Evaluating Methods for Dealing with Missing Outcomes in Discrete-Time Event History Analysis:A Simulation Study

Background: In discrete-time event history analysis, subjects are measured once each time period until they experience the event, prematurely drop out, or when the study concludes. This implies measuring event status of a subject in each time period determines whether (s)he should be measured in subsequent time periods. For that reason, intermittent missing event status causes a problem because, unlike other repeated measurement designs, it does not make sense to simply ignore the corresponding missing event status from the analysis (as long as the dropout is ignorable). Method: We used Monte Carlo simulation to evaluate and compare various alternatives, including event occurrence recall, event (non-)occurrence, case deletion, period deletion, and single and multiple imputation methods, to deal with missing event status. Moreover, we showed the methods’ performance in the analysis of an empirical example on relapse to drug use. Result: The strategies assuming event (non-)occurrence and the recall strategy had the worst performance because of a substantial parameter bias and a sharp decrease in coverage rate. Deletion methods suffered from either loss of power or undercoverage issues resulting from a biased standard error. Single imputation recovered the bias issue but showed an undercoverage estimate. Multiple imputations performed reasonably with a negligible standard error bias leading to a gradual decrease in power. Conclusion: On the basis of the simulation results and real example, we provide practical guidance to researches in terms of the best ways to deal with missing event history data

Evaluating Methods for Dealing with Missing Outcomes in Discrete-Time Event History Analysis: A Simulation Study

Background: In discrete-time event history analysis, subjects are measured once each time period until they experience the event, prematurely drop out, or when the study concludes. This implies measuring event status of a subject in each time period determines whether (s)he should be measured in subsequent time periods. For that reason, intermittent missing event status causes a problem because, unlike other repeated measurement designs, it does not make sense to simply ignore the corresponding missing event status from the analysis (as long as the dropout is ignorable). Method: We used Monte Carlo simulation to evaluate and compare various alternatives, including event occurrence recall, event (non-)occurrence, case deletion, period deletion, and single and multiple imputation methods, to deal with missing event status. Moreover, we showed the methods’ performance in the analysis of an empirical example on relapse to drug use. Result: The strategies assuming event (non-)occurrence and the recall strategy had the worst performance because of a substantial parameter bias and a sharp decrease in coverage rate. Deletion methods suffered from either loss of power or undercoverage issues resulting from a biased standard error. Single imputation recovered the bias issue but showed an undercoverage estimate. Multiple imputations performed reasonably with a negligible standard error bias leading to a gradual decrease in power. Conclusion: On the basis of the simulation results and real example, we provide practical guidance to researches in terms of the best ways to deal with missing event history data

Evaluating Methods for Dealing with Missing Outcomes in Discrete-Time Event History Analysis: A Simulation Study

Background: In discrete-time event history analysis, subjects are measured once each time period until they experience the event, prematurely drop out, or when the study concludes. This implies measuring event status of a subject in each time period determines whether (s)he should be measured in subsequent time periods. For that reason, intermittent missing event status causes a problem because, unlike other repeated measurement designs, it does not make sense to simply ignore the corresponding missing event status from the analysis (as long as the dropout is ignorable). Method: We used Monte Carlo simulation to evaluate and compare various alternatives, including event occurrence recall, event (non-)occurrence, case deletion, period deletion, and single and multiple imputation methods, to deal with missing event status. Moreover, we showed the methods’ performance in the analysis of an empirical example on relapse to drug use. Result: The strategies assuming event (non-)occurrence and the recall strategy had the worst performance because of a substantial parameter bias and a sharp decrease in coverage rate. Deletion methods suffered from either loss of power or undercoverage issues resulting from a biased standard error. Single imputation recovered the bias issue but showed an undercoverage estimate. Multiple imputations performed reasonably with a negligible standard error bias leading to a gradual decrease in power. Conclusion: On the basis of the simulation results and real example, we provide practical guidance to researches in terms of the best ways to deal with missing event history data

Assessment of exercise-induced changes in von Willebrand factor as a marker of severity of aortic stenosis

\u3cp\u3eBackground: Loss of high-molecular-weight multimers (HMWMs) of von Willebrand factor (vWF) occurs due to high shear stress in patients with aortic stenosis. As symptoms of aortic stenosis occur during exercise, measurement of vWF during exercise might identify patients with aortic stenosis of clinical importance. The aim of this pilot study is to evaluate whether vWF changes over time as a result of exercise in patients with asymptomatic moderate or severe aortic stenosis.\u3c/p\u3e\u3cp\u3eMethods: Ten subjects were analysed for changes in vWF by measuring HMWMs and closure time with adenosine diphosphate (CT-ADP). All subjects underwent a full stress test on a bicycle ergometer. At rest and at peak exercise, a transthoracic echocardiogram was performed. HMWMs and CT-ADP were assessed at baseline, during and after exercise.\u3c/p\u3e\u3cp\u3eResults: HMWMs and CT-ADP did not change significantly during exercise, p=0.45 and p=0.65, respectively. HMWMs and CT-ADP correlated well, Spearman's rho -0.621, p<0.001. HMWMs during peak exercise did not correlate with maximal velocity measured, p=0.21. CT-ADP during exercise correlated well with the maximal echocardiographic velocity over the aortic valve (AV), rho 0.82, p=0.04.\u3c/p\u3e\u3cp\u3eConclusions: In a cohort of 10 patients with moderate or severe aortic stenosis, we observed no significant change in vWF biomarkers during exercise. Peak CT-ADP during exercise showed a good correlation with peak AV velocity measured with echo. Although CT-ADP is an easy test to perform and could be an alternative for peak AV velocity measured during exercise, our results suggest that it can only detect large changes in shear stress.\u3c/p\u3

A highly virulent variant of HIV-1 circulating in the Netherlands

We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log10 increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV-CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences-is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence

LifeTime and improving European healthcare through cell-based interceptive medicine

AUTEURS : LifeTime Community Working GroupsInternational audienceHere we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade

Importance of Baseline Prognostic Factors With Increasing Time Since Initiation of Highly Active Antiretroviral Therapy: Collaborative Analysis of Cohorts of HIV-1-Infected Patients

Background: The extent to which the prognosis for AIDS and death of patients initiating highly active antiretroviral therapy (HAART) continues to be affected by their characteristics at the time of initiation (baseline) is unclear. Methods: We analyzed data on 20,379 treatment-naive HIV-1- infected adults who started HAART in 1 of 12 cohort studies in Europe and North America (61,798 person-years of follow-up, 1844 AIDS events, and 1005 deaths). Results: Although baseline CD4 cell count became less prognostic with time, individuals with a baseline CD4 count 350 cells/μL (hazard ratio for AIDS = 2.3, 95% confidence interval [CI]: 1.0 to 2.3; mortality hazard ratio = 2.5, 95% CI: 1.2 to 5.5, 4 to 6 years after starting HAART). Rates of AIDS were persistently higher in individuals who had experienced an AIDS event before starting HAART. Individuals with presumed transmission by means of injection drug use experienced substantially higher rates of AIDS and death than other individuals throughout follow-up (AIDS hazard ratio = 1.6, 95% CI: 0.8 to 3.0; mortality hazard ratio = 3.5, 95% CI: 2.2 to 5.5, 4 to 6 years after starting HAART). Conclusions: Compared with other patient groups, injection drug users and patients with advanced immunodeficiency at baseline experience substantially increased rates of AIDS and death up to 6 years after starting HAART