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Hi Samaya,
Many program can perform bootstrap. If you use SAS, try
* Note that you can have sample size which is greater than your original data since this is sampling with replacement.
now you have a data set with 1000 sets of data with each set having 28 subjects. You can do BE analyse as usual (add
However, there are some drawbacks such as repeated subject number, sometimes sequence is extremely unbalanced (e.g., for certain replicate you might have 20 subject with TR and 8 with RT) etc.
You can generate dummy subject number to replace with the original ones and to remove certain replicates with extremely unbalanced sequence (what you think is unlikely occur in real life. for example, for BE study with 100 subjects (50TR+50RT), after 15 drop out, 45TR +40RT might be likely but 50TR +35RT might not) before doing BE analysis.
Also, you can try adding some random number to your logPK data based on the intra-subject variability of the formulation before analysis. But I'll leave it to you to try them.
Please read the sas manual for details.
❝ Can you please explain in some more detail how to use bootstrap data set to get a sense of the data and bootstrap with larger sample size?
Many program can perform bootstrap. If you use SAS, try
PROC SURVEYSELECT. Use METHOD=URS for sampling with replacement. You can specify the sample size (say 28) with SAMPSIZE=28* and and repeated number (say 1000 times) with REPS=1000. Use subject as selection unit with SAMPLINGUNIT subject; .* Note that you can have sample size which is greater than your original data since this is sampling with replacement.
now you have a data set with 1000 sets of data with each set having 28 subjects. You can do BE analyse as usual (add
BY replicate statement in your sas code) so for each replicate you'll have T/R ratio and 90% CI and ISCV etc. So you'll have 1000 of those now. Obvious BE result of each replicate will be slightly different. Now you can see how they vary.However, there are some drawbacks such as repeated subject number, sometimes sequence is extremely unbalanced (e.g., for certain replicate you might have 20 subject with TR and 8 with RT) etc.
You can generate dummy subject number to replace with the original ones and to remove certain replicates with extremely unbalanced sequence (what you think is unlikely occur in real life. for example, for BE study with 100 subjects (50TR+50RT), after 15 drop out, 45TR +40RT might be likely but 50TR +35RT might not) before doing BE analysis.
Also, you can try adding some random number to your logPK data based on the intra-subject variability of the formulation before analysis. But I'll leave it to you to try them.
Please read the sas manual for details.
—
All the best,
Shuanghe
All the best,
Shuanghe
Complete thread:
- Results Analysis unique_one 2014-07-08 08:58 [Study Assessment]
![[Mix]](https://static.bebac.at/img/mix.png "open in Mix view")
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- The smaller a study, the more uncertain its results Helmut 2014-07-08 17:39
- The smaller a study, the more uncertain its results unique_one 2014-07-09 06:59
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- The smaller a study, the more uncertain its results unique_one 2014-07-09 06:59
- The smaller a study, the more uncertain its results Helmut 2014-07-08 17:39
- Don’t hurry; explore consequences unique_one 2014-07-08 14:01
- 156 ? d_labes 2014-07-08 12:03
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- Results Analysis Shuanghe 2014-07-08 12:57
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- bootstrapShuanghe 2014-07-09 18:26
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- Bootstrapping BE: Desultory thoughts ElMaestro 2021-07-09 16:29
- Bootstrapping BE: Desultory thoughts Helmut 2021-07-09 14:50
- Bootstrapping BE: An attempt in 🇷 ElMaestro 2021-07-09 14:15
- Bootstrapping BE: An attempt in 🇷 Helmut 2021-07-08 13:50
- bootstrap Weidson 2021-07-03 01:36
- bootstrapShuanghe 2014-07-09 18:26
- Results Analysis Samaya B 2014-07-09 07:58
- Results Analysis unique_one 2014-07-08 14:08
- Don’t hurry; explore consequences Helmut 2014-07-08 12:00
- Results Analysis unique_one 2014-07-08 10:42
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Ing. Helmut Schütz