Curso intensivo de regresion logistica

Núm al catàleg de Patrimoni: 3887 (institut Mental i Església)Bernader, J. O

A Computer Simulation of the EPI Survey Strategy

Lemeshow S (Division of Public Health, School of Health Sciences, University of Massachusetts, Amherst MA 01003, USA), Tserkovnyi A G, Tulloch J L, Dowd J E, Lwanga S K and Keja J. A computer simulation of the EPI survey strategy. Internationa Journal of Epidemiology 1985, 14: 473-481. A Monte Carlo simulation study was designed to evaluate the sample survey technique currently used by the Expanded Programme on Immunization(EPI) of the World Health Organization. Of particular interest was how the EPI strategy compared to a more traditional sampling strategy with respect to bias and variability of estimates. It was also of interest to investigate whether the estimates of population vaccination coverage were accurate to within 10 percentage points of the actual levels. It was found that within particular clusters, the EPI method was particularly sensitive to pocketing of vaccinated individuals, but the more traditional method gave more accurate and less variable results under a variety of conditions. However, the stated goal of the EPI, of being able to produce population estimates accurate to within 10 percentage points of the true levels in the population, was satisfied in the artificially created populations studie

Diagnosis-Specific Readmission Risk Prediction Using Electronic Health Data: a Retrospective Cohort Study

Background: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. Methods: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis. The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. Results: 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). Conclusions: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged

Understanding of interaction (subgroup) analysis in clinical trials

Background: When the treatment effect on the outcome of interest is influenced by a baseline/demographic factor, investigators say that an interaction is present. In randomized clinical trials (RCTs), this type of analysis is typically referred to as subgroup analysis. Although interaction (or subgroup) analyses are usually stated as a secondary study objective, it is not uncommon that these results lead to changes in treatment protocols or even modify public health policies. Nonetheless, recent reviews have indicated that their proper assessment, interpretation and reporting remain challenging. Results: Therefore, this article provides an overview of these challenges, to help investigators find the best strategy for application of interaction analyses on binary outcomes in RCTs. Specifically, we discuss the key points of formal interaction testing, including the estimation of both additive and multiplicative interaction effects. We also provide recommendations that, if adhered to, could increase the clarity and the completeness of reports of RCTs. Conclusion: Altogether, this article provides a brief non-statistical guide for clinical investigators on how to perform, interpret and report interaction (subgroup) analyses in RCTs

Cancer Screening Practices Among Amish and Non-Amish Adults Living in Ohio Appalachia

The Amish, a unique community living in Ohio Appalachia, have lower cancer incidence rates than non-Amish living in Ohio Appalachia. The purpose of this study was to examine cancer screening rates among Amish compared to non-Amish adults living in Ohio Appalachia and a national sample of adults of the same race and ethnicity in an effort to explain cancer patterns

Results from the national sepsis practice survey: predictions about mortality and morbidity and recommendations for limitation of care orders

Introduction: Critically ill patients and families rely upon physicians to provide estimates of prognosis and recommendations for care. Little is known about patient and clinician factors which influence these predictions. The association between these predictions and recommendations for continued aggressive care is also understudied. Methods: We administered a mail-based survey with simulated clinical vignettes to a random sample of the Critical Care Assembly of the American Thoracic Society. Vignettes represented a patient with septic shock with multi-organ failure with identical APACHE II scores and sepsis-associated organ failures. Vignettes varied by age (50 or 70 years old), body mass index (BMI) (normal or obese) and co-morbidities (none or recently diagnosed stage IIA lung cancer). All subjects received the vignettes with the highest and lowest mortality predictions from pilot testing and two additional, randomly selected vignettes. Respondents estimated outcomes and selected care for each hypothetical patient. Results: Despite identical severity of illness, the range of estimates for hospital mortality (5th to 95th percentile range, 17% to 78%) and for problems with self-care (5th to 95th percentile range, 2% to 74%) was wide. Similar variation was observed when clinical factors (age, BMI, and co-morbidities) were identical. Estimates of hospital mortality and problems with self-care among survivors were significantly higher in vignettes with obese BMIs (4.3% and 5.3% higher, respectively), older age (8.2% and 11.6% higher, respectively), and cancer diagnosis (5.9% and 6.9% higher, respectively). Higher estimates of mortality (adjusted odds ratio 1.29 per 10% increase in predicted mortality), perceived problems with self-care (adjusted odds ratio 1.26 per 10% increase in predicted problems with self-care), and early-stage lung cancer (adjusted odds ratio 5.82) were independently associated with recommendations to limit care. Conclusions: The studied clinical factors were consistently associated with poorer outcome predictions but did not explain the variation in prognoses offered by experienced physicians. These observations raise concern that provided information and the resulting decisions about continued aggressive care may be influenced by individual physician perception. To provide more reliable and accurate estimates of outcomes, tools are needed which incorporate patient characteristics and preferences with physician predictions and practices