Assessment of meaningful change in routine outcome measurement (ROM) with a combination of a longitudinal and a ‘classify and count’ approach
To assess significant changes of health status in people receiving health care, distribution-based and anchor-based methods have been proposed. However, there is no real consensus on what method is the best for evaluating clinically meaningful change. To maximize the internal and external validity of outcome assessment, we propose combining two approaches as recommended by recent practical guidelines on this field. Specifically, we suggest applying longitudinal hierarchical linear models on subgroups of patients showing reliable change and reliable and clinically significant change. This combined approach improved the model’s ability (1) to quantify the magnitude of changes to be reliable and clinically meaningful and (2) to select significant predictors of changes. An empirical application on a prevalence sample of Italian outpatients attending four community mental health services was done. A cross-sectional model and three longitudinal models were applied on the entire study sample and reliable and clinically meaningful change subsamples to investigate the magnitude of change and the predictive effect on outcomes of clinical, socio-demographic and process variables on different patients’ subgroups. Differences were found suggesting that both the statistical method and the sample used to calculate individual changes affect the estimates. The main conclusion is that ignoring the longitudinal data structure or including patients with unreliable change at the follow-up might result in misleading inferences that can alter the real magnitude of changes and the contributions of predictors. The approach proposed provides robust feedback to clinicians on clinically significant change and can be recommended in outcome studies and research. Copyright Springer Science+Business Media Dordrecht 2014
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 48 (2014)
Issue (Month): 5 (September)
|Contact details of provider:|| Web page: http://www.springer.com|
|Order Information:||Web: http://www.springer.com/economics/journal/11135|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:48:y:2014:i:5:p:2479-2499. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Rebekah McClure)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.