We introduce a computationally efficient and general approach for utilizing multiple, possibly interval-censored, data streams to study complex biomedical endpoints using multistate semi-Markov models. Our motivating application is the REGEN-2069 trial, which investigated the protective efficacy (PE) of the monoclonal antibody combination REGEN-COV against SARS-CoV-2 when administered prophylactically to individuals in households at high risk of secondary transmission. Using data on symptom onset, episodic RT-qPCR sampling, and serological testing, we estimate the PE of REGEN-COV for asymptomatic infection, its effect on seroconversion following infection, and the duration of viral shedding. We find that REGEN-COV reduced the risk of asymptomatic infection and the duration of viral shedding, and led to lower rates of seroconversion among asymptomatically infected participants. Our algorithm for fitting semi-Markov models to interval-censored data employs a Monte Carlo expectation maximization (MCEM) algorithm combined with importance sampling to efficiently address the intractability of the marginal likelihood when data are intermittently observed. Our algorithm provide substantial computational improvements over existing methods and allows us to fit semi-parametric models despite complex coarsening of the data.

翻译：我们提出了一种计算高效且通用的方法，利用多个可能为区间删失的数据流，通过多状态半马尔可夫模型研究复杂的生物医学终点。我们的驱动应用是REGEN-2069试验，该试验研究了单克隆抗体组合REGEN-COV在预防性给药于具有高二次传播风险的家庭成员时，对SARS-CoV-2的保护效力。利用症状出现时间、间歇性RT-qPCR采样和血清学检测数据，我们估计了REGEN-COV对无症状感染的保护效力、其对感染后血清转化的影响以及病毒脱落持续时间。我们发现，REGEN-COV降低了无症状感染的风险和病毒脱落的持续时间，并导致无症状感染参与者的血清转化率降低。我们用于将半马尔可夫模型拟合到区间删失数据的算法采用蒙特卡洛期望最大化算法结合重要性采样，以有效解决数据间歇性观测时边缘似然难以处理的问题。我们的算法相比现有方法提供了显著的计算改进，并允许我们在数据存在复杂粗化的情况下拟合半参数模型。