[R] JMdesign package
bernardnorth at hotmail.com
Mon May 22 12:59:58 CEST 2017
Dear R list
I wonder please if anyone has experience they can share of the JMdesign package that performs sample sized for (surviival/longitudinal) joint models and is based on the paper "Sample size and power determination in joint modeling of longitudinal and survival data " by Chen et al (Statistics in Medicine 2011) .
If so I do have a few queries please.
1. the vignette states that JMdesign can work when the variance-covariance matrix Sigma_theta is unknown. This is the the covariance matrix of the intercept, linear and quadratic effects of the longitudinal profiles of the longitudinal (time-dependent) variable thats affecting event time.
I wonder if that refers to the input SigmaTheta to the R function powerLongSurv in the package ? Because it looks to me as though most of the examples in the vignette do supply a value for that matrix and it errors if its missing. I realise section 3.2 of the Liddy Chen paper does refer to the unknown covariance matrix situation
2. Im also a little confused as to what is meant in the JMdesign vignette by Example 1 and "formula 4.6". Do these refer to the Chen paper ? I cant see a formula denoted 4.6.
3. The original Chen paper refers on page 3 to two possible objectives: 1) power for testing the effect of the longitudinal trajectory and 2) power for testing the effect of a fixed covariate, eg treatment, that affects the trajectory (and therefore indirectly survival) but also has a direct effect on survival.
Does the power calculation in the current JMdesign package only provide power of the first test ? I think it does but from the paper both calculations are possible.
4. Can anyone help with how to estimate the covariance matrix of the intercept, linear and quadratic effects for the Sigma_theta input. I thought theVarCorr output from a lmer mixed model analysis of the profiles might provide the elements for that matrix
my apologies for so many questions - many thanks for any thoughts on any of them
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