Cross Validated Asked by Paze on February 4, 2021
I’ve got a study in which patients (record_id) can have from 1 to 5 aneurysms (concurrently) and each may be treated differently (each aneurysm). We are interested to see whether one treatment (treatmentBinary) is different than the other and what risk factors may contribute to adverse effects.
I’ve set the data up so that we have one observation per aneurysm and not per patient. That means that one patient may be recorded upwards to 5 observations with a variable aneurysm_id denoting which aneurysm the observation is referring to.
I’m testing the model on this data:
[CODE]
* Example generated by -dataex-. To install: ssc install dataex
clear
input str32 record_id float(aneurysm_id neurotromb treatmentBinary)
"007128de18ce5cb1635b8f27c5435ff3" 1 0 1
"00abd7bdb6283dd0ac6b97271608a122" 1 0 .
"0142103f84693c6eda416dfc55f65de1" 1 0 .
"0153826d93a58d7e1837bb98a3c21ba8" 1 0 0
"01c729ac4601e36f245fd817d8977917" 1 0 .
"01c729ac4601e36f245fd817d8977917" 2 0 .
"01dd90093fbf201a1f357e22eaff6b6a" 1 0 .
"0208e14dcabc43dd2b57e2e8b117de4d" 1 0 .
"0210f575075e5def7ffa77530ce17ef0" 1 0 1
"022cc7a9397e81cf58cd9111f9d1db0d" 1 0 1
"02afd543116a22fc7430620727b20bb5" 1 0 .
"0303ef0bd5d256cca1c836e2b70415ac" 1 0 .
"0303ef0bd5d256cca1c836e2b70415ac" 2 0 .
"041b2b0cac589d6e3b65bb924803cf1a" 1 0 0
"0536317a2bbb936e85c3eb8294b076da" 1 0 .
"06161d4668f217937cac0ac033d8d199" 1 0 1
"065e151f8bcebb27fabf8b052fd70566" 1 0 1
"065e151f8bcebb27fabf8b052fd70566" 2 0 .
"065e151f8bcebb27fabf8b052fd70566" 3 0 .
"065e151f8bcebb27fabf8b052fd70566" 4 0 1
"07196414cd6bf89d94a33e149983d102" 1 0 1
"0721c38f8275dab504fc53aebcc005ce" 1 0 1
"0721c38f8275dab504fc53aebcc005ce" 2 0 0
"0721c38f8275dab504fc53aebcc005ce" 3 0 0
"0721c38f8275dab504fc53aebcc005ce" 4 0 .
"07bef516d53279a3f5e477d56d552a2b" 1 0 .
"08678829b7e0ee6a01b17974b4d19cfa" 1 0 1
"08bb6c65e63c499ea19ac24d5113dd94" 1 0 1
"08f036417500c332efd555c76c4654a0" 1 1 0
"090c54d021b4b21c7243cec01efbeb91" 1 0 1
"09166bb44e4c5cdb8f40d402f706816e" 1 0 .
"0930159addcdc35e7dc18812522d4377" 1 0 0
"096844af91d2e266767775b0bee9105e" 1 0 .
"09884af1bb9d59803de0c74d6df57c23" 1 0 .
"09e03748da35e9d799dc5d8ddf1909b5" 1 0 0
"0a4ce4a7941ff6d1f5c217bf5a9a3bf9" 1 0 0
"0a5db40dc58e97927b407c9210aab7ba" 1 0 1
"0a5db40dc58e97927b407c9210aab7ba" 2 0 1
"0a73c992955231650965ed87e3bd52f6" 1 0 .
"0a84ab77fff74c247a525dfde8ce988c" 1 0 1
"0a84ab77fff74c247a525dfde8ce988c" 2 0 0
"0a84ab77fff74c247a525dfde8ce988c" 3 0 .
"0af333ae400f75930125bb0585f0dcf5" 1 0 0
"0af73334d9d2166191f3385de48f15d2" 1 0 .
"0b341ac8f396a8cdb88b7c658f66f653" 1 0 1
"0b341ac8f396a8cdb88b7c658f66f653" 2 0 .
"0b35cf4beb830b361d7c164371f25149" 1 0 .
"0b35cf4beb830b361d7c164371f25149" 2 0 .
"0b3e110c9765e14a5c41fadcc3cfc300" 1 . .
"0b6681f0f441e69c26106ab344ac0733" 1 0 1
"0b8d8253a8415275dbc2619e039985bb" 1 0 1
"0b8d8253a8415275dbc2619e039985bb" 2 0 1
"0b8d8253a8415275dbc2619e039985bb" 3 0 0
"0b92c26375117bf42945c04d8d6573d4" 1 0 .
