Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression
- PMID: 20101669
- DOI: 10.1002/sim.3857
Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression
Abstract
Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most.
(c) 2010 John Wiley & Sons, Ltd.
Similar articles
-
Varying cluster sizes in trials with clusters in one treatment arm: sample size adjustments when testing treatment effects with linear mixed models.Stat Med. 2009 Aug 15;28(18):2307-24. doi: 10.1002/sim.3620. Stat Med. 2009. PMID: 19472169
-
Relative efficiency of unequal versus equal cluster sizes in cluster randomized and multicentre trials.Stat Med. 2007 Jun 15;26(13):2589-603. doi: 10.1002/sim.2740. Stat Med. 2007. PMID: 17094074
-
Relative efficiency of unequal cluster sizes for variance component estimation in cluster randomized and multicentre trials.Stat Methods Med Res. 2008 Aug;17(4):439-58. doi: 10.1177/0962280206079018. Epub 2007 Aug 14. Stat Methods Med Res. 2008. PMID: 17698940
-
Imputation strategies for missing continuous outcomes in cluster randomized trials.Biom J. 2008 Jun;50(3):329-45. doi: 10.1002/bimj.200710423. Biom J. 2008. PMID: 18537126 Review.
-
Planning a cluster randomized controlled trial: methodological issues.Nurs Res. 2009 Mar-Apr;58(2):128-34. doi: 10.1097/NNR.0b013e3181900cb5. Nurs Res. 2009. PMID: 19289934 Review.
Cited by
-
Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome.Stat Methods Med Res. 2021 May;30(5):1288-1305. doi: 10.1177/0962280221990415. Epub 2021 Apr 7. Stat Methods Med Res. 2021. PMID: 33826454 Free PMC article.
-
A web-based education program to encourage organ donation registration among lower-educated adolescents in the Netherlands: study protocol for a cluster randomized controlled trial.Trials. 2018 Oct 1;19(1):532. doi: 10.1186/s13063-018-2927-6. Trials. 2018. PMID: 30285823 Free PMC article.
-
Efficient design of cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances.Stat Med. 2018 Sep 20;37(21):3027-3046. doi: 10.1002/sim.7824. Epub 2018 Jun 10. Stat Med. 2018. PMID: 29888393 Free PMC article.
-
Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation.Biom J. 2021 Jun;63(5):1052-1071. doi: 10.1002/bimj.202000230. Epub 2021 Mar 10. Biom J. 2021. PMID: 33751620 Free PMC article.
-
Sample size calculation in hierarchical factorial trials with unequal cluster sizes.Stat Med. 2022 Feb 20;41(4):645-664. doi: 10.1002/sim.9284. Epub 2022 Jan 2. Stat Med. 2022. PMID: 34978097 Free PMC article.