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Interrupted Time Series Analysis in Drug Utilization Research is Increasing: Systematic Review and Recommendations
Racquel Jandoc, Andrea M. Burden, Muhammad Mamdani, Linda E. Levesque, Suzanne M. Cadarette
PII: S0895-4356(15)00123-7
DOI: 10.1016/j.jclinepi.2014.12.018
Reference: JCE 8839
To appear in: Journal of Clinical Epidemiology
Received Date: 16 April 2014 Revised Date: 28 November 2014 Accepted Date: 24 December 2014
Please cite this article as: Jandoc R, Burden AM, Mamdani M, Levesque LE, Cadarette SM, Interrupted Time Series Analysis in Drug Utilization Research is Increasing: Systematic Review and Recommendations, Journal of Clinical Epidemiology (2015), doi: 10.1016/j.jclinepi.2014.12.018.
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1 Interrupted Time Series Analysis in Drug Utilization Research is Increasing: Systematic
2 Review and Recommendations
3 Racquel Jandoc1, Andrea M. Burden1, Muhammad Mamdani1'2'3, Linda E. Lévesque4, Suzanne
4 M. Cadarette1*
5 1 Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto ON, Canada
6 2 Applied Health Research Center, La Ka Shing Knowledge Institute, St. Michael's Hospital,
7 Toronto ON, Canada
8 3 Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto ON,
9 Canada
10 4 Department of Public Health Sciences, Queen's University, Kingston ON, Canada
14 Correspondence to: Suzanne M. Cadarette, Leslie L. Dan Pharmacy Building, University of
15 Toronto, 144 College Street, Toronto, Ontario, M5S 3M2 Canada. Tel: 416-978-2993, Fax: 41616 978-8511, E-mail: s.cadarette@utoronto.ca
1 Support and Financial Disclosure Declaration
3 Conflicts of interest:
4 Racquel Jandoc - none
5 Andrea M. Burden - none
6 Muhammad Mamdani - none related to this work
7 Linda E. Levesque - none
8 Suzanne M. Cadarette - none
10 Financial support:
11 Project support - Ontario Ministry of Research and Innovation (ER09-06-043)
12 Racquel Jandoc - CIHR Training Program in Bridging Scientific Domains for Drug Safety and
13 Effectiveness
14 Suzanne M. Cadarette - CIHR New Investigator Award (MSH-95364)
1 Abstract
2 Objective: To describe the use and reporting of interrupted time series methods in drug
3 utilization research.
4 Study Design and Setting: We completed a systematic search of MEDLINE®, Web of
5 Science®, and reference lists to identify English-language articles through to December 2013 that
6 employed interrupted time series methods in drug utilization research. We tabulated the number
7 of studies by publication year, and summarized methodological detail.
8 Results: We identified 220 eligible empirical applications since 1984. Only 17 (8%) were
9 published before 2000, and 90 (41%) were published since 2010. Segmented regression was the
10 most commonly applied interrupted time series method (67%). Most studies assessed drug policy
11 changes (51%, n=112); 22% (n=48) examined the impact of new evidence, 18% (n=39)
12 examined safety advisories, and 16% (n=35) examined quality improvement interventions.
13 Autocorrelation was considered in 66% of studies, 31% reported adjusting for seasonality, and
14 15% accounted for non-stationarity.
15 Conclusion: Use of interrupted time series methods in drug utilization research has increased,
16 particularly in recent years. Despite methodological recommendations, there is large variation in
17 reporting of analytic methods. Developing methodological and reporting standards for
18 interrupted time series analysis is important to improve its application in drug utilization
19 research, and we provide recommendations for consideration.
20 Key Words: ARIMA, drug utilization, pharmacoepidemiology, review, segmented regression,
21 time series
22 Running title: Systematic review of interrupted time series methods
23 Word counts: Abstract - 199, Main text - 2887 (including acknowledgements)
What is new?
• There has been an increase in the application of interrupted time series analysis in drug utilization research, particularly in recent years.
• We identified large variation in methodological considerations reported in empirical applications.
• Developing methodological and reporting standards for interrupted time series analysis is important to improve its application in drug utilization research, and we provide recommendations for consideration, Table 2.
2 1. Introduction
3 Interrupted time series analysis is the strongest and most commonly used quasi-experimental
4 design to assess the impact of an intervention when a randomized controlled trial is not feasible
5 [1-6]. This method has been applied in a variety of disciplines, and was first introduced to the
6 field of health services research in 1981 to evaluate the impact of regionalized perinatal care [7].
7 Interrupted time series methods use aggregate data collected over equally spaced time intervals
8 before and after an intervention, with the key assumption that data trends prior to the intervention
9 can be extrapolated to predict trends had the intervention not occurred [3]. Routinely maintained
10 pharmacy and medical databases provide rich data sources to apply interrupted time series
11 methods [3].
12 Several methodological issues need to be considered when completing an interrupted
13 time series analysis. First, given the serial nature of the design; autocorrelation, non-stationarity,
14 and seasonality need to be considered [3, 8]. Autocorrelation refers to the serial dependence of
15 outcome measure error terms. For example, prescription patterns closer to each other may be
16 more similar than those further apart [3, 5, 6]. The presence of autocorrelation can be assessed
1 using the Ljung-Box chi-square statistic [9] or Durbin-Watson statistic [3, 5, 6, 10], and
2 corrected for if necessary. Non-stationary data exhibit an underlying trend that is unrelated to the
3 intervention. For example, the use of a drug commonly increases once it enters the market [8].
4 Non-stationary can be tested using the augmented Dickey-Fuller test [11]. Seasonality represents
5 regular seasonal fluctuations in the outcome, for example, use of medications to treat influenza.
