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Stephan Kolassa
Stephan Kolassa
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I usually find it easier and faster to run a simulation. Papers take a long time to read, to understand and finally come to the conclusion that they don't apply in the special case one is interested in.

Therefore, I would just pick a number of subjects, simulate the covariate you are interested in (distributed as you believe it will be), simulate good/bad outcomes based on the functional form you posit (threshold effects of the covariate? nonlinearity?) with the minimum (clinically) significant effect size you would like to detect, run the result through your analysis and see whether the effect is found at youyour alpha. Rerun this 10,000 times and look whether you found the effect in 80% of the simulations (or whatever other power you need). Adjust the number of subjects, repeat until you have a power you are happy with.

This has the advantage of being very general, so you are not confined to a specific functional form or a specific number or distribution of covariates. You can include dropouts, see chl's comment above, either at random or influenced by covariate or outcome. You basically code the analysis you are going to do on the final sample beforehand, which sometimes helps focus my thinking on the study design. And it is easily done in R (vectorize!).

I usually find it easier and faster to run a simulation. Papers take a long time to read, to understand and finally come to the conclusion that they don't apply in the special case one is interested in.

Therefore, I would just pick a number of subjects, simulate the covariate you are interested in (distributed as you believe it will be), simulate good/bad outcomes based on the functional form you posit (threshold effects of the covariate? nonlinearity?) with the minimum (clinically) significant effect size you would like to detect, run the result through your analysis and see whether the effect is found at you alpha. Rerun this 10,000 times and look whether you found the effect in 80% of the simulations (or whatever other power you need). Adjust the number of subjects, repeat until you have a power you are happy with.

This has the advantage of being very general, so you are not confined to a specific functional form or a specific number or distribution of covariates. You can include dropouts, see chl's comment above, either at random or influenced by covariate or outcome. You basically code the analysis you are going to do on the final sample beforehand, which sometimes helps focus my thinking on the study design. And it is easily done in R (vectorize!).

I usually find it easier and faster to run a simulation. Papers take a long time to read, to understand and finally come to the conclusion that they don't apply in the special case one is interested in.

Therefore, I would just pick a number of subjects, simulate the covariate you are interested in (distributed as you believe it will be), simulate good/bad outcomes based on the functional form you posit (threshold effects of the covariate? nonlinearity?) with the minimum (clinically) significant effect size you would like to detect, run the result through your analysis and see whether the effect is found at your alpha. Rerun this 10,000 times and look whether you found the effect in 80% of the simulations (or whatever other power you need). Adjust the number of subjects, repeat until you have a power you are happy with.

This has the advantage of being very general, so you are not confined to a specific functional form or a specific number or distribution of covariates. You can include dropouts, see chl's comment above, either at random or influenced by covariate or outcome. You basically code the analysis you are going to do on the final sample beforehand, which sometimes helps focus my thinking on the study design. And it is easily done in R (vectorize!).

Source Link
Stephan Kolassa
Stephan Kolassa
  • 130.8k
  • 22
  • 264
  • 497

I usually find it easier and faster to run a simulation. Papers take a long time to read, to understand and finally come to the conclusion that they don't apply in the special case one is interested in.

Therefore, I would just pick a number of subjects, simulate the covariate you are interested in (distributed as you believe it will be), simulate good/bad outcomes based on the functional form you posit (threshold effects of the covariate? nonlinearity?) with the minimum (clinically) significant effect size you would like to detect, run the result through your analysis and see whether the effect is found at you alpha. Rerun this 10,000 times and look whether you found the effect in 80% of the simulations (or whatever other power you need). Adjust the number of subjects, repeat until you have a power you are happy with.

This has the advantage of being very general, so you are not confined to a specific functional form or a specific number or distribution of covariates. You can include dropouts, see chl's comment above, either at random or influenced by covariate or outcome. You basically code the analysis you are going to do on the final sample beforehand, which sometimes helps focus my thinking on the study design. And it is easily done in R (vectorize!).