Sources of Safety Data and Statistical Strategies for Design and Analysis: Clinical Trials.

BACKGROUND: There has been an increased emphasis on the proactive and comprehensive evaluation of safety endpoints to ensure patient well-being throughout the medical product life cycle. In fact, depending on the severity of the underlying disease, it is important to plan for a comprehensive safety evaluation at the start of any development program. Statisticians should be intimately involved in this process and contribute their expertise to study design, safety data collection, analysis, reporting (including data visualization), and interpretation. METHODS: In this manuscript, we review the challenges associated with the analysis of safety endpoints and describe the safety data that are available to influence the design and analysis of premarket clinical trials. RESULTS: We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from clinical trials compared to other sources. CONCLUSIONS: Clinical trials are an important source of safety data that contribute to the totality of safety information available to generate evidence for regulators, sponsors, payers, physicians, and patients. This work is a result of the efforts of the American Statistical Association Biopharmaceutical Section Safety Working Group.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance.

BACKGROUND: Safety data are continuously evaluated throughout the life cycle of a medical product to accurately assess and characterize the risks associated with the product. The knowledge about a medical product's safety profile continually evolves as safety data accumulate. METHODS: This paper discusses data sources and analysis considerations for safety signal detection after a medical product is approved for marketing. This manuscript is the second in a series of papers from the American Statistical Association Biopharmaceutical Section Safety Working Group. RESULTS: We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from passive postmarketing surveillance systems compared to other sources. CONCLUSIONS: Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

Sensitivity analysis for missing dichotomous outcome data in multi-visit randomized clinical trial with randomization-based covariance adjustment.

Dichotomous endpoints in clinical trials have only two possible outcomes, either directly or via categorization of an ordinal or continuous observation. It is common to have missing data for one or more visits during a multi-visit study. This paper presents a closed form method for sensitivity analysis of a randomized multi-visit clinical trial that possibly has missing not at random (MNAR) dichotomous data. Counts of missing data are redistributed to the favorable and unfavorable outcomes mathematically to address possibly informative missing data. Adjusted proportion estimates and their closed form covariance matrix estimates are provided. Treatment comparisons over time are addressed with Mantel-Haenszel adjustment for a stratification factor and/or randomization-based adjustment for baseline covariables. The application of such sensitivity analyses is illustrated with an example. An appendix outlines an extension of the methodology to ordinal endpoints.

Lorcaserin for Smoking Cessation and Associated Weight Gain: A Randomized 12-Week Clinical Trial.

INTRODUCTION: Lorcaserin is a selective serotonin 2C receptor agonist approved by the Food and Drug Administration for chronic weight management. Preclinical data suggest that it may also be effective in smoking cessation through modulation of the dopaminergic reward system. METHODS: This was a 12-week, randomized, double-blind, placebo-controlled trial conducted in 30 centers in the United States. Six hundred three adult smokers with a Body Mass Index of 18.5-35 kg/m2, averaging at least 10 cigarettes/day with no period of abstinence >3 months for the past year were randomized to lorcaserin 10 mg once daily (QD), 10 mg twice daily (BID) or placebo; all received standardized smoking cessation counseling weekly. The target quit date was day 15. The primary endpoint was the exhaled carbon monoxide confirmed Continuous Abstinence Rate for weeks 9-12 (month 3). RESULTS: Continuous Abstinence Rates for month 3 were 5.6%, 8.7%, and 15.3% for the placebo, QD and BID groups, respectively (BID vs. placebo odds ratio 3.02, 95% confidence interval 1.47, 6.22, p = .0027. Change in weight at week 12 (randomized population) was -0.01, -0.35 and -0.98 kg, respectively (p = .0004, BID vs. placebo), and +0.73, +0.76, and -0.41 kg in participants achieving month 3 continuous abstinence. The most frequent adverse events were headache, nausea, constipation, and fatigue. CONCLUSIONS: Lorcaserin with counseling was associated with dose-related increases in smoking cessation and prevention of associated weight gain over a 3-month period. Further investigation of lorcaserin in smoking cessation is warranted. Trial Registration: ClinicalTrials.gov. Identifier: NCT02044874. IMPLICATIONS: This randomized, controlled trial demonstrated that lorcaserin used in conjunction with standard cessation counseling was associated with dose-related increases in smoking cessation and prevention of associated weight gain. To our knowledge, this is the first demonstration in humans of a potential role of 5-HT2C agonism in the modulation of central neurological circuits involved with reward.

Early weight loss while on lorcaserin, diet and exercise as a predictor of week 52 weight-loss outcomes.

OBJECTIVE: To identify an early treatment milestone that optimizes sensitivity and specificity for predicting ≥5% weight loss at Week (W) 52 in patients with and without type 2 diabetes on lorcaserin or placebo. METHODS: Post hoc area under the curve for receiver operating characteristic analyses of data from three phase 3 trials comparing lifestyle modification+placebo with lifestyle modification+lorcaserin. A total of 6897 patients (18-65 years; BMI, 30-45 or 27-29.9 kg/m(2) with ≥1 comorbidity) were randomized to placebo or lorcaserin 10 mg bid. Changes (baseline to W52) in cardiometabolic parameters were assessed. RESULTS: Response (≥5% weight loss from baseline) at W12 was a strong predictor of W52 response. Lorcaserin patients with a W12 response achieved mean W52 weight losses of 10.6 kg (without diabetes) and 9.3 kg (with diabetes). Proportions achieving ≥5% and ≥10% weight loss at W52 were 85.5% and 49.8% (without diabetes), and 70.5% and 35.9% (with diabetes). Lorcaserin patients who did not achieve a W12 response lost 3.2 kg (without diabetes) and 2.8 kg (with diabetes) at W52. Responders had greater improvements in cardiometabolic risk factors than the modified intent-to-treat (MITT) population, consistent with greater weight loss. CONCLUSIONS: ≥5% weight loss by W12 predicts robust response to lorcaserin at 1 year.

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.