OBJECTIVE: To describe the statistical design issues and practical considerations that had to be addressed in setting up a clustered observational study of emergency admission to hospital of elderly people. STUDY DESIGN AND SETTING: Clustered observational study (sample survey) of elderly people registered with 18 general practices in Halton Primary Care Trust in the north-west of England. RESULTS: The statistical design features that warranted particular attention were sample size determination, intra-class correlation, sampling and recruitment, bias and confounding. Pragmatic decisions based on derived scenarios of different design effects are discussed. A pilot study was carried out in one practice. From the remaining practices, a total of 4000 people were sampled, stratified by gender. The average cluster size was 200 and the intra-class correlation coefficient for the emergency admission outcome was 0.00034, 95% confidence interval (0-0.008). CONCLUSION: Studies that involve sampling from clusters of people are common in a wide range of healthcare settings. The clustering adds an extra level of complexity to the study design. This study provides an empirical illustration of the importance of statistical as well as clinical reasoning in study design in clinical practice.
OBJECTIVE: To describe the statistical design issues and practical considerations that had to be addressed in setting up a clustered observational study of emergency admission to hospital of elderly people. STUDY DESIGN AND SETTING: Clustered observational study (sample survey) of elderly people registered with 18 general practices in Halton Primary Care Trust in the north-west of England. RESULTS: The statistical design features that warranted particular attention were sample size determination, intra-class correlation, sampling and recruitment, bias and confounding. Pragmatic decisions based on derived scenarios of different design effects are discussed. A pilot study was carried out in one practice. From the remaining practices, a total of 4000 people were sampled, stratified by gender. The average cluster size was 200 and the intra-class correlation coefficient for the emergency admission outcome was 0.00034, 95% confidence interval (0-0.008). CONCLUSION: Studies that involve sampling from clusters of people are common in a wide range of healthcare settings. The clustering adds an extra level of complexity to the study design. This study provides an empirical illustration of the importance of statistical as well as clinical reasoning in study design in clinical practice.
Authors: William A Grobman; Jennifer L Bailit; Madeline Murguia Rice; Ronald J Wapner; Michael W Varner; John M Thorp; Kenneth J Leveno; Steve N Caritis; Jay D Iams; Alan T Tita; George Saade; Yoram Sorokin; Dwight J Rouse; Jorge E Tolosa; J Peter Van Dorsten Journal: Am J Obstet Gynecol Date: 2014-03-12 Impact factor: 8.661
Authors: Thomas E Love; Randall D Cebul; Douglas Einstadter; Anil K Jain; Holly Miller; C Martin Harris; Peter J Greco; Scott S Husak; Neal V Dawson Journal: J Gen Intern Med Date: 2008-04 Impact factor: 5.128
Authors: Peter Bower; Martin Cartwright; Shashivadan P Hirani; James Barlow; Jane Hendy; Martin Knapp; Catherine Henderson; Anne Rogers; Caroline Sanders; Martin Bardsley; Adam Steventon; Raymond Fitzpatrick; Helen Doll; Stanton Newman Journal: BMC Health Serv Res Date: 2011-08-05 Impact factor: 2.655
Authors: Adam Steventon; Martin Bardsley; John Billings; Jennifer Dixon; Helen Doll; Shashi Hirani; Martin Cartwright; Lorna Rixon; Martin Knapp; Catherine Henderson; Anne Rogers; Ray Fitzpatrick; Jane Hendy; Stanton Newman Journal: BMJ Date: 2012-06-21
Authors: Adam Steventon; Martin Bardsley; John Billings; Jennifer Dixon; Helen Doll; Michelle Beynon; Shashi Hirani; Martin Cartwright; Lorna Rixon; Martin Knapp; Catherine Henderson; Anne Rogers; Jane Hendy; Ray Fitzpatrick; Stanton Newman Journal: Age Ageing Date: 2013-02-25 Impact factor: 10.668