Research pearls: The significance of statistics and perils of pooling. Part 2: predictive modeling

Hohmann, Erik, Wetzler, Merrick J. and D'Agostino, Ralph B. (2017) Research pearls: The significance of statistics and perils of pooling. Part 2: predictive modeling. Arthroscopy: The Journal of Arthroscopic and Related Surgery, 33 7: 1423-1432. doi:10.1016/j.arthro.2017.01.054


Author Hohmann, Erik
Wetzler, Merrick J.
D'Agostino, Ralph B.
Title Research pearls: The significance of statistics and perils of pooling. Part 2: predictive modeling
Journal name Arthroscopy: The Journal of Arthroscopic and Related Surgery   Check publisher's open access policy
ISSN 1526-3231
0749-8063
Publication date 2017-07-01
Year available 2017
Sub-type Article (original research)
DOI 10.1016/j.arthro.2017.01.054
Open Access Status Not yet assessed
Volume 33
Issue 7
Start page 1423
End page 1432
Total pages 10
Place of publication Maryland Heights, MO United States
Publisher W.B. Saunders
Language eng
Formatted abstract
The focus of predictive modeling or predictive analytics is to use statistical techniques to predict outcomes and/or the results of an intervention or observation for patients that are conditional on a specific set of measurements taken on the patients prior to the outcomes occurring. Statistical methods to estimate these models include using such techniques as Bayesian methods; data mining methods, such as machine learning; and classical statistical models of regression such as logistic (for binary outcomes), linear (for continuous outcomes), and survival (Cox proportional hazards) for time-to-event outcomes.

A Bayesian approach incorporates a prior estimate that the outcome of interest is true, which is made prior to data collection, and then this prior probability is updated to reflect the information provided by the data. In principle, data mining uses specific algorithms to identify patterns in data sets and allows a researcher to make predictions about outcomes.

Regression models describe the relations between 2 or more variables where the primary difference among methods concerns the form of the outcome variable, whether it is measured as a binary variable (i.e., success/failure), continuous measure (i.e., pain score at 6 months postop), or time to event (i.e., time to surgical revision). The outcome variable is the variable of interest, and the predictor variable(s) are used to predict outcomes. The predictor variable is also referred to as the independent variable and is assumed to be something the researcher can modify in order to see its impact on the outcome (i.e., using one of several possible surgical approaches).

Survival analysis investigates the time until an event occurs. This can be an event such as failure of a medical device or death. It allows the inclusion of censored data, meaning that not all patients need to have the event (i.e., die) prior to the study's completion.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Faculty of Medicine
 
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