PMID- 27091355
OWN - NLM
STAT- MEDLINE
DCOM- 20170918
LR  - 20220310
IS  - 1365-2753 (Electronic)
IS  - 1356-1294 (Linking)
VI  - 22
IP  - 6
DP  - 2016 Dec
TI  - Using machine learning to identify structural breaks in single-group interrupted 
      time series designs.
PG  - 851-855
LID - 10.1111/jep.12544 [doi]
AB  - RATIONALE, AIMS AND OBJECTIVES: Single-group interrupted time series analysis 
      (ITSA) is a popular evaluation methodology in which a single unit of observation 
      is being studied, the outcome variable is serially ordered as a time series and 
      the intervention is expected to 'interrupt' the level and/or trend of the time 
      series, subsequent to its introduction. Given that the internal validity of the 
      design rests on the premise that the interruption in the time series is 
      associated with the introduction of the treatment, treatment effects may seem 
      less plausible if a parallel trend already exists in the time series prior to the 
      actual intervention. Thus, sensitivity analyses should focus on detecting 
      structural breaks in the time series before the intervention. METHOD: In this 
      paper, we introduce a machine-learning algorithm called optimal discriminant 
      analysis (ODA) as an approach to determine if structural breaks can be identified 
      in years prior to the initiation of the intervention, using data from 
      California's 1988 voter-initiated Proposition 99 to reduce smoking rates. 
      RESULTS: The ODA analysis indicates that numerous structural breaks occurred 
      prior to the actual initiation of Proposition 99 in 1989, including perfect 
      structural breaks in 1983 and 1985, thereby casting doubt on the validity of 
      treatment effects estimated for the actual intervention when using a single-group 
      ITSA design. CONCLUSIONS: Given the widespread use of ITSA for evaluating 
      observational data and the increasing use of machine-learning techniques in 
      traditional research, we recommend that structural break sensitivity analysis is 
      routinely incorporated in all research using the single-group ITSA design.
CI  - (c) 2016 John Wiley & Sons, Ltd.
FAU - Linden, Ariel
AU  - Linden A
AD  - Linden Consulting Group, LLC, Ann Arbor, MI, USA.
AD  - Division of General Medicine, Medical School, University of Michigan, Ann Arbor, 
      MI, USA.
FAU - Yarnold, Paul R
AU  - Yarnold PR
AD  - Optimal Data Analysis, LLC, Chicago, IL, USA.
LA  - eng
PT  - Journal Article
DEP - 20160419
PL  - England
TA  - J Eval Clin Pract
JT  - Journal of evaluation in clinical practice
JID - 9609066
SB  - IM
MH  - Adult
MH  - Discriminant Analysis
MH  - Female
MH  - Humans
MH  - *Interrupted Time Series Analysis
MH  - *Machine Learning
MH  - Male
MH  - Middle Aged
MH  - Research Design
MH  - Selection Bias
OTO - NOTNLM
OT  - causal inference
OT  - data mining
OT  - interrupted time series analysis
OT  - machine learning
OT  - maximum-accuracy model
OT  - optimal discriminant analysis
OT  - quasi-experimental
OT  - structural breaks
EDAT- 2016/04/20 06:00
MHDA- 2017/09/19 06:00
CRDT- 2016/04/20 06:00
PHST- 2016/03/21 00:00 [received]
PHST- 2016/03/23 00:00 [accepted]
PHST- 2016/04/20 06:00 [pubmed]
PHST- 2017/09/19 06:00 [medline]
PHST- 2016/04/20 06:00 [entrez]
AID - 10.1111/jep.12544 [doi]
PST - ppublish
SO  - J Eval Clin Pract. 2016 Dec;22(6):851-855. doi: 10.1111/jep.12544. Epub 2016 Apr 
      19.