The Experts below are selected from a list of 78 Experts worldwide ranked by ideXlab platform
Merril L. Knudtson - One of the best experts on this subject based on the ideXlab platform.
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multiple imputation versus data enhancement for dealing with missing data in observational Health Care Outcome analyses
Journal of Clinical Epidemiology, 2002Co-Authors: Peter Faris, Colleen M. Norris, William A. Ghali, Rollin Brant, Diane P Galbraith, Merril L. KnudtsonAbstract:Abstract The problem of missing data is frequently encountered in observational studies. We compared approaches to dealing with missing data. Three multiple imputation methods were compared with a method of enhancing a clinical database through merging with administrative data. The clinical database used for comparison contained information collected from 6,065 cardiac Care patients in 1995 in the province of Alberta, Canada. The effectiveness of the different strategies was evaluated using measures of discrimination and goodness of fit for the 1995 data. The strategies were further evaluated by examining how well the models predicted Outcomes in data collected from patients in 1996. In general, the different methods produced similar results, with one of the multiple imputation methods demonstrating a slight advantage. It is concluded that the choice of missing data strategy should be guided by statistical expertise and data resources.
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Dealing with missing data in observational Health Care Outcome analyses.
Journal of clinical epidemiology, 2000Co-Authors: Colleen M. Norris, William A. Ghali, Merril L. Knudtson, C. David Naylor, L. Duncan SaundersAbstract:Abstract Observational Outcome analyses appear frequently in the Health research literature. For such analyses, clinical registries are preferred to administrative databases. Missing data are a common problem in any clinical registry, and pose a threat to the validity of observational Outcomes analyses. Faced with missing data in a new clinical registry, we compared three possible responses: exclude cases with missing data; assume that the missing data indicated absence of risk; or merge the clinical database with an existing administrative database. The predictive model derived using the merged data showed a higher C statistic ( C = 0.770), better model goodness-of-fit as measured in a decile-of-risk analysis, the largest gradient of risk across deciles (46.3), and the largest decrease in deviance (−2 log likelihood=406.2). The superior performance of the enhanced data model supports the use of this "enhancement" methodology and bears consideration when researchers are faced with nonrandom missing data.
Colleen M. Norris - One of the best experts on this subject based on the ideXlab platform.
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multiple imputation versus data enhancement for dealing with missing data in observational Health Care Outcome analyses
Journal of Clinical Epidemiology, 2002Co-Authors: Peter Faris, Colleen M. Norris, William A. Ghali, Rollin Brant, Diane P Galbraith, Merril L. KnudtsonAbstract:Abstract The problem of missing data is frequently encountered in observational studies. We compared approaches to dealing with missing data. Three multiple imputation methods were compared with a method of enhancing a clinical database through merging with administrative data. The clinical database used for comparison contained information collected from 6,065 cardiac Care patients in 1995 in the province of Alberta, Canada. The effectiveness of the different strategies was evaluated using measures of discrimination and goodness of fit for the 1995 data. The strategies were further evaluated by examining how well the models predicted Outcomes in data collected from patients in 1996. In general, the different methods produced similar results, with one of the multiple imputation methods demonstrating a slight advantage. It is concluded that the choice of missing data strategy should be guided by statistical expertise and data resources.
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Dealing with missing data in observational Health Care Outcome analyses.
Journal of clinical epidemiology, 2000Co-Authors: Colleen M. Norris, William A. Ghali, Merril L. Knudtson, C. David Naylor, L. Duncan SaundersAbstract:Abstract Observational Outcome analyses appear frequently in the Health research literature. For such analyses, clinical registries are preferred to administrative databases. Missing data are a common problem in any clinical registry, and pose a threat to the validity of observational Outcomes analyses. Faced with missing data in a new clinical registry, we compared three possible responses: exclude cases with missing data; assume that the missing data indicated absence of risk; or merge the clinical database with an existing administrative database. The predictive model derived using the merged data showed a higher C statistic ( C = 0.770), better model goodness-of-fit as measured in a decile-of-risk analysis, the largest gradient of risk across deciles (46.3), and the largest decrease in deviance (−2 log likelihood=406.2). The superior performance of the enhanced data model supports the use of this "enhancement" methodology and bears consideration when researchers are faced with nonrandom missing data.
L. Duncan Saunders - One of the best experts on this subject based on the ideXlab platform.
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Dealing with missing data in observational Health Care Outcome analyses.
