Comparison of statistical methods for the analysis of recurrent adverse events in the presence of non-proportional hazards and unobserved heterogeneity: a simulation study.
Eijkemans, Marinus J C
Laufer, Miriam K
JournalBMC Medical Research Methodology
MetadataShow full item record
AbstractBackground: In preventive drug trials such as intermittent preventive treatment for malaria prevention during pregnancy (IPTp), where there is repeated treatment administration, recurrence of adverse events (AEs) is expected. Challenges in modelling the risk of the AEs include accounting for time-to-AE and within-patient-correlation, beyond the conventional methods. The correlation comes from two sources; (a) individual patient unobserved heterogeneity (i.e. frailty) and (b) the dependence between AEs characterised by time-dependent treatment effects. Potential AE-dependence can be modelled via time-dependent treatment effects, event-specific baseline and event-specific random effect, while heterogeneity can be modelled via subject-specific random effect. Methods that can improve the estimation of both the unobserved heterogeneity and treatment effects can be useful in understanding the evolution of risk of AEs, especially in preventive trials where time-dependent treatment effect is expected. Methods: Using both a simulation study and the Chloroquine for Malaria in Pregnancy (NCT01443130) trial data to demonstrate the application of the models, we investigated whether the lognormal shared frailty models with restricted cubic splines and non-proportional hazards (LSF-NPH) assumption can improve estimates for both frailty variance and treatment effect compared to the conventional inverse Gaussian shared frailty model with proportional hazard (ISF-PH), in the presence of time-dependent treatment effects and unobserved patient heterogeneity. We assessed the bias, precision gain and coverage probability of 95% confidence interval of the frailty variance estimates for the models under varying known unobserved heterogeneity, sample sizes and time-dependent effects. Results: The ISF-PH model provided a better coverage probability of 95% confidence interval, less bias and less precise frailty variance estimates compared to the LSF-NPH models. The LSF-NPH models yielded unbiased hazard ratio estimates at the expense of imprecision and high mean square error compared to the ISF-PH model. Conclusion: The choice of the shared frailty model for the recurrent AEs analysis should be driven by the study objective. Using the LSF-NPH models is appropriate if unbiased hazard ratio estimation is of primary interest in the presence of time-dependent treatment effects. However, ISF-PH model is appropriate if unbiased frailty variance estimation is of primary interest. Trial registration: ClinicalTrials.gov; NCT01443130.
Rights/Terms© 2022. The Author(s).
Randomised controlled trials
Recurrent adverse events
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/17804
- Nonproportional hazards and unobserved heterogeneity in clustered survival data: When can we tell the difference?
- Authors: Balan TA, Putter H
- Issue date: 2019 Aug 15
- A tutorial on frailty models.
- Authors: Balan TA, Putter H
- Issue date: 2020 Nov
- Influence of trial duration on the bias of the estimated treatment effect in clinical trials when individual heterogeneity is ignored.
- Authors: Cécilia-Joseph E, Auvert B, Broët P, Moreau T
- Issue date: 2015 May
- Computational issues in fitting joint frailty models for recurrent events with an associated terminal event.
- Authors: Toenges G, Jahn-Eimermacher A
- Issue date: 2020 May
- Fitting a shared frailty illness-death model to left-truncated semi-competing risks data to examine the impact of education level on incident dementia.
- Authors: Lee C, Gilsanz P, Haneuse S
- Issue date: 2021 Jan 11