Multiplicity Adjustment and Sample Size Calculation in Clinical Trials with Multiple Endpoints: An Industry Survey of Current Practices in Japan

Sakamaki, Kentaro; Morita, Yusuke; Iba, Katsuhiro; Kamiura, Toshifumi; Yoshida, Seitaro; Ogawa, Naoyuki; Suganami, Hideki; Tsuchiya, Satoru; Fukimbara, Satoru

doi:10.1007/s43441-020-00126-2

Cited by 1 publication

(5 citation statements)

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“…Multiple endpoints are crucial in clinical studies [9,11,12]; however, multiplicity problems can arise in clinical studies when assessing multiple endpoints, posing challenges in data analysis and interpretation [18][19][20]. The more endpoints evaluated, the higher the probability of observing statistically significant results by chance alone, leading to inflated type I error rates.…”

Section: Discussionmentioning

confidence: 99%

“…In order to accomplish this task, an appropriate and representative sample of volunteers (either patients or healthy subjects) is recruited [1][2][3][4]. In order to quantify the effect of each intervention, some measurable quantities are needed, the "endpoints", which refer to specific outcomes measured to assess the effectiveness of a treatment or intervention [11,12]. After the endpoints are measured in each subject of the study, an appropriate statistical analysis follows that aims to identify whether significant differences exist between the groups under comparison.…”

Section: Background Aspects: Endpoints and Statistical Assessmentmentioning

confidence: 99%

“…The selection of endpoints in a clinical study requires careful consideration of various factors, to ensure that the study is scientifically sound, ethically conducted, and capable of providing meaningful insights into the effects of the intervention being studied [11,12,15,16]. In this context, a single or many endpoints are measured in a clinical study in order to express the desired characteristics.…”

Section: Background Aspects: Endpoints and Statistical Assessmentmentioning

confidence: 99%

“…It is crucial to choose endpoints that are clinically meaningful, reliable, and sensitive to changes induced by the treatment [3,4]. While some clinical studies may focus on a single primary endpoint, many trials incorporate multiple endpoints to provide a comprehensive assessment of treatment impact, encompassing efficacy, safety, and patient-reported outcomes [9][10][11][12][13][14]; however, sometimes clinical endpoints are related to each other, particularly when measuring complex effects like the antihypertensive impact of medications. These interrelated endpoints can lead to certain drawbacks in clinical research such as the potential for redundancy, where multiple endpoints may measure similar aspects of the intervention impact, leading to unnecessary duplication of effort and resources.…”

Section: Introductionmentioning

confidence: 99%

“…These interrelated endpoints can lead to certain drawbacks in clinical research such as the potential for redundancy, where multiple endpoints may measure similar aspects of the intervention impact, leading to unnecessary duplication of effort and resources. Additionally, interrelated endpoints can increase the complexity of data analysis and interpretation, requiring sophisticated statistical methods to account for correlations between outcomes [11,12].…”

Section: Introductionmentioning

confidence: 99%

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A Vector Theory of Assessing Clinical Trials: An Application to Bioequivalence

Karalis

2024

JCDD

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A novel idea is introduced regarding the statistical comparisons of endpoints in clinical trials. Currently, the (dis)similarity of measured endpoints is not assessed. Instead, statistical analysis is directly applied, which can lead to multiplicity issues, reduced statistical power, and the recruitment of more subjects. The Vector-Based Comparison (VBC) approach originates from vector algebra and considers clinical endpoints as “vectors”. In the general case of N clinical endpoints, a Cartesian coordinate system is defined, and the most important primary endpoint (E1) is set. Following an explicitly defined procedure, the pairwise relationships of the remaining N-1 endpoints with E1 are estimated, and the N-1 endpoints are decomposed into axes perpendicular to E1. The angle between vectors provides insight into the level of dependency between variables. Vectors that are perpendicular to each other are considered independent, and only these are used in the statistical analysis. In this work, VBC is applied to bioequivalence studies of three anti-hypertensive drugs: amlodipine, irbesartan, and hydrochlorothiazide. The results suggest that VBC is a reproducible, easily applicable method allowing for the discrimination and utilization of the endpoint component expressing different attributes. All clinical characteristics are assessed with increased statistical power, without inflation of type I error.

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