Journal of clinical epidemiology, 2000Co-Authors: Colleen M. Norris, William A. Ghali, Merril L. Knudtson, C. David Naylor, L. Duncan SaundersAbstract:Abstract Observational Outcome analyses appear frequently in the Health research literature. For such analyses, clinical registries are preferred to administrative databases. Missing data are a common problem in any clinical registry, and pose a threat to the validity of observational Outcomes analyses. Faced with missing data in a new clinical registry, we compared three possible responses: exclude cases with missing data; assume that the missing data indicated absence of risk; or merge the clinical database with an existing administrative database. The predictive model derived using the merged data showed a higher C statistic ( C = 0.770), better model goodness-of-fit as measured in a decile-of-risk analysis, the largest gradient of risk across deciles (46.3), and the largest decrease in deviance (−2 log likelihood=406.2). The superior performance of the enhanced data model supports the use of this "enhancement" methodology and bears consideration when researchers are faced with nonrandom missing data.
William A. Ghali - One of the best experts on this subject based on the ideXlab platform.
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multiple imputation versus data enhancement for dealing with missing data in observational Health Care Outcome analyses
Journal of Clinical Epidemiology, 2002Co-Authors: Peter Faris, Colleen M. Norris, William A. Ghali, Rollin Brant, Diane P Galbraith, Merril L. KnudtsonAbstract:Abstract The problem of missing data is frequently encountered in observational studies. We compared approaches to dealing with missing data. Three multiple imputation methods were compared with a method of enhancing a clinical database through merging with administrative data. The clinical database used for comparison contained information collected from 6,065 cardiac Care patients in 1995 in the province of Alberta, Canada. The effectiveness of the different strategies was evaluated using measures of discrimination and goodness of fit for the 1995 data. The strategies were further evaluated by examining how well the models predicted Outcomes in data collected from patients in 1996. In general, the different methods produced similar results, with one of the multiple imputation methods demonstrating a slight advantage. It is concluded that the choice of missing data strategy should be guided by statistical expertise and data resources.
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Dealing with missing data in observational Health Care Outcome analyses.
Journal of clinical epidemiology, 2000Co-Authors: Colleen M. Norris, William A. Ghali, Merril L. Knudtson, C. David Naylor, L. Duncan SaundersAbstract:Abstract Observational Outcome analyses appear frequently in the Health research literature. For such analyses, clinical registries are preferred to administrative databases. Missing data are a common problem in any clinical registry, and pose a threat to the validity of observational Outcomes analyses. Faced with missing data in a new clinical registry, we compared three possible responses: exclude cases with missing data; assume that the missing data indicated absence of risk; or merge the clinical database with an existing administrative database. The predictive model derived using the merged data showed a higher C statistic ( C = 0.770), better model goodness-of-fit as measured in a decile-of-risk analysis, the largest gradient of risk across deciles (46.3), and the largest decrease in deviance (−2 log likelihood=406.2). The superior performance of the enhanced data model supports the use of this "enhancement" methodology and bears consideration when researchers are faced with nonrandom missing data.
Kelly Morris - One of the best experts on this subject based on the ideXlab platform.
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patient satisfaction with Care for urgent Health problems a survey of family practice patients
Annals of Family Medicine, 2007Co-Authors: Michelle Howard, James Goertzen, Brian Hutchison, Janusz Kaczorowski, Kelly MorrisAbstract:PURPOSE Patient satisfaction is an important Health Care Outcome. This study compared patients' satisfaction with Care received for an urgent Health prob- lem from their family physician, at an after-hours clinic in which their physician participated, at a walk-in clinic, at the emergency department, from telephone Health advisory services, or from more than 1 of those services. METHODS We mailed a questionnaire to a random sample of patients from 36 family practices in Thunder Bay, Ontario. We elicited satisfaction with Care for the most recent urgent Health problem in the past 6 months on a 7-point scale (very dissatisfi ed to very satisfi ed). RESULTS The response rate was 62.3% (5,884 of 9,397). Of the 5,722 eligible patients 1,342 (23.4%) reported an urgent Health problem, and data were avail- able for both services used and satisfaction for 1,227 patients. After adjusting for sociodemographic characteristics and self-reported Health status, satisfaction with Care received for most recent urgent Health problem was signifi cantly higher among patients who visited or spoke to their family physician (mean 6.1; 95% confi dence interval (CI), 5.8-6.4) compared with all other services (all P <.004, adjusted for multiple comparisons), with the exception of patients who used the after-hours clinic affi liated with their physician, whose satisfaction was not signifi - cantly different (mean 5.6; 95% CI, 5.2-6.0). CONCLUSIONS Satisfaction was highest for patients receiving Care from their own family physician or their physician's after-hours clinic. These results are important for new primary Care models that emphasize continuity and after-hours availabil- ity of family physicians.