"0b92c26375117bf42945c04d8d6573d4" 2 0 .
"0ba961f437f43105c357403c920bdef1" 1 0 0
"0bb601fabe1fdfa794a5272408997a2f" 1 0 0
"0c75b36e91363d596dc46bd563c3f5ef" 1 0 .
"0d461328a3bae7164ce7d3a10f366812" 1 0 .
"0d4cc4eb459301a804cbef22914f44a3" 1 0 .
"0d4e29e11bb94e922112089f3fec61ef" 1 0 .
"0d4e29e11bb94e922112089f3fec61ef" 2 0 .
"0d513c74d667f55c8f4a9836c304149c" 1 0 .
"0da25de126bb3b3ee565eff8888004c2" 1 0 .
"0da25de126bb3b3ee565eff8888004c2" 2 0 .
"0db9ae1f2201577f431b7603d0819fa6" 1 0 .
"0dd8a681f6a5d4c888831a591e57a747" 1 0 1
"0e05d6958d878368b5fb831211fad6a1" 1 0 .
"0e3ff41e0e2b2cb5ec336fd0b04e5d44" 1 0 0
"0f61e560ab56b8fea1f2593d7d3b2718" 1 0 .
"0f61e560ab56b8fea1f2593d7d3b2718" 2 0 .
"0f69f1f998984d37f133185179d63c60" 1 0 1
"1037032886a93e66406a4c910d1ef747" 1 0 .
"1037032886a93e66406a4c910d1ef747" 2 0 .
"1044b81b354b420e85ae835ea07de2d6" 1 0 .
"10620fc488346291281212a404681386" 1 0 .
"1074389c469944edf026d193a55b1148" 1 0 1
"1090d5a678119b03cddab609289a4d3c" 1 0 0
"111eebb45cef2211a2a2ff0219095e6a" 1 0 .
"11ddcbc8de8ef56cbc578fc81b602ffc" 1 0 1
"11f22488513cf717c333786c789b0289" 1 0 1
"11f22488513cf717c333786c789b0289" 2 0 1
"121552b22cee2a1eb4360b4d2534cd39" 1 0 0
"1251d707c5dc9243dc45d04beb7c3493" 1 0 .
"125689659bb3821fa81698dd72462773" 1 0 .
"127ba572433921c5bb408fc62eb9b5d7" 1 0 0
"129bea3f73e84e37d77d55fadfeb49dd" 1 0 1
"12e8dc6fb87822be26d6678cee9644f5" 1 0 1
"12f05a65f771c9675c2c5e9cdbfc33d1" 1 0 1
"12f05a65f771c9675c2c5e9cdbfc33d1" 2 0 1
"13d2bc86f1a19ed2959cd7354bc92d1d" 1 0 .
"13db5ede38e2ae1da17884c9a18df202" 1 0 1
"13f946e50df8ad74d7cf9fa05b4ad05b" 1 0 .
"146c4b8be7996a9789873fe55a47ab41" 1 0 0
"147fadd87da13a0271225d944d2a5e98" 1 0 1
"14a1dcfa015343bbefaac9a3a45769e5" 1 1 1
"14a1dcfa015343bbefaac9a3a45769e5" 2 1 .
"14d1377f74a63ffa29db2d99e7f6a1ce" 1 0 .
"150017d944a87b4c61f90034380c0659" 1 0 1
"150f6ca1ea453260eabf3472d3ebcad1" 1 0 1
end
[/CODE]
This is an excerpt of 100 observations.
I’m running a mixed logistic model with neurotromb as the dependent and treatmentBinary as independent, and grouping by record_id. I haven’t used aneurysm_id in the model and I’m not sure as to whether I should or not?
In any case the model takes a long time to run and never reaches convergence. I don’t understand why and am hoping someone perhaps can see?
Thank you.
You don't want aneurysm_id in the model because this is the measurement-level identifier. For the first question, "whether one treatment is different than the other", your model should look something like:
outcome ~ treatmentBinary + confounders + competing exposures + (1 | record_id)
For the 2nd question, "what risk factors may contribute to adverse effects" the model will be similar, but for each "risk factor" you must ensure that you run a seperate model, where only confounders (and competing exposures) are included, and not meditators. This is because, for the association of variable A with the outcome, a variable B may be a confounder, and should therefore be included, but when assising the association of variable B with the outcome, A would be a mediator and should not be included. A causal diagram is very helpful to determine the set of variables for include. See this answer for more details:
How do DAGs help to reduce bias in causal inference?
As to the specific reasons for Stata having problems converging, this could be related to the small cluster sizes. You could try using -meqrlogit- instead of -melogit-, or alternatively try glmer from the lme4 package in R
Answered by Robert Long on February 4, 2021
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