Mixed-effects, mixed distribution model

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Mixed-effects, mixed distribution model

Post by Gujarish » Sat Jul 06, 2019 8:57 am

Hello everyone,,,
I have collected three waves of data (2007, 2009, and 2011) on the number of minutes that people report walking for transport in the previous 7 days. The sample-size at each wave comprised 200 neighbourhoods and 11,000, 7900, and 6900 respondents in 2007, 2009 and 2011 respectively. Hence I have a three-level repeated measures dataset (neighbourhoods, individuals, time).

The variable at each wave has an excessive number of cases with zero-values (~60%), reflecting the fact that most people didn't walk for transport during the survey reference period; the rest of the cases have non-zero values that range from 1-840 minutes. Hence, the non-zero data arise from a continuous distribution, and are not independent counts.

Tooze et al (Statisitical Methods in Medical Research, 2002;11:341-355) propose a model for repeated measures data with clumping at zero, using a mixed effects mixed distribution model with correlated random effects. The model contains components to model the probability of a non-zero value and the mean of non-zero values, allowing for repeated measurments using random effects and allowing for correlation between the two components. They used the MIXCORR macro in SAS PROC NLMIXED.

Can this type of model be estimated using "runmlwin"?

Many thanks in advance. Regards.

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Re: Mixed-effects, mixed distribution model

Post by GeorgeLeckie » Mon Jul 08, 2019 12:04 pm

Dear Gujarish,

No you cannot fit this specific model in MLwiN.

For these data, what you could do in MLwiN is fit the analyses in two parts.

First, fit a multilevel model for whether peopled walked at all (0 steps vs 1+ steps)

Then for the subset who had 1+ steps you could fit a multilevel model for the number of steps taken

Best wishes


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