/II vs. III/IV), need for CSF diversion, and sex. RESULTS: A total of 639 patients with an average gestational age of 27.5 weeks were included. IVH grade I/II and grade III/IV patients had a positive correlation between initial HC and VI (r = 0.47, p < 0.001 and r = 0.48, p < 0.001, respectively). In our longitudinal models, patients with a low-grade IVH (I/II) had an HC:VI ratio 0.52 higher than those with a high-grade IVH (p-value < 0.001). Patients with low-grade IVH had an HC:ER ratio 12.94 higher than those with high-grade IVH (p-value < 0.001). Patients with low-grade IVH had a HC:FOHR ratio 12.91 higher than those with high-grade IVH (p-value < 0.001). Infants who did not require CSF diversion had an HC:VI ratio 0.47 higher than those who eventually did (p < 0.001). Infants without CSF diversion had an HC:ER ratio 16.53 higher than those who received CSF diversion (p < 0.001). Infants without CSF diversion had an HC:FOHR ratio 15.45 higher than those who received CSF diversion (95% CI (11.34, 19.56), p < 0.001). CONCLUSIONS: There is a significant difference in the ratio of HC:VI, HC:ER, and HC:FOHR size between patients with high-grade IVH and low-grade IVH. Likewise, there is a significant difference in HC:VI, HC:ER, and HC:FOHR between those who did and did not have CSF diversion. The routine assessments of both head circumference and ventricle size by ultrasound are important clinical tools in infants with IVH of prematurity.

Sample size re-estimation in clinical trials.

In clinical trials, sample size re-estimation is often conducted at interim. The purpose is to determine whether the study will achieve study objectives if the observed treatment effect at interim preserves till end of the study. A traditional approach is to conduct a conditional power analysis for sample size only based on observed treatment effect. This approach, however, does not take into consideration the variabilities of (i) the observed (estimate) treatment effect and (ii) the observed (estimate) variability associated with the treatment effect. Thus, the resultant re-estimated sample sizes may not be robust and hence may not be reliable. In this article, a couple of methods are proposed, namely, adjusted effect size (AES) approach and iterated expectation/variance (IEV) approach, which can account for the variability associated with the observed responses at interim. The proposed methods provide interval estimates of sample size required for the intended trial, which is useful for making critical go/no go decision. Statistical properties of the proposed methods are evaluated in terms of controlling of type I error rate and statistical power. The results show that traditional approach performs poorly in controlling type I error inflation, whereas IEV approach has the best performance in most cases. Additionally, all re-estimation approaches can keep the statistical power over 80 % ; especially, IEV approach's statistical power, using adjusted significance level, is over 95 % . However, IEV approach may lead to a greater increment in sample size when detecting a smaller effect size. In general, IEV approach is effective when effect size is large; otherwise, AES approach is more suitable for controlling type I error rate and keep power over 80 % with a more reasonable re-estimated sample size.

Innovative design and analysis for rare disease drug development.

One of the most challenges for rare diseases drug development is probably the availability of subjects with the diseases under a small patient population. It is then a great concern how to conduct clinical trials with the limited number of subjects available for obtaining substantial evidence regarding effectiveness and safety for approval of the drug product under investigation. For rare diseases drug development, FDA indicated that the Agency does not have the intention to create a statutory standard for approval of orphan drugs that is different from the standard for approval of drugs in common conditions. In this case, innovative thinking and approach for obtaining substantial evidence for approval of rare diseases drug products are necessarily applied. In this article, basic considerations for rare disease drug development are discussed. The innovative thinking of demonstrating not-ineffectiveness rather than effectiveness with a limited number of subjects available is outlined. In addition, an innovative approach utilizing a two-stage adaptive seamless trial design in conjunction with the concept of real-world data and real-world evidence is proposed not only to obtain substantial evidence for approval of rare diseases drug products, but also to meet the same standard as those drug products in common conditions. Under the two-stage adaptive seamless trial design, sample size calculation for rare diseases clinical trials based on the innovative probability monitoring procedure is also discussed.

Unified approaches to assessing treatment effect of traditional Chinese medicine based on health profiles.

Two dissimilarity indices are introduced to measure the disharmony of a human body system by mimicking the population bioequivalence and the individual bioequivalence concepts. Hypotheses for the treatment effect of a traditional Chinese medicine are formulated based on the two indices and then tested under the proposed designs by reverting an approximate confidence upper bound. The proposed methods can also be used when a drug product has multiple components or a trial has multiple endpoints.

Some thoughts on the QR method for analytical similarity evaluation.

As indicated in a recent published draft guidance on comparative analytical assessment, the United States (US) Food and Drug Administration (FDA) seems to suggest the use of quality range (QR) method for analytical similarity evaluation. It is a concern that the use of QR method for analytical similarity evaluation could potentially approve biological products which are not deemed biosimilar to the reference biological products. In this article, the limitations and potential risk for the use of the QR method for analytical similarity evaluation are discussed. Alternatively, two modified versions of the QR method, which are referred to as effect size (ES) mQR and plausibility interval (PI) mQR methods are suggested. The performance and statistical properties of the mQR methods are evaluated via extensive clinical trial simulation under various scenarios. The results indicate that the modified versions of the QR method not only overcome the limitations of the QR method for analytical similarity evaluation, but also can potentially help in detecting reference product changes during manufacturing process.

Criteria for dose-finding in two-stage seamless adaptive design.

