Biomedical subjects

Michael Schemper

Publications and source records attributed to Michael Schemper.

4 recordsLinked to original sources

PubMed2003

Predictive accuracy and explained variation.

Measures of the predictive accuracy of regression models quantify the extent to which covariates determine an individual outcome. Explained variation measures the relative gains in predictive accuracy when prediction based on covariates replaces unconditional prediction. A unified concept of predictive accuracy and explained variation based on the absolute prediction error is presented for models with continuous, binary, polytomous and survival outcomes. The measures are given both in a model-based formulation and in a formulation directly contrasting observed and expected outcomes. Various aspects of application are demonstrated by examples from three forms of regression models. It is emphasized that the likely degree of absolute or relative predictive accuracy often is low even if there are highly significant and relatively strong covariates.

Austria↗

PubMed2003

Comparing the importance of prognostic factors in Cox and logistic regression using SAS.

Two SAS macro programs are presented that evaluate the relative importance of prognostic factors in the proportional hazards regression model and in the logistic regression model. The importance of a prognostic factor is quantified by the proportion of variation in the outcome attributable to this factor. For proportional hazards regression, the program %RELIMPCR uses the recently proposed measure V to calculate the proportion of explained variation (PEV). For the logistic model, the R(2) measure based on squared raw residuals is used by the program %RELIMPLR. Both programs are able to compute marginal and partial PEV, to compare PEVs of factors, of groups of factors, and even to compare PEVs of different models. The programs use a bootstrap resampling scheme to test differences of the PEVs of different factors. Confidence limits for P-values are provided. The programs further allow to base the computation of PEV on models with shrinked or bias-corrected parameter estimates. The SAS macros are freely available at www.akh-wien.ac.at/imc/biometrie/relimp

Data Interpretation, Statistical↗

PubMed2002

A solution to the problem of separation in logistic regression.

The phenomenon of separation or monotone likelihood is observed in the fitting process of a logistic model if the likelihood converges while at least one parameter estimate diverges to +/- infinity. Separation primarily occurs in small samples with several unbalanced and highly predictive risk factors. A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. It produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald tests and confidence intervals are available but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. The clear advantage of the procedure over previous options of analysis is impressively demonstrated by the statistical analysis of two cancer studies.

Breast Neoplasms↗

PubMed2002

Endovascular stent grafting versus open surgical operation in patients with infrarenal aortic aneurysms: a propensity score-adjusted analysis.

BACKGROUND: Although transfemoral endovascular aneurysm management (TEAM) of infrarenal abdominal aortic aneurysms (AAA) is widely performed, open graft replacement is still considered the standard of care. The aim of this study was to investigate whether clear indications for TEAM can be established in patients with significant comorbidities without investigating differences in relative procedure efficacy or durability. METHODS AND RESULTS: A propensity score-based analysis of 454 consecutive patients treated electively for AAA from January 1995 through December 2000 was performed. Of those 454 patients, 248 received open surgery and 206 received TEAM. In-hospital mortality rates (MRs) were compared. After adjusting for propensity scores, a Cox proportional hazard model (COX) was employed to test the influence of the respective treatment on postoperative 900-day survival estimates (SEs). Several potential preoperative risk factors were used as covariates. The MR of all patients was 3.7%. Explorative analysis demonstrated that patients treated by TEAM presented with significantly more risk factors. In American Society of Anesthesiologists class IV patients, a significant difference in MR was detected (4.7% for TEAM versus 19.2% for open surgery; P<0.02). After adjusting for the propensity to receive TEAM or open surgery, a regression analysis of survival based on COX revealed predictive influences of impaired kidney (P<0.047) or pulmonary function (P<0.001), increased age (P<0.05), and selection of treatment modality (P<0.002) on SE. CONCLUSIONS: TEAM represents a less invasive procedure for AAA therapy in patients with significant preoperative risk factors. Especially in geriatric patients with multiple morbidities, TEAM offers a method of therapy with acceptable MRs and SEs, making active treatment possible in otherwise incurable patients.

Adult↗