Sandeep Menon

Does hormone therapy improve age-related skin changes in postmenopausal women?: A randomized, double-blind, double-dummy, placebo-controlled multicenter study assessing the effects of norethindrone acetate and ethinyl estradiol in the improvement of mild to moderate age-related skin changes in postmenopausal women

BACKGROUND In postmenopausal women, declining estrogen levels are associated with a variety of skin changes, many of which are reportedly improved by estrogen supplementation. OBJECTIVE A study was conducted to assess the effects of continuous combined norethindrone acetate (NA) and ethinyl estradiol (EE) in the control of mild to moderate age-related skin changes in postmenopausal women. METHODS Four hundred eighty-five subjects were enrolled in this 48-week randomized, double-blind study. Subjects were randomized to one of three study arms: placebo group (165 subjects), 1 mg NA/5 microg EE group (162 subjects), or a 1 mg NA/10 microg EE group (158 subjects). The primary efficacy parameters of the study were investigator global assessment of coarse and fine facial wrinkling at week 48 and subjective self-assessment of changes in wrinkling from baseline at week 48. Secondary parameters included investigator global assessment of skin laxity/sagging at week 48, investigator global assessment of skin texture/dryness at week 48, patient self-assessment of laxity/sagging, texture/dryness, and wrinkle depth determined by image analysis of skin replicas of the periorbital (crows feet) and jowl areas, and skin elasticity determined by timed deformation and recoil. RESULTS There were similar scores in investigator global assessment in wrinkling and sagging modules at baseline across all three treatment groups. There were slight decreases in all parameters for all treatment groups for the primary subject end points, but there were no statistically significant differences between the NA/EE groups and placebo. For subject self-assessment of overall severity of skin wrinkling, there were no significant changes at weeks 24 and 48 compared to baseline. These data were unaffected by smoking status or alcohol consumption. LIMITATIONS This study assessed the effects of 48 weeks of low-dose estrogen upon facial skin in women who were, on average, 5 years postmenopausal. The effects of higher estrogen doses, longer treatment duration, or effects upon perimenopausal women cannot be extrapolated from this study. CONCLUSION Low-dose hormone therapy for 48 weeks in postmenopausal women did not significantly alter mild to moderate age-related facial skin changes.

Views on Emerging Issues Pertaining to Data Monitoring Committees for Adaptive Trials

In this paper, the authors express their views on a range of topics related to data monitoring committees (DMCs) for adaptive trials that have emerged recently. The topics pertain to DMC roles and responsibilities, membership, training, and communication. DMCs have been monitoring trials using the group sequential design (GSD) for over 30 years. While decisions may be more complicated with novel adaptive designs, the fundamental roles and responsibilities of a DMC will remain the same, namely, to protect patient safety and ensure the scientific integrity of the trial. It will be the DMC’s responsibility to recommend changes to the trial within the scope of a prespecified adaptation plan or decision criteria and not to otherwise recommend changes to the study design except for serious safety-related concerns. Nevertheless, compared with traditional data monitoring, some additional considerations are necessary when convening DMCs for novel adaptive designs. They include the need to identify DMC members who are familiar with adaptive design and to consider possible sponsor involvement in unique situations. The need for additional expertise in DMC members has prompted some researchers to propose alternative DMC models or alternative governance model. These various options and authors’ views on them are expressed in this article.

Sample Size Re-estimation Designs In Confirmatory Clinical Trials—Current State, Statistical Considerations, and Practical Guidance

A sample size re-estimation (SSR) design is a flexible, adaptive design with the primary purpose of allowing sample size of a study to be reassessed in the mid-course of the study to ensure adequate power. In real world drug product, biologic, and device development, there may be large uncertainty in key factors that drive the sample size estimation for a confirmatory clinical trial. For example, early phase studies may have encouraging results but could be of shorter duration, or use a different endpoint than what is required for confirmatory phase clinical trials. The negative impact of high uncertainty at design stage for a confirmatory trial can be mitigated by an SSR design. Recent surveys have reported an encouraging upward trend in the use of SSR designs in clinical trials since the release of the draft guidance for adaptive design clinical trials for drugs and biologics by the U.S. Food and Drug Administration in 2010 (U.S. Food and Drug Administration (FDA) (February, 2010), Draft Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics). To support broad understanding and acceptance of SSR designs in confirmatory settings, especially unblinded SSR designs, we summarize statistical methods pertaining to SSR designs, including recent development in this field, and discuss design alternatives among blinded SSR, unblinded SSR, and conventional group sequential designs. To support appropriate implementation of SSR designs, we make recommendations on operational logistics for trial conduct based on accumulated experience in recent years, and provide points to consider for final data analysis and reporting for studies where the sample size has been increased following either a blinded or an unblinded SSR algorithm.