In pharmaceutical/clinical development, two-stage seamless adaptive designs are commonly considered. Such designs include a two-stage phase I/II or phase II/III adaptive trial that combines one phase IIb study for dose-finding or treatment selection and one phase III study for efficacy confirmation into a single study. At the end of stage 1, promising dose(s) will be selected based on pre-specified selection criteria. In practice, since there is little power with limited subjects available at interim, commonly considered selection criteria for critical decision-making include (i) conditional power, (ii) precision analysis, (iii) predictive probability of success, and (iv) probability of being the best dose or treatment. The selected promising dose(s) will then proceed to the next stage for efficacy confirmation. In this article, we introduce, compare, and evaluate these criteria. Simulation studies and a numeric example are given to illustrate those criteria. Besides, we attempt to address some concerns for the two-stage seamless adaptive clinical trial.

Innovative Thinking on Endpoint Selection in Clinical Trials.

In clinical trials, selection of appropriate study endpoints is critical for an accurate and reliable evaluation of safety and effectiveness of a test treatment under investigation. In practice, however, there are usually multiple endpoints available for measurement of disease status and/or therapeutic effect of the test treatment under study. For example, in cancer clinical trials, overall survival, response rate, and/or time to disease progression are usually considered as primary clinical endpoints for evaluation of safety and effectiveness of the test treatment under investigation. Once the study endpoints have been selected, sample size required for achieving a desired power is then determined. It, however, should be noted that different study endpoints may result in different sample sizes. In practice, it is usually not clear which study endpoint can best inform the disease status and measure the treatment effect. Moreover, different study endpoints may not translate one another although they may be highly correlated one another. In this article, we intend to develop an innovative endpoint namely therapeutic index based on a utility function to combine and utilize information collected from all study endpoints. Statistical properties and performances of the proposed therapeutic index are evaluated theoretically. A numerical example concerning a cancer clinical trial is given to illustrate the use of the proposed therapeutic index.

Probability monitoring procedures for sample size determination.

In clinical research, power analysis is often performed for sample size calculation. The purpose is to achieve a desired power of correctly detecting a clinically meaningful difference at a pre-specified level of significance if such a difference truly exists. However, in some situations such as (i) clinical trials with extremely low incidence rates and (ii) for rare disease drug development clinical trials, power analysis for sample size calculation may not be feasible because (i) it may require a huge sample size for detecting a relatively small difference and (ii) eligible patients may not be available for a small target patient population. In these cases, other procedures for sample size determination with certain statistical assurance are needed. In this article, an innovative method based on a probability monitoring procedure is proposed for sample size determination. The concept is to select an appropriate sample size for controlling the probability of crossing safety and/or efficacy boundaries. For rare disease clinical development, an adaptive probability monitoring procedure may be applied if a multiple-stage adaptive trial design is used.

Demonstrating effectiveness or demonstrating not ineffectiveness - A potential solution for rare disease drug product development?

For review and approval of new drug products, substantial evidence regarding safety and effectiveness of the drug products under investigation are necessarily provided. A traditional approach is to test a null hypothesis of ineffectiveness against an alternative hypothesis of effectiveness at the 5% level of significance. The rejection of the null hypothesis of ineffectiveness is in favor of the alternative hypothesis of effectiveness. This approach, however, is somewhat misleading because the rejection of the null hypothesis of ineffectiveness leads to the conclusion of not ineffectiveness, which consists of the proportion of inconclusiveness and the proportion of effectiveness. In this article, we explore the potential use of the concept of demonstrating not ineffectiveness and then effectiveness for regulatory approval of new drug products, especially for rare disease drug products. For rare disease drug product development, one of the major obstacles and challenges is how to use small patient population available for achieving the same standards for regulatory approval. To address this problem, a two-stage approach by first demonstrating not ineffectiveness and then ruling out (or controlling) the probability of inconclusiveness for demonstrating effectiveness is proposed. The proposed two-stage approach is useful with small patient population available for achieving the same standards for regulatory approval of rare disease drug products.

The use of 95% CI or 90% CI for drug product development - a controversial issue?

For review and approval of drug products, a 95% confidence interval approach for evaluation of new drugs is commonly used, while a 90% confidence interval approach is considered for assessment of generic drugs and biosimilar products. In the past decade, FDA has been challenged for adopting different standards (i.e., 5% type-I error rate for new drugs and 10% type-I error rate for generics/biosimilars) for regulatory submissions of drugs and biologics. This note intends to clarify the confusion by pointing out the fundamental differences between (i) the concepts of point hypotheses and interval hypotheses, and (ii) the concepts of interval hypotheses testing and confidence interval approach. In general, the method of interval hypotheses testing is not equivalent to the confidence interval approach although they may be operationally equivalent under certain conditions.

Statistical considerations for rare diseases drug development.

One of the most challenges for rare disease clinical trials is probably the availability of a small patient population. It is then a great concern on how to conduct clinical trials with a small number of subjects available for obtaining substantial evidence regarding safety and effectiveness for approval of the rare disease drug product under investigation. FDA, however, does not have the intention to create a statutory standard for approval of orphan drugs that are different from the standard for approval of drugs in common conditions. Thus, it is suggested that innovative trial designs such as a complete n-of-1 trial design or an adaptive design should be used for an accurate and reliable assessment of rare disease drug products under investigation. In this article, basic considerations, innovative trial designs, and statistical methods for data analysis are discussed. In addition, some innovative thinking for the evaluation of rare disease drug products is proposed.

Simultaneous confidence interval methods for analytical similarity assessment.

In analytical similarity assessment of a biosimilar product, key quality attributes of the test and reference products need to be shown statistically similar. When there were multiple references, similarity among the reference products is also required. We proposed a simultaneous confidence approach based on the fiducial inference theory as an alternative to the pairwise comparison method. Three versions with two types of simultaneous confidence intervals for each version were proposed based on different assumptions of the population variance. We conducted extensive simulation studies to compare the performance of our proposed method and the pairwise method, and provided examples to illustrate the concern of using pairwise method.