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It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. [1] Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element.
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
Retrospective cohort studies exhibit the benefits of cohort studies and have distinct advantages relative to prospective ones: They are conducted on a smaller scale. [5] They typically require less time to complete. [5] They are generally less expensive, because resources are mainly devoted to collecting data. [6]
The prospective study is important for research on the etiology of diseases and disorders. The distinguishing feature of a prospective cohort study is that at the time the investigators begin enrolling subjects and collecting baseline exposure information, none of the subjects have developed any of the outcomes of interest. [2]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
A pooled analysis is a statistical technique for combining the results of multiple epidemiological studies. It is one of three types of literature reviews frequently used in epidemiology, along with meta-analysis and traditional narrative reviews. Pooled analyses may be either retrospective or prospective. [1]
Case–control study versus cohort on a timeline. "OR" stands for "odds ratio" and "RR" stands for "relative risk".In statistics, epidemiology, marketing and demography, a cohort is a group of subjects who share a defining characteristic (typically subjects who experienced a common event in a selected time period, such as birth or graduation).
In medical research, epidemiology, social science, and biology, a cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence study) is a type of observational study that analyzes data from a population, or a representative subset, at a specific point in time—that is, cross-sectional data. [definition needed]