全球智库信息集成服务系统(G2TT): DP13836 P-hacking in clinical trials and how incentives shape the distribution of results across phases

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP13836
	DP13836 P-hacking in clinical trials and how incentives shape the distribution of results across phases
	Jerome Adda; Christian Decker; Marco Ottaviani
发表日期	2019-07-01
出版年	2019
语种	英语
摘要	Clinical research should conform to high standards of ethical and scientific integrity, given that human lives are at stake. However, economic incentives can generate conflicts of interest for investigators, who may be inclined to withhold unfavorable results or even tamper with data in order to achieve desired outcomes. To shed light on the integrity of clinical trial results, this paper systematically analyzes the distribution of p-values of primary outcomes for phase II and phase III drug trials reported to the ClinicalTrials.gov registry. First, we detect no bunching of results just above the classical 5% threshold for statistical significance. Second, a density discontinuity test reveals an upward jump at the 5% threshold for phase III results by small industry sponsors. Third, we document a larger fraction of significant results in phase III compared to phase II. Linking trials across phases, we find that early favorable results increase the likelihood of continuing into the next phase. Once we take into account this selective continuation, we can explain almost completely the excess of significant results in phase III for trials conducted by large industry sponsors. For small industry sponsors, instead, part of the excess remains unexplained.
主题	Industrial Organization
关键词	Clinical trials Drug development Selective reporting P-hacking Economic incentives in research
URL	https://cepr.org/publications/dp13836-1
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/542711
推荐引用方式 GB/T 7714	Jerome Adda,Christian Decker,Marco Ottaviani. DP13836 P-hacking in clinical trials and how incentives shape the distribution of results across phases. 2019.