Statistical Monitoring of Clinical Trials With Multiple Co-Primary Endpoints Using Multivariate B-value

This article develops methods of statistical monitoring of clinical trials with multiple co-primary endpoints, where success is defined as meeting both endpoints simultaneously. In practice, a group sequential design (GSD) method is used to stop trials early for promising efficacy, and conditional power (CP) is used for futility stopping rules. In this article, we show that stopping boundaries for the GSD with multiple co-primary endpoints should be the same as those for studies with single endpoints. Lan and Wittes proposed the B-value tool to calculate the CP of single endpoint trials and we extend this tool to calculate the CP for studies with multiple co-primary endpoints. We consider the cases of two-arm studies with co-primary normal and provide an example of implementation with simulated trial. A fixed-weight sample size reestimation approach based on CP is introduced.

Protein expression of the chemokine receptor CXCR4 and its ligand CXCL12 in primary cutaneous melanoma—biomarkers of potential utility? ☆ ☆☆

Dysregulation of the CXCR4/CXCL12 axis, relevant in melanoma progression, activates cell cycle progression and migration via stimulation of the MAPK pathway. We sought to ascertain the cooperativity of the CXCR4/CXCL12 axis with established prognosticators and BRAF status in melanoma. Samples (n = 107) of primary cutaneous melanoma were assessed for protein expression of CXCR4 and CXCL12, and molecular analyses were performed to ascertain BRAF status. Univariate analyses of CXCR4 protein showed that the proportion of CXCR4 positives was greater in melanomas with absence of mitoses (P < .0001), absence of ulceration (P = .0008), and absence of regression (P = .02). Patients presenting at shallower stages (American Joint Committee on Cancer [AJCC] 1-2) exhibited a larger proportion of CXCR4 positives (76.9%, P < .0001 and 69.0%, P = .008), whereas those at deeper stages (AJCC 3-4) exhibited a larger proportion of negatives (75.0%, P = .004 and 66.7%, P = .22). In a multivariate analysis, lower odds of CXCR4 protein expression were associated with AJCC stage 3 (odds ratio [OR]=0.16, P = .01), AJCC stage 4 (OR=0.17, P = .04), and mitoses (OR=0.21, P = .01). Univariate analyses of CXCL12 protein showed that the proportion of CXCL12 negatives was significantly smaller in melanomas with depth of at least 1 mm, absence of ulceration, and absence of vascular invasion (P < .0001 for all). CXCR4 and CXCL12 appear to be biomarkers associated with established prognosticators of good and poor clinical outcome, respectively, in primary cutaneous melanoma. A BRAF mutation does not appear to be associated with CXCR4/CXCL12 axis upregulation in primary cutaneous melanoma.

A Practical Guide to Data Monitoring Committees in Adaptive Trials

Adaptive clinical trials require access to interim data to carry out trial modification as allowed by a prespecified adaptation plan. A data monitoring committee (DMC) is a group of experts that is charged with monitoring accruing trial data to ensure the safety of trial participants and that in adaptive trials may also play a role in implementing a preplanned adaptation. In this paper, we summarize current practices and viewpoints and provide guidance on evolving issues related to the use of DMCs in adaptive trials. We describe the common types of adaptive designs and point out some DMC-related issues that are unique to this class of designs. We include 3 examples of DMCs in late-stage adaptive trials that have been implemented in practice. We advocate training opportunities for researchers who may be interested in serving on a DMC for an adaptive trial since qualified DMC members are fundamental to the successful execution of DMC responsibilities.

On model selections for repeated measurement data in clinical studies.

Repeated measurement designs have been widely used in various randomized controlled trials for evaluating long-term intervention efficacies. For some clinical trials, the primary research question is how to compare two treatments at a fixed time, using a t-test. Although simple, robust, and convenient, this type of analysis fails to utilize a large amount of collected information. Alternatively, the mixed-effects model is commonly used for repeated measurement data. It models all available data jointly and allows explicit assessment of the overall treatment effects across the entire time spectrum. In this paper, we propose an analytic strategy for longitudinal clinical trial data where the mixed-effects model is coupled with a model selection scheme. The proposed test statistics not only make full use of all available data but also utilize the information from the optimal model deemed for the data. The performance of the proposed method under various setups, including different data missing mechanisms, is evaluated via extensive Monte Carlo simulations. Our numerical results demonstrate that the proposed analytic procedure is more powerful than the t-test when the primary interest is to test for the treatment effect at the last time point. Simulations also reveal that the proposed method outperforms the usual mixed-effects model for testing the overall treatment effects across time. In addition, the proposed framework is more robust and flexible in dealing with missing data compared with several competing methods. The utility of the proposed method is demonstrated by analyzing a clinical trial on the cognitive effect of testosterone in geriatric men with low baseline testosterone levels.

