Using WinBUGS to Study Family Frailty in Child Mortality, with an Application to Child Survival in Ivory Coast

Koissi, Marie-Claire; Högnäs, Göran

doi:10.11564/20-1-384

Cited by 9 publications

(10 citation statements)

References 34 publications

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“…Frailty was approximated by a version of the activities of daily living (ADL) score 15 , represented by an integer value ranging from 0 to 5; 0 means no difficulties, and 5 means complete difficulty in the components eat, toilet, dress, transfer, bathe and walk. This variable was not recorded in the registry data, so a Markov chain Monte Carlo approach was adapted from Koissi and Högnäs 16 . This process inferred frailty weights, that is the probability of being at each ADL level for each patient, as well as model parameters that are the hazard ratios of other‐cause mortality for each level ( Appendix S1 , supporting information).…”

Section: Methodsmentioning

confidence: 99%

Bridging the Age Gap: a prognostic model that predicts survival and aids in primary treatment decisions for older women with oestrogen receptor-positive early breast cancer

Ward¹,

Holmes²,

Morgan

et al. 2020

British Journal of Surgery

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Background: A prognostic model was developed and validated using cancer registry data. This underpins an online decision support tool, informing primary treatment choice for women aged 70 years or older with hormone receptor-positive early breast cancer. Methods: Data from women diagnosed between 2002 and 2010 in the English Northern and Yorkshire and West Midlands regions were used to develop the model. Primary treatment options of surgery with adjuvant endocrine therapy or primary endocrine therapy were compared. Models predicting the hazard of breast cancer-specific mortality and hazard of other-cause mortality were combined to derive survival probabilities. The model was validated externally using data from the Eastern Cancer Registration and Information Centre. Results: The model was developed using data from 23 842 women, and validated externally on a data set from 14 526 patients. The overall model calibration was good. At 2 and 5 years, predicted mortality from breast cancer and other causes differed from the observed rate by less than 1 per cent. At 5 years, there were slight overpredictions in breast cancer mortality (2629 predicted versus 2556 observed deaths; P = 0⋅142) and mortality from all causes (6399 versus 6320 respectively; P = 0⋅583). The discrepancy varied between subgroups. Model discrimination was 0⋅75 or above for all mortality measures. Conclusion: A prognostic model for older women with oestrogen receptor-positive early breast cancer was developed and validated in the present study. This forms a basis for an online decision support tool (https://agegap.shef .ac.uk/).

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Section: Methodsmentioning

confidence: 99%

Bridging the Age Gap: a prognostic model that predicts survival and aids in primary treatment decisions for older women with oestrogen receptor-positive early breast cancer

Ward¹,

Holmes²,

Morgan

et al. 2020

British Journal of Surgery

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“…The ignorance of heterogeneity in models according to [14], may lead to biased parameter estimates. But more importantly, geographical heterogeneity can be an effect of unmeasured covariates which may include contextual factors.…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, studies on childhood anaemia in Malawi have not assessed the geographical heterogeneity in childhood anaemia causes [ 7 , 13 ]. The ignorance of heterogeneity in models according to [ 14 ], may lead to biased parameter estimates. But more importantly, geographical heterogeneity can be an effect of unmeasured covariates which may include contextual factors.…”

Section: Introductionmentioning

confidence: 99%

Bayesian random effects modelling with application to childhood anaemia in Malawi

Ngwira

Kazembe

2015

BMC Public Health

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Background: Epidemiological studies in Malawi on child anaemia have neglected the community spatial effect to childhood anaemia. Neglecting the community spatial effect in the model ignores the influence of unobserved or unmeasured contextual variables, and at the same time the resultant model may under estimate model parameter standard errors which can result in erroneous significance of covariates. We aimed at investigating risk factors of childhood anaemia in Malawi with focus on geographical spatial effect.

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“…According to Koissi & Högnäs, (2013) ignorance of geographical heterogeneity due to unobserved characteristics could lead to biased estimation of the parameters (14). Geographical heterogeneity could be the effect of the unmeasured factors, which means that the geographical differences of factors that caused anaemia can be partially explained by the variability in environmental (15).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Geo-Additive Modeling of Childhood Anemia in India

Chungkham

Marbaniang

Narzary

2021

Preprint

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Background The geographical differences that caused anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections. The studies failed to explain the non-linear effect of the continuous covariates on childhood anaemia. The present paper aimed to investigate the risk factors of childhood anaemia in India with focus on geographical spatial effect. Methods Geo-additive logistic regression models were fitted to the data to understand fixed as well as spatial effects of childhood anaemia. Logistic regression was fitted for the categorical variable with outcomes (anaemia (Hb < 11) and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized spline and spatial effects were smoothed by the two-dimensional spline. Results At 95% posterior credible interval, the influence of unobserved factors on childhood anaemia is very strong in the Northern and Central part of India. However, most of the states in North Eastern part of India showed negative spatial effects. A U-shape non-linear relationship was observed between childhood anaemia and mother’s age. This indicates that mothers of young and old ages are more likely to have children who are anaemic; in particular mothers aged 15 years to about 25 years. Then the risk of childhood anaemia starts declining after the age of 25 years and it continues till the age of around 37 years, thereafter again starts increasing. Further, the non-linear effects of duration of breastfeeding on childhood anaemia show that the risk of childhood anaemia decreases till 29 months thereafter increases. Conclusion Strong evidence of residual spatial effect to childhood anaemia in India. Government child health programme should gear up in treating childhood anaemia by focusing on known measurable factors such as mother’s education, mother’s anaemia status, family wealth status, child fever, stunting, underweight, and wasting which have been found to be significant in this study, attention should also be given to effects of unknown or unmeasured factors to childhood anaemia at the community level. Special attention to these unmeasurable factors should be focused in the states of central and northern India which have shown significant positive spatial effects.

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Using WinBUGS to Study Family Frailty in Child Mortality, with an Application to Child Survival in Ivory Coast

Abstract: Abstract

Cited by 9 publications

References 34 publications

Bridging the Age Gap: a prognostic model that predicts survival and aids in primary treatment decisions for older women with oestrogen receptor-positive early breast cancer

Bridging the Age Gap: a prognostic model that predicts survival and aids in primary treatment decisions for older women with oestrogen receptor-positive early breast cancer

Bayesian random effects modelling with application to childhood anaemia in Malawi

A Bayesian Geo-Additive Modeling of Childhood Anemia in India

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