Sampe size calculations [Power / Sample Size]
Hi everyone,
I must first admit that I am statistically challenge. Yes, I took it at university and passed, but the keg of beer I had after the final exam purged more than the contents of my bladder.
Let's say we want to conduct an equivalency study, with 80% power and confidence margins of 10%. The primary outcome we want to measure, depending on what criteria is used, occurs at a rate of either 5% or 10%. My brain is telling me that if we chose a primary outcome that will occur at an expected rate of 5%, we would need a significantly higher sample size than if we chose a primary endpoint that occurs at a rate of 10%.
But I'm told that this is not actually the case. I probably misunderstood, but this is how I understood what I was told. Apparently below 50% (expected rate), the variability in expected to be high (variability is apparently higher at 10% than at 5%!), and therefore, the lower the rate the lower the sample size needed. However, once you go above 50% expected rates, then the opposite happens. As you approach 100% expected rates, then the variability descreases and the sample size decreases too (so, it seems variability is highest in the 50% neighborhood and lowest in the suburbs). The bit between 50% and 100% makes perfect sense to me. But the bit below 50% does not. Like I said, I would expect a higher sample size with a primary outcome with expected rates of 5% than with an expected rate of 10%. But that's not what I'm being told. Can someone point out where I'm going cuckoo?
Thank you.
I must first admit that I am statistically challenge. Yes, I took it at university and passed, but the keg of beer I had after the final exam purged more than the contents of my bladder.
Let's say we want to conduct an equivalency study, with 80% power and confidence margins of 10%. The primary outcome we want to measure, depending on what criteria is used, occurs at a rate of either 5% or 10%. My brain is telling me that if we chose a primary outcome that will occur at an expected rate of 5%, we would need a significantly higher sample size than if we chose a primary endpoint that occurs at a rate of 10%.
But I'm told that this is not actually the case. I probably misunderstood, but this is how I understood what I was told. Apparently below 50% (expected rate), the variability in expected to be high (variability is apparently higher at 10% than at 5%!), and therefore, the lower the rate the lower the sample size needed. However, once you go above 50% expected rates, then the opposite happens. As you approach 100% expected rates, then the variability descreases and the sample size decreases too (so, it seems variability is highest in the 50% neighborhood and lowest in the suburbs). The bit between 50% and 100% makes perfect sense to me. But the bit below 50% does not. Like I said, I would expect a higher sample size with a primary outcome with expected rates of 5% than with an expected rate of 10%. But that's not what I'm being told. Can someone point out where I'm going cuckoo?
Thank you.
Complete thread:
- Sampe size calculationsThe Outlaw Torn 2012-02-09 12:04
- Sampe size for equivalence of proportions d_labes 2012-02-10 09:28
- Sampe size for equivalence of proportions The Outlaw Torn 2012-02-10 11:59
- Going to Pup before cataclysm d_labes 2012-02-10 13:10
- Going to Pup before cataclysm The Outlaw Torn 2012-02-10 14:18
- Quasi-synonyms d_labes 2012-02-10 15:18
- Going to Pup before cataclysm The Outlaw Torn 2012-02-10 14:18
- Going to Pup before cataclysm d_labes 2012-02-10 13:10
- Sampe size for equivalence of proportions The Outlaw Torn 2012-02-10 11:59
- Sampe size for equivalence of proportions d_labes 2012-02-10 09:28
