Effect of Variable Selection Strategy on the Performance of Prognostic Models When Using Multiple Imputation

BACKGROUND: Variable selection is an important issue when developing prognostic models. Missing data occur frequently in clinical research. Multiple imputation is increasingly used to address the presence of missing data in clinical research. The effect of different variable selection strategies with multiply imputed data on the external performance of derived prognostic models has not been well examined. METHODS AND RESULTS: We used backward variable selection with 9 different ways to handle multiply imputed data in a derivation sample to develop logistic regression models for predicting death within 1 year of hospitalization with an acute myocardial infarction. We assessed the prognostic accuracy of each derived model in a temporally distinct validation sample. The derivation and validation samples consisted of 11524 patients hospitalized between 1999 and 2001 and 7889 patients hospitalized between 2004 and 2005, respectively. We considered 41 candidate predictor variables. Missing data occurred frequently, with only 13% of patients in the derivation sample and 31% of patients in the validation sample having complete data. Regardless of the significance level for variable selection, the prognostic model developed using only the complete cases in the derivation sample had substantially worse performance in the validation sample than did the models for which variables were selected using the multiply imputed versions of the derivation sample. The other 8 approaches to handling multiply imputed data resulted in prognostic models with performance similar to one another. CONCLUSIONS: Ignoring missing data and using only subjects with complete data can result in the derivation of prognostic models with poor performance. Multiple imputation should be used to account for missing data when developing prognostic models

Improving quality indicator report cards through Bayesian modeling

<p>Abstract</p> <p>Background</p> <p>The National Database for Nursing Quality Indicators^®(NDNQI^®) was established in 1998 to assist hospitals in monitoring indicators of nursing quality (eg, falls and pressure ulcers). Hospitals participating in NDNQI transmit data from nursing units to an NDNQI data repository. Data are summarized and published in reports that allow participating facilities to compare the results for their units with those from other units across the nation. A disadvantage of this reporting scheme is that the sampling variability is not explicit. For example, suppose a small nursing unit that has 2 out of 10 (rate of 20%) patients with pressure ulcers. Should the nursing unit immediately undertake a quality improvement plan because of the rate difference from the national average (7%)?</p> <p>Methods</p> <p>In this paper, we propose approximating 95% credible intervals (CrIs) for unit-level data using statistical models that account for the variability in unit rates for report cards.</p> <p>Results</p> <p>Bayesian CrIs communicate the level of uncertainty of estimates more clearly to decision makers than other significance tests.</p> <p>Conclusion</p> <p>A benefit of this approach is that nursing units would be better able to distinguish problematic or beneficial trends from fluctuations likely due to chance.</p

Benefits of ICU admission in critically ill patients: Whether instrumental variable methods or propensity scores should be used

<p>Abstract</p> <p>Background</p> <p>The assessment of the causal effect of Intensive Care Unit (ICU) admission generally involves usual observational designs and thus requires controlling for confounding variables. Instrumental variable analysis is an econometric technique that allows causal inferences of the effectiveness of some treatments during situations to be made when a randomized trial has not been or cannot be conducted. This technique relies on the existence of one variable or "instrument" that is supposed to achieve similar observations with a different treatment for "arbitrary" reasons, thus inducing substantial variation in the treatment decision with no direct effect on the outcome. The objective of the study was to assess the benefit in terms of hospital mortality of ICU admission in a cohort of patients proposed for ICU admission (ELDICUS cohort).</p> <p>Methods</p> <p>Using this cohort of 8,201 patients triaged for ICU (including 6,752 (82.3%) patients admitted), the benefit of ICU admission was evaluated using 3 different approaches: instrumental variables, standard regression and propensity score matched analyses. We further evaluated the results obtained using different instrumental variable methods that have been proposed for dichotomous outcomes.</p> <p>Results</p> <p>The physician's main specialization was found to be the best instrument. All instrumental variable models adequately reduced baseline imbalances, but failed to show a significant effect of ICU admission on hospital mortality, with confidence intervals far higher than those obtained in standard or propensity-based analyses.</p> <p>Conclusions</p> <p>Instrumental variable methods offer an appealing alternative to handle the selection bias related to nonrandomized designs, especially when the presence of significant unmeasured confounding is suspected. Applied to the ELDICUS database, this analysis failed to show any significant beneficial effect of ICU admission on hospital mortality. This result could be due to the lack of statistical power of these methods.</p

Smoking and health-related quality of life in English general population: Implications for economic evaluations

Copyright @ 2012 Vogl et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.Background: Little is known as to how health-related quality of life (HRQoL) when measured by generic instruments such as EQ-5D differ across smokers, ex-smokers and never-smokers in the general population; whether the overall pattern of this difference remain consistent in each domain of HRQoL; and what implications this variation, if any, would have for economic evaluations of tobacco control interventions. Methods: Using the 2006 round of Health Survey for England data (n = 13,241), this paper aims to examine the impact of smoking status on health-related quality of life in English population. Depending upon the nature of the EQ-5D data (i.e. tariff or domains), linear or logistic regression models were fitted to control for biology, clinical conditions, socio-economic background and lifestyle factors that an individual may have regardless of their smoking status. Age- and gender-specific predicted values according to smoking status are offered as the potential 'utility' values to be used in future economic evaluation models. Results: The observed difference of 0.1100 in EQ-5D scores between never-smokers (0.8839) and heavy-smokers (0.7739) reduced to 0.0516 after adjusting for biological, clinical, lifestyle and socioeconomic conditions. Heavy-smokers, when compared with never-smokers, were significantly more likely to report some/severe problems in all five domains - mobility (67%), self-care (70%), usual activity (42%), pain/discomfort (46%) and anxiety/depression (86%) -. 'Utility' values by age and gender for each category of smoking are provided to be used in the future economic evaluations. Conclusion: Smoking is significantly and negatively associated with health-related quality of life in English general population and the magnitude of this association is determined by the number of cigarettes smoked. The varying degree of this association, captured through instruments such as EQ-5D, may need to be fed into the design of future economic evaluations where the intervention being evaluated affects (e.g. tobacco control) or is affected (e.g. treatment for lung cancer) by individual's (or patients') smoking status

Examining the impact of 11 long-standing health conditions on health-related quality of life using the EQ-5D in a general population sample

Objectives Health-related quality of life (HRQoL) measures have been increasingly used in economic evaluations for policy guidance. We investigate the impact of 11 self-reported long-standing health conditions on HRQoL using the EQ-5D in a UK sample. Methods We used data from 13,955 patients in the South Yorkshire Cohort study collected between 2010 and 2012 containing the EQ-5D, a preference-based measure. Ordinary least squares (OLS), Tobit and two-part regression analyses were undertaken to estimate the impact of 11 long-standing health conditions on HRQoL at the individual level. Results The results varied significantly with the regression models employed. In the OLS and Tobit models, pain had the largest negative impact on HRQoL, followed by depression, osteoarthritis and anxiety/nerves, after controlling for all other conditions and sociodemographic characteristics. The magnitude of coefficients was higher in the Tobit model than in the OLS model. In the two-part model, these four long-standing health conditions were statistically significant, but the magnitude of coefficients decreased significantly compared to that in the OLS and Tobit models and was ranked from pain followed by depression, anxiety/nerves and osteoarthritis. Conclusions Pain, depression, osteoarthritis and anxiety/nerves are associated with the greatest losses of HRQoL in the UK population. The estimates presented in this article should be used to inform economic evaluations when assessing health care interventions, though improvements can be made in terms of diagnostic information and obtaining longitudinal data