Dataset Information

Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data.

ABSTRACT:

Background

Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures.

Methods

This work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes.

Results

Using the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model.With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach.

Conclusion

Repeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures.

SUBMITTER: Sagara I

PROVIDER: S-EPMC4132199 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data.

Sagara Issaka I Giorgi Roch R Doumbo Ogobara K OK Piarroux Renaud R Gaudart Jean J

Malaria journal 20140729

<h4>Background</h4>Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of mala ...[more]

PMID: 25073652

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Project description:Background/objectiveArtificial bone models (ABMs) are used in orthopaedics for research of biomechanics, development of implants and educational purposes. Most of the commercially available ABMs approximate the morphology of Europeans, but they may not depict the Asian anatomy. Therefore, our aim was to develop the first Asian ABM of the pelvis and compare it with the existing pelvic ABM (Synbone®; Caucasian male).MethodsOne hundred clinical computed tomography (CTs) of adult pelvises (male n = 50, female n = 50) of Malay, Chinese and Indian descent were acquired. CTs were segmented and defined landmarks were placed. Three 3D statistical pelvic model and mean models (overall, male, female) were generated. Anatomical variations were analysed using principal component analysis. To measure gender-related differences and differences to the existing ABM, distances between the anterior superior iliac spines (ASIS), the anterior inferior iliac spines (AIIS), the promontory and the symphysis (conjugate vera, CV) as well as the ischial spines (diameter transversa, DT) were quantified.ResultsPrincipal component analysis displayed large variability regarding the pelvic shape and size. Female and male statistical models were similar in ASIS (225 ± 20; 227 ± 13 mm; P = 0.4153) and AIIS (185 ± 11; 187 ± 10 mm; P = 0.3982) and differed in CV (116 ± 10; 105 ± 10 mm; P < 0.0001) and DT (105 ± 7; 88 ± 8 mm; P < 0.0001). Comparing the unisex mean model with the pre-existing ABM, the ASIS (226; 275 mm; P < 0.0001), the AIIS (186; 209 mm; P < 0.0001) and the CV (111; 105 mm; P < 0.0001) differed significantly. Both models were similar regarding DT (97; 95 mm; P = 0.6927). The analysis revealed notable gender- and size-dependent anatomical variations within the Asian population. Chinese, Malay and Indian descents did not differ notably. The overall Asian model was smaller than the existing ABM.The translation potential of this articleOwing to the large differences between the Asian ABM and the pre-existing ABM, as well as differences between genders, the use of an Asian- and gender-specific ABM is important to consider in research, biomechanics and implant development for this population.

Dataset Information

Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data.

Background

Methods

Results

Conclusion

Publications

Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data.

Similar Datasets

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