Hypoglycemic events analysis via recurrent time-to-event (HEART) models

C-index regression for recurrent event data

Recurrent event data analysis plays an important role in many fields, e.g., medicine, social science, and economics. While the existing approaches under the proportional rates or mean model yield poor performance when the underlying model is misspecified, we propose a novel model-free approach by introducing a lower bound on the concordance index (C-Index). We develop an estimation method through deriving a continuous lower bound on the C-Index based on the log-sigmoid function and also provide a variable selection procedure in high dimensional settings. Under both low and high dimensional settings, simulation results show that the proposed methods outperform the gamma frailty recurrent event model when the proportional mean assumption is violated. Moreover, an application to the hospital readmission dataset shows results in line with previous studies and a higher C-Index value further assures model decency.

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial

Background

In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy.

Methods

We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether–lumefantrine (AL), amodiaquine–artesunate (ASAQ) and dihydroartemisinin–piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment.

Results

Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p < 0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: − 0.5045, 0.4469; p = 0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: − 0.0889, 0.3194; p = 0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p = 0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p = 0.004) for Poisson model.

Conclusion

We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes.

Trial registration

ClinicalTrials.gov: NCT00852423

Analysis of recurrent hypoglycemic events

Hypoglycemia is a major safety concern for diabetic patients. Hypoglycemic events can be modeled based on time to recurrent events or count data. In this article, we evaluated a gamma frailty model with variance estimated by the inverse of observed Fisher information matrix, a gamma frailty model with the sandwich variance estimator, and a piecewise negative binomial regression model. Simulations showed that the sandwich variance estimator performed better when the frailty model is mis-specified, and the piecewise negative binomial regression sometimes fails to converge. All three methods were applied to a dataset from a clinical trial evaluating insulin treatments.

Adverse events during a disorder-specific psychotherapy compared to a nonspecific psychotherapy in patients with chronic depression

OBJECTIVES

A recent trial comparing Cognitive Behavioral Analysis System of Psychotherapy (CBASP) and supportive psychotherapy in chronic depression found CBASP to be more effective in treating depressive symptoms. We aimed to evaluate adverse events that occurred during this trial.

MATERIALS AND METHOD

A randomized trial of chronically depressed outpatients was performed. The treatment included 32 sessions of CBASP or supportive psychotherapy. Therapists asked patients about adverse events and their intensity in each session using a standardized checklist. We analyzed the mean number of (severe) adverse events per patient up to Session 32 with gamma frailty recurrent event models.

RESULTS

Two hundred and sixty patients were included in the analyses (66% female, mean age 45 years). Patients in the supportive psychotherapy group reported less severe adverse events in general, and less severe adverse events related to personal life and to occupational life than patients in the CBASP group. Less adverse events related to suicidal thoughts were reported in the CBASP compared with the supportive psychotherapy group.

CONCLUSIONS

Differences in the adverse events profile may be explained by the treatment elements. Adverse events related to personal and occupational life for example might be considered a necessary and expected yet temporary adverse treatment outcome of an effective CBASP treatment.