Search

Importance: Surrogate end points in oncology trade the advantage of reducing the time needed to conduct clinical trials for the disadvantage of greater uncertainty regarding the treatment effect on patient-centered end points, such as overall survival (OS) and quality of life., Objective: To quantify the amount of time saved through the acceptance of surrogate end points, including response rate (RR) and progression-free survival (PFS)., Design, Setting, and Participants: This retrospective study of US Food and Drug Administration (FDA) oncology approvals and their drug registration trials based on actual publication analyzed the original and updated clinical trials data that led to FDA-approved drug indications in oncology from 2006 to 2017 by using existing publications, conference abstracts, and package inserts from the FDA. Data related to cancer type, line of therapy (first-line, second-line, and third- or later-line treatment of advanced or metastatic disease), FDA approval type, end point basis for approval (RR, PFS, or OS/quality of life), sample size, accrual rate, and drug RR were extracted by March 23, 2018. All data were analyzed by July 13, 2018., Main Outcomes and Measures: The main outcome was the study duration needed to complete the primary end point analysis used for each drug indication approval. This was estimated from reported enrollment dates, analysis cutoff dates, time to response, median duration of response, median PFS, and median OS., Results: In total, 188 distinct indications among 107 cancer drugs were identified. The RR was more often used for FDA approval in subsequent lines of therapy (17 of 71 drug indications [24%] in first-line therapy vs 34 of 77 drug indications [44%] in second-line therapy vs 19 of 24 drug indications [79%] in third- or later-line therapy, P < .001). Study duration for PFS (median, 31 [range, 10-104] months) was similar to that for OS (median, 33 [range, 12-117] months; P = .31), whereas study duration for RR (median, 25 [range, 11-54] months) was shorter than that for OS (P = .001). In multivariate analysis, compared with using OS, use of PFS as the end point was associated with study durations that were shorter by a mean of 11 months (95% CI, 5-17 months), and the use of RR as the end point was associated with study durations that were shorter by a mean of 19 months (95% CI, 13-25 months)., Conclusions and Relevance: From the findings of this study, an estimated 11 months appeared to be needed (ie, approximately 12% longer in the drug development cycle) to assess the OS benefit of a cancer drug. This study's findings suggest that this must be weighed against the downside of increased uncertainty of clinical benefit arising from using surrogate end points.

Recently, new draft guidelines on multiplicity issues in clinical trials have been issued by European Medicine Agency (EMA) and Food and Drug Administration (FDA), respectively. Multiplicity is an issue in clinical trials, if the probability of a false-positive decision is increased by insufficiently accounting for testing multiple hypotheses. We outline the regulatory principles related to multiplicity issues in confirmatory clinical trials intended to support a marketing authorization application in the EU, describe the reasons for an increasing complexity regarding multiple hypotheses testing and discuss the specific multiplicity issues emerging within the regulatory context and being relevant for drug approval.

Purpose: To conduct a synthesis of the literature on methods to evaluate the impacts of FDA regulatory actions and identify best practices for future evaluations., Methods: We searched MEDLINE for manuscripts published between January 1948 and August 2011 that included terms related to FDA, regulatory actions, and empirical evaluation; the review additionally included FDA-identified literature. We used a modified Delphi method to identify preferred methodologies. We included studies with explicit methods to address threats to validity and identified designs and analytic methods with strong internal validity that have been applied to other policy evaluations., Results: We included 18 studies out of 243 abstracts and papers screened. Overall, analytic rigor in prior evaluations of FDA regulatory actions varied considerably; less than a quarter of studies (22%) included control groups. Only 56% assessed changes in the use of substitute products/services, and 11% examined patient health outcomes. Among studies meeting minimal criteria of rigor, 50% found no impact or weak/modest impacts of FDA actions and 33% detected unintended consequences. Among those studies finding significant intended effects of FDA actions, all cited the importance of intensive communication efforts. There are preferred methods with strong internal validity that have yet to be applied to evaluations of FDA regulatory actions., Conclusions: Rigorous evaluations of the impact of FDA regulatory actions have been limited and infrequent. Several methods with strong internal validity are available to improve trustworthiness of future evaluations of FDA policies., (Copyright © 2013 John Wiley & Sons, Ltd.)

