scholarly journals A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials

2019 ◽  
Vol 38 (30) ◽  
pp. 5565-5586
Author(s):  
Jing Wu ◽  
Ming‐Hui Chen ◽  
Elizabeth D. Schifano ◽  
Joseph G. Ibrahim ◽  
Jeffrey D. Fisher
Keyword(s):  
Hiv Prevention ◽  
Count Data ◽  
Joint Model ◽  
Longitudinal Count Data ◽  
Prevention Trials ◽  
Bayesian Joint Model

scholarly journals Competing risks joint models using R-INLA

2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

scholarly journals Competing risks joint models using R-INLA

2020 ◽  
pp. 1471082X1991365
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

scholarly journals Venue-Based Recruitment of Women at Elevated Risk for HIV: An HIV Prevention Trials Network Study

2014 ◽  
Vol 23 (6) ◽  
pp. 541-551 ◽  
Author(s):  
Danielle F. Haley ◽  
Carol Golin ◽  
Wafaa El-Sadr ◽  
James P. Hughes ◽  
Jing Wang ◽  
...  
Keyword(s):  
Hiv Prevention ◽  
Elevated Risk ◽  
Prevention Trials

Implementing post-trial access plans for HIV prevention research

2018 ◽  
Vol 44 (5) ◽  
pp. 354-358 ◽  
Author(s):  
Amy Paul ◽  
Maria W Merritt ◽  
Jeremy Sugarman
Keyword(s):  
Hiv Prevention ◽  
Large Scale ◽  
Healthcare Systems ◽  
System Level ◽  
Planning Under Uncertainty ◽  
Work Related ◽  
Prevention Trials ◽  
Practical Experiences ◽  
Post Trial ◽  
Local Healthcare

Ethics guidance increasingly recognises that researchers and sponsors have obligations to consider provisions for post-trial access (PTA) to interventions that are found to be beneficial in research. Yet, there is little information regarding whether and how such plans can actually be implemented. Understanding practical experiences of developing and implementing these plans is critical to both optimising their implementation and informing conceptual work related to PTA. This viewpoint is informed by experiences with developing and implementing PTA plans for six large-scale multicentre HIV prevention trials supported by the HIV Prevention Trials Network. These experiences suggest that planning and implementing PTA often involve challenges of planning under uncertainty and confronting practical barriers to accessing healthcare systems. Even in relatively favourable circumstances where a tested intervention medication is approved and available in the local healthcare system, system-level barriers can threaten the viability of PTA plans. The aggregate experience across these HIV prevention trials suggests that simply referring participants to local healthcare systems for PTA will not necessarily result in continued access to beneficial interventions for trial participants. Serious commitments to PTA will require additional efforts to learn from future approaches, measuring the success of PTA plans with dedicated follow-up and further developing normative guidance to help research stakeholders navigate the complex practical challenges of realising PTA.

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