Power in Diletti's sample size table [🇷 for BE/BA]

posted by Helmut Homepage – Vienna, Austria, 2007-01-02 00:59 (6547 d 01:50 ago) – Posting: # 428
Views: 45,680

(edited on 2007-06-04 12:41)

Dear all,

I performed some comparisons of the code based on Jones/Kenward implemented in R, Diletti's table and results obtained from StudySize (v2.0.1). Quite interesting...

All comparisons were done for CV=20% with ratios of 0.85–1.20. Dilleti reported sample sizes to obtain ≥80% power, calculated odd sample sizes were reported rounded up to the next even number (underlined):
+======+=====+===========+===========+
|  GMR |  n  |  R-code   | StudySize |
+------+-----+-----------+-----------+
| 0.85 | 134 | 0.8014178 |  0.80167  |
| 0.90 |  38 | 0.8140704 |  0.81536  |
| 0.95 |  20 | 0.8300156 |  0.83451  |
| 1.00 |  16 | 0.8214263 |  0.83305  |
| 1.05 |  18 | 0.79503430.79996  |
| 1.10 |  32 | 0.8084890 |  0.80992  |
| 1.15 |  72 | 0.8035456 |  0.80411  |
| 1.20 | 294 | 0.8017617 |  0.80182  |
+======+=====+===========+===========+


Power with sample sizes given by Diletti et al. at a GMR of 1.05 were below 80%, calculated both with R and StudySize…

A Monte Carlo Simulation (1000000 runs) for GMR=1.05 and 18 subjects in StudySize resulted in:
Power 0.8006 (95% coverage probability: 0.7998-0.8013).

Differences may be due to the implementation of the algorithm to obtain the value of the noncentral t-distribution by numeric integration...

Maybe somebody of you has access to SAS or software specialized in power analysis (e.g., PASS or nQuery Advisor) and would like to check these results?

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