Dataset Information

The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models.

ABSTRACT:

Background

Many measures of prediction accuracy have been developed. However, the most popular ones in typical medical outcome prediction settings require additional investigation of calibration.

Methods

We show how rescaling the Brier score produces a measure that combines discrimination and calibration in one value and improves interpretability by adjusting for a benchmark model. We have called this measure the index of prediction accuracy (IPA). The IPA permits a common interpretation across binary, time to event, and competing risk outcomes. We illustrate this measure using example datasets.

Results

The IPA is simple to compute, and example code is provided. The values of the IPA appear very interpretable.

Conclusions

IPA should be a prominent measure reported in studies of medical prediction model performance. However, IPA is only a measure of average performance and, by default, does not measure the utility of a medical decision.

SUBMITTER: Kattan MW

PROVIDER: S-EPMC6460739 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models.

Kattan Michael W MW Gerds Thomas A TA

Diagnostic and prognostic research 20180504

<h4>Background</h4>Many measures of prediction accuracy have been developed. However, the most popular ones in typical medical outcome prediction settings require additional investigation of calibration.<h4>Methods</h4>We show how rescaling the Brier score produces a measure that combines discrimination and calibration in one value and improves interpretability by adjusting for a benchmark model. We have called this measure the index of prediction accuracy (IPA). The IPA permits a common interpr ...[more]

PMID: 31093557

Similar Datasets

Project description:BACKGROUND:The ability to predict risk allows healthcare providers to propose which patients might benefit most from certain therapies, and is relevant to payers' demands to justify clinical and economic value. To understand the robustness of risk prediction models for heart failure (HF), we conducted a systematic literature review to (1) identify HF risk-prediction models, (2) assess statistical approach and extent of validation, (3) identify common variables, and (4) assess risk of bias (ROB). METHODS:Literature databases were searched from March 2013 to May 2018 to identify risk prediction models conducted in an out-of-hospital setting in adults with HF. Distinct risk prediction variables were ranked according to outcomes assessed and incorporation into the studies. ROB was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS:Of 4720 non-duplicated citations, 40 risk-prediction publications were deemed relevant. Within the 40 publications, 58 models assessed 55 (co)primary outcomes, including all-cause mortality (n = 17), cardiovascular death (n = 9), HF hospitalizations (n = 15), and composite endpoints (n = 14). Few publications reported detail on handling missing data (n = 11; 28%). The discriminatory ability for predicting all-cause mortality, cardiovascular death, and composite endpoints was generally better than for HF hospitalization. 105 distinct predictor variables were identified. Predictors included in >5 publications were: N-terminal prohormone brain-natriuretic peptide, creatinine, blood urea nitrogen, systolic blood pressure, sodium, NYHA class, left ventricular ejection fraction, heart rate, and characteristics including male sex, diabetes, age, and BMI. Only 11/58 (19%) models had overall low ROB, based on our application of PROBAST. In total, 26/58 (45%) models discussed internal validation, and 14/58 (24%) external validation. CONCLUSIONS:The majority of the 58 identified risk-prediction models for HF present particular concerns according to ROB assessment, mainly due to lack of validation and calibration. The potential utility of novel approaches such as machine learning tools is yet to be determined. REGISTRATION NUMBER:The SLR was registered in Prospero (ID: CRD42018100709).

Dataset Information

The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models.

Background

Methods

Results

Conclusions

Publications

The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets