PubMed · 10728550
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J M Mayr, E H Sorantin. 2000. Foam party risks.. https://doi.org/10.1136/ip.6.1.73-a
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Because of the central role of the general practice in the delivery of British primary care, intervention trials in primary care often use the practice as the unit of randomization. The creation of primary care groups (PCGs) in April 1999 changed the organization of primary care and the commissioning of secondary care services. PCGs will directly affect the organization and delivery of primary, secondary and social care services. The PCG therefore becomes an appropriate target for organizational and educational interventions. Trials testing these interventions should involve randomization by PCG. This paper discusses the sample size required for a trial in primary care assessing the effect of a falls prevention programme among older people. In this trial PCGs will be randomized. The sample size calculations involve estimating intra-PCG correlation in primary outcome: fractured femur rate for those 65 years and over. No data on fractured femur rate were available at PCG level. PCGs are, however, similar in size and often coterminous with local authorities. Therefore, intra-PCG correlation in fractured femur rate was estimated from the intra-local authority correlation calculated from routine data. Three alternative trial designs are considered. In the first design, PCGs are selected for inclusion in the trial from the total population of England (eight regions). In the second design, PCGs are selected from two regions only. The third design is similar to the second except that PCGs are stratified by region and baseline value of fracture rate. Intracluster correlation is estimated for each of these designs using two methods: an approximation which assumes cluster sizes are equal and an alternative method which takes account of the fact that cluster sizes vary. Estimates of sample size required vary between 26 and 7 PCGs in each intervention group, depending on the trial design and the method used to calculate sample size. Not unexpectedly, stratification by baseline value of the outcome variable decreases the sample size required. In our analyses, geographic restriction of the population to be sampled reduces between-cluster variability in the primary outcome. This leads to an increase in precision. When allowance for variable cluster size is made, the increase in precision is not as great as would be expected with equal cluster sizes. This paper highlights the usefulness of routine data in work of this kind, and establishes one of the essential prerequisites for our proposed trial and other trials using primary outcomes with similar between-PCG variation: a feasible sample size.
Cluster randomized trials are often used in primary care settings. In the U.K., general practices are usually the unit of allocation. The effect of variability in practice list size on sample size calculations is demonstrated using the General Medical Services Statistics for England and Wales, 1997. Summary statistics and tables are given to help design such trials assuming that a fixed proportion of patients are to be recruited from each cluster. Three different weightings of the cluster means are compared: uniform, cluster size and minimum variance weights. Minimum variance weights are shown to be superior to uniform, particularly when clusters are small, and to cluster size weights, particularly when clusters are large. Where there are large numbers of participants per cluster and cluster size weights are used, the power actually falls as more patients are recruited to large clusters. When minimum variance weights are used the increase in the design effect due to variation in list size is small, regardless of the size of intracluster correlation coefficient or the number of participants per cluster, provided there is no loss of randomized units. When the expected number of participants per practice is low a greater loss in power comes from practices which fail to recruit patients. A method to estimate the likely effect and allow for it is presented.
OBJECTIVES: Poor postural stability and muscular strength in postmenopausal women are associated with increased risk of falls and fractures. This study examined whether these risk factors for falls differed according to habitual physical activity and menopausal hormone replacement therapy (HRT) use. METHODS: Subjects were 117 postmenopausal women (mean age 65.3+/-6.0 years); of whom 70 had taken HRT for at least 5 years (42 tibolone and 28 transdermal oestradiol), whilst 47 had not received HRT. Duration of physical activity was assessed with monitors worn on a waist belt. Subjects were grouped into low (LPA; < or = 15 min day(-1)) or high (HPA; >15 min day(-1)) physical activity. Postural stability was assessed using a swaymeter which measured displacement at the waist. Maximal isometric strength of knee flexors was determined in 23 of the tibolone group, 26 of the oestrogen group and 12 of the no therapy group. RESULTS: Stature and body mass did not differ according to physical activity participation or HRT use, although the more active women were on average 2.5 years younger than the less active women. Body sway was lower in more physically active women in three of the four measurement conditions (P<0.05) and this effect persisted after inclusion of age as covariate. Body sway tended to be highest in the no therapy group, although not significantly so. Mean knee extensor strength was higher in women taking tibolone and oestrogen than in those not on therapy (115.3 (5.2), 118.2 (7.2) and 97.6 (9.3) Nm, respectively), although again this difference was not statistically significant. CONCLUSIONS: The more physically active postmenopausal women had significantly better postural stability than less active women, whilst HRT had no significant effect. Physical activity might thus have a role in reducing the risk of fracture through reducing the risk of falling.