PMID- 35057743
OWN - NLM
STAT- MEDLINE
DCOM- 20220321
LR  - 20220531
IS  - 1471-2288 (Electronic)
IS  - 1471-2288 (Linking)
VI  - 22
IP  - 1
DP  - 2022 Jan 20
TI  - Comparison of statistical methods for the analysis of recurrent adverse events in 
      the presence of non-proportional hazards and unobserved heterogeneity: a 
      simulation study.
PG  - 24
LID - 10.1186/s12874-021-01475-8 [doi]
LID - 24
AB  - BACKGROUND: 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.
CI  - (c) 2022. The Author(s).
FAU - Patson, Noel
AU  - Patson N
AD  - School of Public Health, University of the Witwatersrand, Johannesburg, South 
      Africa. noelpatson@gmail.com.
AD  - School of Global and Public Health, Kamuzu University of Health Sciences, 
      Blantyre, Malawi. noelpatson@gmail.com.
FAU - Mukaka, Mavuto
AU  - Mukaka M
AD  - Mahidol Oxford Tropical Medicine Research unit (MORU), Bangkok, Thailand.
AD  - Centre for Tropical Medicine, Nuffield Department of Medicine, University of 
      Oxford, Oxford, UK.
FAU - Kazembe, Lawrence
AU  - Kazembe L
AD  - Department of Biostatistics, University of Namibia, Windhoek, Namibia.
FAU - Eijkemans, Marinus J C
AU  - Eijkemans MJC
AD  - Julius Center for Health Sciences and Primary Care, University Medical Center 
      Utrecht, Utrecht University, Utrecht, The Netherlands.
FAU - Mathanga, Don
AU  - Mathanga D
AD  - School of Global and Public Health, Kamuzu University of Health Sciences, 
      Blantyre, Malawi.
FAU - Laufer, Miriam K
AU  - Laufer MK
AD  - Center for Vaccine Development and Global Health, University of Maryland School 
      of Medicine, Baltimore, MD, USA.
FAU - Chirwa, Tobias
AU  - Chirwa T
AD  - School of Public Health, University of the Witwatersrand, Johannesburg, South 
      Africa.
LA  - eng
SI  - ClinicalTrials.gov/NCT01443130
GR  - U01 AI087624/AI/NIAID NIH HHS/United States
GR  - K24 AI114996/AI/NIAID NIH HHS/United States
GR  - D43 TW010075/TW/FIC NIH HHS/United States
PT  - Clinical Trial
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20220120
PL  - England
TA  - BMC Med Res Methodol
JT  - BMC medical research methodology
JID - 100968545
SB  - IM
MH  - Computer Simulation
MH  - Humans
MH  - *Models, Statistical
MH  - Probability
MH  - Proportional Hazards Models
MH  - Sample Size
PMC - PMC8771190
OTO - NOTNLM
OT  - Non-proportional hazards
OT  - Randomised controlled trials
OT  - Recurrent adverse events
OT  - Unobserved heterogeneity
COIS- The authors declare that they have no competing interests.
EDAT- 2022/01/22 06:00
MHDA- 2022/03/22 06:00
PMCR- 2022/01/20
CRDT- 2022/01/21 05:41
PHST- 2021/05/01 00:00 [received]
PHST- 2021/11/19 00:00 [accepted]
PHST- 2022/01/21 05:41 [entrez]
PHST- 2022/01/22 06:00 [pubmed]
PHST- 2022/03/22 06:00 [medline]
PHST- 2022/01/20 00:00 [pmc-release]
AID - 10.1186/s12874-021-01475-8 [pii]
AID - 1475 [pii]
AID - 10.1186/s12874-021-01475-8 [doi]
PST - epublish
SO  - BMC Med Res Methodol. 2022 Jan 20;22(1):24. doi: 10.1186/s12874-021-01475-8.