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In this case, the "before" and "after" data sets are paired, as each patient has a "before" measurement and an "after" measurement, that are likely related. In contrast, another clinical trial might measure n patients before treatment and a different set of m patients after treatment; in that case, the "before" and "after" data are unpaired.
Interrupted time series analysis (ITS), sometimes known as quasi-experimental time series analysis, is a method of statistical analysis involving tracking a long-term period before and after a point of intervention to assess the intervention's effects.
Power analysis can either be done before (a priori or prospective power analysis) or after (post hoc or retrospective power analysis) data are collected. A priori power analysis is conducted prior to the research study, and is typically used in estimating sufficient sample sizes to achieve adequate power.
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."
In a scientific study, post hoc analysis (from Latin post hoc, "after this") consists of statistical analyses that were specified after the data were seen. [1] [2] They are usually used to uncover specific differences between three or more group means when an analysis of variance (ANOVA) test is significant. [3]
Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3]
In clinical trials and other scientific studies, an interim analysis is an analysis of data that is conducted before data collection has been completed. Clinical trials are unusual in that enrollment of subjects is a continual process staggered in time.
Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, [1] and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values , amongst other issues.