<tt>runmlwin</tt>: A Program to Run the<b>MLwiN</b>Multilevel Modeling Software from within<i>Stata</i>

Leckie, George; Charlton, Chris

doi:10.18637/jss.v052.i11

Cited by 294 publications

(317 citation statements)

References 11 publications

(6 reference statements)

Supporting

Mentioning

306

Contrasting

Unclassified

Order By: Relevance

“…The 95% CrI can thus be interpreted as the interval within which there is a 95% chance the true population values are included. All analyses were conducted using MLWin Version 2.1 (Browne, 2009;Rasbash et al, 2009) and Stata 14 (College Station, TX) with the RunMlWin add-on package (Leckie et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Kirby

Nagel

Rosa

et al. 2017

International Journal of Hygiene and Environmental Health

View full text Add to dashboard Cite

Kirby, M.A.; Nagel, C.L.; Rosa, G.; Umupfasoni, M.M.; Iyakaremye, L.; Clasen, T.F. (2017) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Graphical abstract ABSTRACTUnsafe drinking water is a substantial health risk contributing to child diarrhoea. We investigated impacts of a program that provided a water filter to households in rural Rwandan villages. We assessed drinking water quality and reported diarrhoea 12-24 months after intervention delivery among 269 households in the poorest tertile with a child under 5 from 9 intervention villages and 9 matched control villages. We also documented filter coverage and use. In Round 1 (12-18 months after delivery), 97.4% of intervention households reported receiving the filter, 84.5%were working, and 86.0% of working filters contained water. Sensors confirmed half of households with working filters filled them at least once every other day on average. Coverage and usage was similar in Round 2 (19-24 months after delivery). The odds of detecting faecal indicator bacteria in drinking water were 78% lower in the intervention arm than the control arm (odds ratio (OR) 0.22, 95% credible interval (CrI) 0.10-0.39, p<0.001). The intervention arm also had 50% lower odds of reported diarrhoea among children <5 than the control arm (OR=0.50, 95% CrI 0.23-0.90, p=0.03). The protective effect of the filter is also suggested by reduced odds of reported diarrhoea-related visits to community health workers or clinics, although these did not reach statistical significance.

show abstract

Section: Discussionmentioning

confidence: 99%

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Kirby

Nagel

Rosa

et al. 2017

International Journal of Hygiene and Environmental Health

View full text Add to dashboard Cite

show abstract

“…The dependent variables were generated with trends and residuals 14 as specified by the DGPs in Table 1. The data were generated in Stata, and the models were estimated using MCMC 15 Gibbs sampling in MLwiN v2.25 (Rasbash et al 2013) with the runmlwin (Leckie and Charlton 2013) (Brooks and Gelman 1998;Gelman and Rubin 1992). The original version of this compares the variance of five different chains (from five different starting values) compared to the variance of the pooling of all five of these chains.…”

Section: Simulation Designmentioning

confidence: 99%

Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model

Bell¹,

Jones²

2014

DemRes

160

154

View full text Add to dashboard Cite

“…The simulations were conducted in a similar way to those in Bell and Jones (2013a), using Bayesian MCMC methods (Browne, 2009) in MLwiN version 2.28 (Rasbash et al, 2013), through Stata using the runmlwin command (Leckie & Charlton, 2013). True values from the DGP were used as starting values as non-informative priors, and the model was run for 20,000 iterations, following a 1000 iteration burn-in.…”

Section: Simulation Exercisementioning

confidence: 99%

Don't birth cohorts matter? A commentary and simulation exercise on Reither, Hauser, and Yang's (2009) age–period–cohort study of obesity

Bell

Jones

2014

Social Science & Medicine

View full text Add to dashboard Cite

(HAPC -Yang & Land, 2006) to assess changes in obesity in the USA population. Their results suggest that there is only a minimal effect of cohorts, and that it is periods which have driven the increase in obesity over time. We use simulations to show that this result may be incorrect. Using simulated data in which it is cohorts, rather than periods, that are responsible for the rise in obesity, we are able to replicate the periodtrending results of Reither et al. In this instance, the HAPC model misses the true cohort trend entirely, erroneously finds a period trend, and underestimates the age trend. ' may be correct, but because age, period and cohort are confounded there is no way to tell. This is typical of age-period-cohort models, and shows the importance of caution when any APC model is used. We finish with a discussion of ways forward for researchers wishing to model age, period and cohort in a robust and non-arbitrary manner. Research Highlights Reither et al HAPC study suggest rises in obesity are caused by period effects  Simulations suggest that this result may be erroneous  Identical results were found with data simulated from an entirely different process  Results show the pitfalls of using APC models without critical forethought.

show abstract

`runmlwin`: A Program to Run theMLwiNMultilevel Modeling Software from withinStata

Cited by 294 publications

References 11 publications

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model

Don't birth cohorts matter? A commentary and simulation exercise on Reither, Hauser, and Yang's (2009) age–period–cohort study of obesity

Contact Info

Product

Resources

About

runmlwin: A Program to Run theMLwiNMultilevel Modeling Software from withinStata

Cited by 294 publications

References 11 publications

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model

Don't birth cohorts matter? A commentary and simulation exercise on Reither, Hauser, and Yang's (2009) age–period–cohort study of obesity

Contact Info

Product

Resources

About

`runmlwin`: A Program to Run theMLwiNMultilevel Modeling Software from withinStata