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 Curious
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Aug
25
revised Estimating medians and modes of skewed distributions using GLMs
Improved the question - my previous descriptions of estimated quantities were vague.
Aug
25
revised Estimating medians and modes of skewed distributions using GLMs
correcting a typo
Aug
25
awarded  Curious
Aug
24
revised Estimating medians and modes of skewed distributions using GLMs
expanded question to include the work I have done on this problem
Aug
24
asked Estimating medians and modes of skewed distributions using GLMs
Jul
24
comment Computing Standard Errors in EM algorithm
I am not sure if this theory carries straight over to the complete data observed information matrix too. I imagine it does since the EM algorithm by maximising the complete data LL also maximises the ordinary LL. However I would want to see some theory on this and I am no expert.
Jul
24
comment Computing Standard Errors in EM algorithm
The Observed information matrix under certain "regularity conditions" can be a consistent estimator of the Fisher Information matrix. I tried to prove this in a post here
Jul
23
revised How to simulate informative censoring in a Cox PH model?
To clarify some comments I made
Jul
23
comment How to simulate informative censoring in a Cox PH model?
Sorry I don't know how to post the code here in a comment! I think I need to assume a model for the event times beyond the point of censoring - this would be a counterfactual event. I think if this counterfactual model is the same as the event times model then censoring will only affect the baseline survival function estimate (shifts it down?), but the regression parameters remain well estimated. Only if the counterfactual model is different does censoring make a difference - i.e. running a coxph on the "full" data if we could see it would produce a different result than on the observed data.
Jul
23
comment How to simulate informative censoring in a Cox PH model?
Thanks @DWin. I looked at the link you gave, and as far as I could understand, the code therein showed how the baseline hazard function estimate becomes worse as time increases (due I think to the ever decreasing numbers of subjects at risk). I amended this code keeping the same censoring function. I also simulated covariate effects. If I choose the sample size high enough I can get unbiased estimates from coxph even though the same degradation of the hazard function occurs for increasing time. Thus the bias you speak of I think does not relate to estimation of the regression parameters.
Jul
22
revised How to simulate informative censoring in a Cox PH model?
improving notation
Jul
21
asked How to simulate informative censoring in a Cox PH model?
Jun
24
asked covariate-adjusted analysis for time to event endpoint in a cross over design RCT
Jun
8
revised Sample size calculation for crossover design using dependent t-test - role of $\rho$?
Clarified the difference between the index j for both models (treatments versus periods)
Jun
6
asked Sample size calculation for crossover design using dependent t-test - role of $\rho$?
Jun
4
awarded  Nice Question
Jun
1
comment Residual Diagnostics and Homogeneity of variances in linear mixed model
Great questions - a possible answer to your number 2 can be found here comp.soft-sys.sas.narkive.com/7Qmrgufe/…
May
28
awarded  Popular Question
Feb
11
comment Picking cases to label for classification
The parameter estimates obtained could then be used to build prior distributions on the model paramters so that you could then estimate within a Bayesian framework the mixture model on the labelled and unlabelled data. In this way once the expert has done their classification, the classification of subjects to diseased/un-diseased would be model-based. The accuracy of this classification could of course be checked against the cases the expert has reviewed.
Feb
11
comment Picking cases to label for classification
You could use mixture models assuming your subjects are drawn from one of 2 subgroups in the population: diseased or not. If your response variable is say normally distributed (maybe some measure of kidney function over time) then you could fit a 2-component normal mixture distribution to the data where each group is modelled with a (for example) linear mixed effects model. You could then incorporate the expert by fitting 2 separate mixed models to only those cases reviewed (hence you know the 2 groups).