

Sibling clustering in LGM 

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I am doing a latent curve model (i.e. growth model) with data including MZ, DZ, full and halfsiblings. Thus, there is nonidependence within sibclusters and the degree of clustering will vary between the 4 sibtypes. I want one set of estimates though, not 4 (i.e. one for each sibtype, as in a multiple group analysis). Essentially, I just want the standard errors to be adjusted for the complex clustering, but otherwise (i.e. one set of parameters) standard output. Also, for the command that does this, could you direct me to the underlying math. thanks much. 


Perhaps you want to do Type= Complex where cluster = family. The cluster members are then the MZ...sibs. This is using a "sandwich" estimator for the SEs as described in the Version 2 Tech appendix 8, eqn (170). Here it is only assumed that you have independent observations across families, not within families. I am confused by your statement that you want one set of estimates, not 4. You also say that you want one for each sibtype  isn't that 4? 


Sorry i was not clear. I have four types of sibpairs in the data (MZ twin...half sibs). I want to estimate 1 set of parameters for the whole sample, as opposed to one set of parameters for each sibtype (as in a multiple group analysis). I was concerned that using the standard: "Type= Complex where cluster = family" would not be sufficient to account for the differing degree of clustering between sibtypes. I don't know though. What do you think? 


The sandwich formula assumes nothing about degree or varying degree of withinfamily correlatedness, so I think it should be fine. One could alternatively analyze in a multivariate fashion (2 sibs measured on 1 variable gives 2variate observation vector), where the correlatedness is instead modeled. You say that you don't want multiplegroup analysis, but there is the new alternative used in the Version 4 User's Guide QTL example, ex5.23, where the sib type correlation variation can be read in as data (1., 0.5, etc). 


Thank you. You and your team provide an excellent product and impeccable technical support. M+ is amazing. 

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