Hi Tim The PDF presentation reported 188 GPs not using computers in the analysis.
While I agree in principle that clustering should be considered with this type of modeling the design effect in my experience with this sort of data is limited as the "association analysis" sort of reduces its impact. In some sense you are trying to capitalize on that to show association. However, there are enough statistical packages around that can handle this these days then there were back in the old days (the 90s). In contrast when you are estimating the population prevalence estimates the design effects are obviously greater if you treat the study as a simple random sample rather than a cluster sample. When using the GP as the unit of analysis you are negating clustering as it becomes the number of events over number of trials method for each GP. The problem then becomes that not all GPs will have the event or number of trials because you are relying on 100 encounters only out of 5,000 encounters (on average in a gven year). For example, each GP will have on average 1.9 depression encounters in the 100 encounters - or 2 trials on average, some GPs won't have any in the 100 encounters since it was a happy week... thereby reducing the statistical power even further for particular clinical indicators. General practice is comprised of many many different clinical events that are thinly distributed across a year almost Poisson in nature rather than binomial. This is a problem when one looks at clinical quality indicators particularly at a GP or practice levels hence the statistical power question, generalisability and suitability of analyses is raised. I would suspect the NPCC and NPS programs face similar issues when looking at changes over time for individual feedback vs overall differences as part of the total program evaluation. Geoff -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Tim Churches Sent: Tuesday, 10 July 2007 10:49 a.m. To: General Practice Computing Group Talk Cc: 'General Practice Computing Group Talk' Subject: Re: RE: [GPCG_TALK] BEACH thinks computers don't help GP quality Geoff Sayer <[EMAIL PROTECTED]> wrote: > 3. The statistical power of the study - which would require greater > detail > of the occurrence of events. The 1200 odd GPs (Computers: 1069 vs Non > computers: 188) only provided 100 consecutive encounters. Some of these > quality indicators may not have the power to show an association if it > is a small one. > > Depression occurs at 1.9 per 100 encs (from most recent report) thereby > it is expected that there are 357 depression encounters in the Non-Computer > GP group. You then put several factors into a multivariate analysis the > statistical power is reduced again if there is in fact a real but small > difference between the two groups. >From my reading of the abstract and the actual presentation, my guess is that the analysis was done at the GP level i.e. they fitted a linear regression model to the number (or percentage, since there were 100 patients per GP presumably) of patients for each GP receiving each of the "quality indicators" . Thus there may well be precision and power issues if only 188 of the GPs were not computer users. Hmmm, but hold on - they report "adjusted odds ratios", which suggests that they fitted a logistic model (which is a type of generalised linear regression model and thus more or less consistent with the way the study was reported) - if so, then the model really does need to take account of the cluster sample design - there will be a lot of correlation between patients seen by the same GP and hence the "design effect" is likely to be large, which further reduces the precision of the parameter estimates (and adjusted odds ratios) from the model. It is not obvious that a model wh! i! ch accounted for the sample design was used. Or they could have used multilevel modelling, which is probably even better, but trickier. Would be interesting to know what type of model was fitted to what data, and if sub-optimal, to repeat the analysis using statistical methods which make the most of the collected data while also taking into account the sample design. Tim C _______________________________________________ Gpcg_talk mailing list [email protected] http://ozdocit.org/cgi-bin/mailman/listinfo/gpcg_talk _______________________________________________ Gpcg_talk mailing list [email protected] http://ozdocit.org/cgi-bin/mailman/listinfo/gpcg_talk
