Advancements in predicting and modeling rare event outcomes for enhanced decision-making

被引：6

作者：

Feng, Cindy ^{[1
,2
]}

Li, Longhai ^{[3
]}

Xu, Chang ^{[4
,5
]}

机构：

[1] Dalhousie Univ, Fac Med, Dept Community Hlth & Epidemiol, 5790 Univ Ave, Halifax, NS B3H 1V7, Canada

[2] Univ Saskatchewan, Sch Publ Hlth, 104 Clin Pl, Saskatoon, SK S7N2Z4, Canada

[3] Univ Saskatchewan, Dept Math & Stat, 106 Wiggins Rd, Saskatoon, SK S7N5E6, Canada

[4] Anhui Med Univ, Key Lab Populat Hlth Across Life Cycle, Minist Educ, Hefei, Anhui, Peoples R China

[5] Anhui Med Univ, Sch Publ Hlth, Hefei, Anhui, Peoples R China

来源：

BMC MEDICAL RESEARCH METHODOLOGY | 2023年 / 23卷 / 01期

关键词：

LOGISTIC-REGRESSION; PROPENSITY SCORE; NUMBER;

D O I：

10.1186/s12874-023-02060-x

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showcased in this issue aims to provide valuable insights and strategies to enhance the accuracy of rare event prediction and modeling.

引用

页数：3

共 10 条

[1] Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models [J].

Austin, Peter C. ;

Steyerberg, Ewout W. .

STATISTICAL METHODS IN MEDICAL RESEARCH, 2017, 26 (02) :796-808

[2] Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders [J].

Cepeda, MS ;

Boston, R ;

Farrar, JT ;

Strom, BL .

AMERICAN JOURNAL OF EPIDEMIOLOGY, 2003, 158 (03) :280-287

[3] REGRESSION MODELING STRATEGIES FOR IMPROVED PROGNOSTIC PREDICTION [J].

HARRELL, FE ;

LEE, KL ;

CALIFF, RM ;

PRYOR, DB ;

ROSATI, RA .

STATISTICS IN MEDICINE, 1984, 3 (02) :143-152

[4]

James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]

[5] Machine learning: Trends, perspectives, and prospects [J].

Jordan, M. I. ;

Mitchell, T. M. .

SCIENCE, 2015, 349 (6245) :255-260

[6]

King G., 2001, POLIT ANAL, V9, P137, DOI [DOI 10.1093/OXFORDJOURNALS.PAN.A004868, 10.1093/oxfordjournals.pan.a004868]

[7]

Kleber A, 2013, DEVEL SEDIM, V66, P1, DOI 10.1016/B978-0-444-53118-6.00001-5

[8] ZERO-INFLATED POISSON REGRESSION, WITH AN APPLICATION TO DEFECTS IN MANUFACTURING [J].

LAMBERT, D .

TECHNOMETRICS, 1992, 34 (01) :1-14

[9] A simulation study of the number of events per variable in logistic regression analysis [J].

Peduzzi, P ;

Concato, J ;

Kemper, E ;

Holford, TR ;

Feinstein, AR .

JOURNAL OF CLINICAL EPIDEMIOLOGY, 1996, 49 (12) :1373-1379

[10] THE CENTRAL ROLE OF THE PROPENSITY SCORE IN OBSERVATIONAL STUDIES FOR CAUSAL EFFECTS [J].

ROSENBAUM, PR ;

RUBIN, DB .

BIOMETRIKA, 1983, 70 (01) :41-55

← 1 →