Composite endpoints are often used in clinical trials in order to increase the overall event rates, reduce the sizes of the trials and achieve desired power. For example, in a trial to study the effect of a treatment on the prevention of venous thromboembolic events after a major orthopaedic surgery of the lower limbs, the primary endpoint is usually a composite endpoint consisting of any deep vein thrombosis identified by systematic venography of lower limbs, symptomatic and well-documented non-fatal pulmonary embolism, and death from all causes. Just as any endpoints, missing data can occur in the components of the composite endpoint. If a patient has missing data on some of the components but not all the components, this patient may not have complete data but partial data for the composite endpoint. To be consistent with the intention-to-treat principle, the patient should not be discarded from the analysis. In this research, we propose an approach for the analysis of a composite endpoint with missing data in components. The main idea is to first derive the probabilities of all possible study outcomes based on the appropriate model and then to construct the overall rate for the composite endpoint. Simulations are conducted to compare the approach with several naïve methods. A data example is used to illustrate the application of the approach.

Bauer and Kohne proposed an adaptive design using Fisher's combination of independent p-values based on subsamples from different stages (Biometrics 1994; 50(4):1029-1041). Their method provides great flexibility in the selection of statistical methods for hypothesis testing of subsamples. However, the choices for the stopping boundaries are not flexible enough to meet practical needs (Biometrics 2001; 57(3): 886-891). In this paper, an adaptive design method is proposed using linear combination of the independent p-values. The method provides great flexibility in the selection of stopping boundaries and no numerical integration is required for the two-stage designs. The stopping boundaries and p-values can be calculated manually. The operating characteristics of the adaptive designs are studied using computer simulations with and without sample size adjustment. Examples are presented for superiority and non-inferiority trials with different endpoints (normal, binary, and survival) under different adaptations. The statistical efficiency of the proposed method is compared with other methods based on conditional power., (Copyright (c) 2006 John Wiley & Sons, Ltd.)

Scleroderma patients usually have serious medical events in several organ systems and it is desirable to have a composite index that accounts for disease activity in these organ systems. We show how one may use a composite 'time to event' analysis for evaluating such patients and more generally for patients suffering from a chronic disease. The composite 'time to event' analysis requires a composite endpoint with a Kaplan-Meier type analysis. As an illustration, we use data from a clinical trial for scleroderma patients and present sensitivity analysis where one or more of the organ involvement definition criteria are modified. In addition, we propose desirability functions to monitor patients' disease improvement when the outcomes are all continuous. This method offers several possible advantages over existing methods for measuring patients' improvement.

In this paper we discuss statistical considerations regarding endpoints in preventive vaccine trials. Brief discussion is given to preclinical, Phase I, and Phase II trials, with the bulk of attention paid to endpoint choice and analysis in Phase III efficacy trials. In addition to traditional efficacy measures of vaccine effects for immunized individuals, consideration is given to waning, strain specific efficacy, correlates of protective immunity, postinfection endpoints, and cluster randomized trials.

This review covers a number of the many design and analytic issues associated with clinical trials that incorporate patient reported outcomes as primary or secondary endpoints. We use a clinical trial designed to evaluate a new therapy for the prevention of migraines to illustrate how endpoints are defined by the objectives of the study, the methods for handling longitudinal assessments with multiple scales or outcomes, and the methods of analysis in the presence of missing data.

Persistent genital infection with human papillomavirus (HPV) is a natural candidate as a surrogate marker for cervical cancer because of the strong epidemiologic and molecular evidence that HPV infection is the causative agent for almost all cervical cancers. However, while infection with high-risk types of HPV appears to be necessary for the development of cervical cancer, most infections are controlled by host immune response and do not lead to cancer in the vast majority of infected women. Because diagnostic tests cannot distinguish a persistent infection in the pathogenesis of cervical cancer from a transient infection, it is difficult to describe the disease mechanism as a progressive process based on observations. Therefore, the disease pathogenesis pathway does not fit into the usual surrogate marker framework, raising practical concerns about using HPV infection as a surrogate for a clinical endpoint in vaccine trials. In this paper, we describe the challenges in defining HPV infection as a surrogate endpoint in a HPV vaccine trial that is aimed at reducing cervical cancer rates and examine potential effects of the vaccine. We then outline some issues in the design and analysis of HPV vaccine trials, including the use of operationally defined HPV infection events meant to capture persistent infections. We conclude with a recommendation for a multistate model that uses HPV infection to help explain the mechanisms of vaccine action rather than validate it as an endpoint substitute.

Healthcare decision makers are increasingly requesting information on the cost and cost-effectiveness of new medicines at the time of product launch. In order to provide this information, data on healthcare resource utilization and, in some cases, costs, may be collected in clinical trials. In this paper, we discuss some of the issues statisticians need to address when it is appropriate to include these economic endpoints in the trial. Several design issues are discussed, including the alternative types of and methods for collecting economic endpoint data, sample size and generalizability. Alternative approaches in the analysis of resource utilization, cost and cost-effectiveness are also presented. Finally, several of the analytic approaches are applied to actual data from a clinical trial.