Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited*

By Andrew A. Kramer, Jack E. Zimmerman. Critical Care Medicine, 2007

813 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Emergency Medicine, Epidemiology

View Paper: https://www.semanticscholar.org/paper/444c48e4344bcf3b583389b35a084d7fbfffcf54

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In Cardiology and Cardiovascular Medicine

In Emergency Medicine

In Epidemiology

Citations in the corpus

Development and Evaluation of the Universal ACS NSQIP Surgical Risk Calculator: A Decision Aide and Informed Consent Tool for Patients and Surgeons (2013, methodology): “Second, the Hosmer-Lemeshow (HL) statistic is a measure of calibration that reflects bias in predicting risk across the range of risk.(14) If there is a tendency to over- or underestimate risk for different risk groups, the chi-square statistic will become larger.”
Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension (2013, methodology): “Model calibration was assessed by calculation of the Hosmer–Lemeshow C-statistic (33), and calibration plots are provided as recommended by Kramer et al (34) and can be found in Supplemental Digital Content 2 (http://links.lww.com/CCM/A676).”
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. (2016, methodology): “A major concern with the addition of histologic grade to the radiomics nomogram was that histologic grade was obtained from presurgery biopsy, which may introduce sampling bias compared with surgical tissue, and, hence, was prone to a high false-negative rate.(26) Therefore, the incremental value of”
A tutorial on calibration measurements and calibration models for clinical prediction models (2020, methodology): “To create artificial data, we utilized the method from Zimmerman et al.(14) Twenty-three artificial features were constructed with 20 binary and 3 continuous independent variables.”
The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models (2016, methodology): “Although the Hosmer–Lemeshow test has been criticized in many respects (Kramer and Zimmerman 2007), using this test in a simulation study nicely illustrates that model comparisons based on the predictiveness curve are problematic even when automated calibration checks are carried out beforehand.”
Assessment of Clinical Criteria for Sepsis (2016, neutral): “Black 20 892 (14) 808 (10) 9552 (14) 777 (10) 9755 (15)”
External validation of multivariable prediction models: a systematic review of methodological conduct and reporting (2014, background): “to calibration, preference should be to present a calibration plot, possibly with the calibration slope and intercept in rather than the Hosmer-Lemeshow test, which has a number of known weaknesses related to sample size [48].”
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “In addition, calibration is a useful tool for both discrimination analysis and survival analysis, as it can obtain the agreement between the observed clinical outcomes and model predictions [87].”
Discrimination capacity in species distribution models depends on the representativeness of the environmental domain (2013, background): “However, this statistic has well-known drawbacks (see, for instance, Lemeshow & Hosmer, 1982; Hosmer et al., 1997; Kramer & Zimmerman, 2007) that may discourage its use for assessing calibration.”