Comparison of Operating Characteristics of Commonly Used Sample Size Re-Estimation Procedures in a Two-Stage Design

In group sequential clinical trials, there are several sample size re-estimation methods proposed in the literature that allow for change of sample size at the interim analysis. Most of these methods are based on either the conditional error function or the interim effect size. Our simulation studies compared the operating characteristics of three commonly used sample size re-estimation methods, Chen et al. (2004), Cui et al. (1999), and Muller and Schafer (2001). Gao et al. (2008) extended the CDL method and provided an analytical expression of lower and upper threshold of conditional power where the type I error is preserved. Recently, Mehta and Pocock (2010) extensively discussed that the real benefit of the adaptive approach is to invest the sample size resources in stages and increasing the sample size only if the interim results are in the so called “promising zone” which they define in their article. We incorporated this concept in our simulations while comparing the three methods. To test the robustness of these methods, we explored the impact of incorrect variance assumption on the operating characteristics. We found that the operating characteristics of the three methods are very comparable. In addition, the concept of promising zone, as suggested by MP, gives the desired power and smaller average sample size, and thus increases the efficiency of the trial design.

Clinical dose–response for a broad set of biological products: A model-based meta-analysis:

Characterizing clinical dose–response is a critical step in drug development. Uncertainty in the dose–response model when planning a dose-ranging study can often undermine efficiency in both the design and analysis of the trial. Results of a previous meta-analysis on a portfolio of small molecule compounds from a large pharmaceutical company demonstrated a consistent dose–response relationship that was well described by the maximal effect model. Biologics are different from small molecules due to their large molecular sizes and their potential to induce immunogenicity. A model-based meta-analysis was conducted on the clinical efficacy of 71 distinct biologics evaluated in 91 placebo-controlled dose–response studies published between 1995 and 2014. The maximal effect model, arising from receptor occupancy theory, described the clinical dose–response data for the majority of the biologics (81.7%, n = 58). Five biologics (7%) with data showing non-monotonic trend assuming the maximal effect model were identified and discussed. A Bayesian model-based hierarchical approach using different joint specifications of prior densities for the maximal effect model parameters was used to meta-analyze the whole set of biologics excluding these five biologics (n = 66). Posterior predictive distributions of the maximal effect model parameters were reported and they could be used to aid the design of future dose-ranging studies. Compared to the meta-analysis of small molecules, the combination of fewer doses, narrower dosing ranges, and small sample sizes further limited the information available to estimate clinical dose–response among biologics.

Ethical Considerations in Adaptive Design Clinical Trials

Adaptive design clinical trial methodologies offer both opportunities and challenges for observing basic ethical principles in human subject research. Using both published and unpublished adaptive design clinical trials, we have selected and reviewed examples of clinical trials with different design adaptations to discuss the ethical obstacles presented and often successfully resolved by these approaches, including (1) confirmatory trials for treatments widely accepted on the basis of uncontrolled case series or open-label trials (clinical equipoise and “justice” in the sense of which trial groups will “receive the benefits of research and bear its burdens”) (infantile hemangioma/propranolol); (2) interim results analysis by unblinded data monitoring committees (“withholding information necessary to make a considered judgment” [“respect for persons”] versus compromising the trial’s scientific basis) (BIG 1-98); (3) adaptations involving sample size reassessment or dose adjustment via dropping or adding treatment arms, allowing fewer subjects to produce statistically significant results, fewer subjects treated with ineffective/toxic doses, and more subjects given doses showing tolerance and treatment activity (“beneficence” or “protecting from harm and making efforts to secure wellbeing”) (ECMO, Neuromyelitis Optica); (4) adaptive randomization inferential problems balanced against ethical benefits (trastuzumab vs taxane in advanced gastric cancer; ADVENT); (5) more efficient allocation of societal resources for research, in both public and commercial realms, versus uncertain regulatory acceptance (indicaterol; VALOR); and (6) platform, umbrella, and basket trials offering additional efficiencies (I-SPY II, BATTLE, Lung-MAP). The importance of careful design, meticulous planning, and rigorous ethical review of adaptive design trials on a case-by-case basis cannot be overemphasized.