Kaizer Alexander M, Koopmeiners Joseph S
Department of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, Aurora, Colorado.
Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota.
Stat Med. 2020 Apr 30;39(9):1328-1342. doi: 10.1002/sim.8478. Epub 2020 Jan 21.
Identifying noncompliance in a randomized trial is challenging, but could be improved by leveraging biomarker data to identify participants that did not comply with their assigned treatment. For randomized trials of very low nicotine content (VLNC) cigarettes, the biomarker of total nicotine equivalents (TNE) could be used to identify noncompliance. Compliant participants should have lower levels of TNEs than participants that did not comply and smoked normal nicotine content cigarettes, resulting in a mixture of compliant and noncompliant participants at each dose level. Thresholds of TNE could then be identified from the compliant groups at each dose level and used to determine which study participants were compliant. Furthermore, proposed biological relationships of TNE with nicotine dose could be incorporated into improve the efficiency of estimation, but may introduce bias if misspecified. To account for multiple modeling assumptions across dose levels, we explore model averaging via reversible jump markov chain monte carlo (MCMC) within each dose level to take advantage of improvements in efficiency when the proposed relationship is true and to downweight the biological model when it is misspecified. In simulation studies, we demonstrate that model averaging in the presence of a correct biological relationship results in a decrease in the mean square error (MSE) of up to 85%, but downweights the model in dose levels where the relationship is not appropriate. We apply our approach to data from a randomized trial of VLNC cigarettes to estimate TNE thresholds and probability of compliance curves as a function of TNEs for each nicotine dose used in the trial.
在随机试验中识别不依从性具有挑战性，但利用生物标志物数据来识别未遵守指定治疗的参与者可能会有所改善。对于极低尼古丁含量（VLNC）香烟的随机试验，总尼古丁当量（TNE）生物标志物可用于识别不依从性。依从的参与者的TNE水平应低于不依从且吸食正常尼古丁含量香烟的参与者，这导致在每个剂量水平上都存在依从和不依从参与者的混合情况。然后可以从每个剂量水平的依从组中确定TNE阈值，并用于确定哪些研究参与者是依从的。此外，TNE与尼古丁剂量之间的拟议生物学关系可纳入其中以提高估计效率，但如果指定错误可能会引入偏差。为了考虑跨剂量水平的多个建模假设，我们在每个剂量水平内通过可逆跳跃马尔可夫链蒙特卡罗（MCMC）探索模型平均，以利用当拟议关系正确时效率的提高，并在指定错误时降低生物模型的权重。在模拟研究中，我们证明在存在正确生物学关系的情况下进行模型平均可使均方误差（MSE）降低高达85%，但会在关系不合适的剂量水平上降低模型的权重。我们将我们的方法应用于VLNC香烟随机试验的数据，以估计TNE阈值以及作为试验中使用的每种尼古丁剂量的TNE函数的依从概率曲线。