6 When present, terms for seasonality (e.g. months) should be included in the model [3, 6]. Failing
7 to account for autocorrelation, non-stationarity, and seasonality may lead to biased results.
8 The number of data points available for analysis, and the number of observations within
9 each data point, are important when using interrupted time series analysis. Although there is no
10 gold standard, it is generally agreed that more data points and observations are better. Depending
11 on the minimum effect size and the amount of variation, a minimum of nine data points pre-,
12 post-, and when applicable, between interventions [4, 12]; and at least 100 observations per data
13 point is encouraged [3]. A larger number of observations in each data point provides more stable
14 estimates and thus reduces the variability and outliers within a time series analysis. Data point
15 outliers that are explainable, such as a sudden peak in drug dispensing in anticipation of a drug
16 restriction policy, can be controlled for using an indicator term [3]. Outliers that result from
17 random variation can be treated as regular data points [3]. A larger number of data points also
18 permits more stable estimates for forecasting pre-intervention trends had the intervention not
19 occurred. In general however, caution should be used when forecasting beyond the data points
20 observed in interrupted time series analyses. Another caveat when conducting interrupted time
21 series analysis relates to possible outcome measure ceiling or floor effects. For example, when
22 studying the impact of an intervention in improving the proportion of patients treated with a
23 drug, the outcome has a natural ceiling of 100%, and thus depending on the initial level of
1 measurement, minimal change in the outcome may be observed [13]. Authors must consider
2 ceiling and floor effects when designing their study and interpreting results.
3 A clear intervention time point helps to identify pre- and post-intervention data points,
4 yet if intervention effects are gradual or delayed, then a lag period may be considered [3].
5 Lagged intervention effects can be accounted for by excluding the lag period from the analysis,
6 modeling the lag period as a separate segment in the time series [3], or using a ramp function in
7 autoregressive integrated moving average (ARIMA) models [14]. Here, graphical figures
8 displaying the results of interrupted time series analysis are particularly useful. Even without
9 statistical output, figures allow readers to visually examine baseline trends, the time point at
10 which the intervention occurred, and the impact of the intervention [3, 5]. All interrupted time
11 series studies should therefore include graphical display to facilitate interpretation of study
12 results.
13 The main threat to validity in interrupted time series analysis relates to time-varying
14 confounding, such as changes in outcome coding, co-interventions, or changes in the population
15 under study [3-5, 15]. These threats need to be considered at the individual study level and
16 require intimate knowledge of the data and healthcare utilization trends. The use of a comparison
17 outcome in the same population, or a comparison group using the same outcome in a group not
18 exposed to the intervention, helps to alleviate concerns related to time-varying confounding [3,
19 5]. Indeed, an advantage of interrupted time series analysis is the ease in stratifying results by
20 different groups [5].
21 Interrupted time series analysis has been applied in a variety of disciplines, however its use
22 to study the impact of healthcare interventions on drug utilization has not been well described.
1 The purpose of our study was to describe the use and reporting of interrupted time series
2 methods in drug utilization research.
3 2. Methods
4 We completed a systematic MEDLINE® keyword and Web of Science® citation search to
5 identify all English-language articles that employed interrupted time series methods to study
6 drug utilization in humans. Empirical applications that examined the impact of interventions at
7 the population level, including: drug policy changes, new evidence in the form of guideline
8 changes or major publications, quality improvement interventions, and government or media
9 safety advisories; on prescription drug utilization were eligible. We defined drug utilization as
10 the number or proportion of: drug(s) dispensed, patients dispensed a drug, or patients meeting an
11 adherence target. Systematic reviews, methodological contributions, letters to the editor, and
12 conference abstracts were excluded since the focus was on use and reporting of empirical
13 applications. We also excluded single institution studies so we could focus on population-based
14 interventions that may be more generalizable.
15 We first searched MEDLINE from inception (1946) to December 2013 with keyword
16 terms related to time series analysis and drug utilization (Appendix A). We then used Web of
17 Science® to perform a citation search of methodological papers identified in the keyword search
18 [3, 5, 16], and a commonly cited paper [7]. Finally, we manually searched reference lists from all
19 methodological contributions [B.1-10], review papers [B.11-14], and eligible empirical
20 applications [B.15-204] identified in the keyword and citation searches to identify additional
21 empirical applications. Two authors (RJ and AMB) independently completed each search and
22 reviewed articles for eligibility. Discrepancies were resolved through discussion with a third
23 author (SMC). A proportional Venn diagram was created to illustrate the number of empirical
1 applications identified by each search strategy. The number of empirical applications was then
2 plotted by publication year.
3 We abstracted the following characteristics for each application: intervention(s) of interest,
4 primary data source, and methodological detail (time intervals, outcome measure, interrupted
5 time series methods used, and methodological considerations reported). As described above,
6 there are several methodological considerations in interrupted time series analysis, and we have
7 taken care to abstract whether authors reported these; however, we were unable to evaluate
8 aspects that would require access to each study's raw data. Thus methodological considerations
9 abstracted included reporting of: autocorrelation, non-stationarity, and seasonality; use of a
10 comparison group; clearly defined time points; number of pre- and post-intervention points;
11 outliers; forecasting; and absolute and/or relative changes with confidence intervals or standard
12 errors. Additional considerations abstracted included the use of lag periods, sensitivity analysis,
13 and graphical figures to display results. One author (RJ) abstracted all data and a second author
14 (AMB) verified all abstracted data. All methodological considerations were summarized using
15 descriptive statistics.
17 3. Results
18 Of 1917 unique articles identified, 10 were methodological contributions [B.1-10], 4 were
19 review papers [B.11-14], and 220 were eligible empirical applications [B.15-234] (Fig. 1,
20 Appendix B).
21 *Insert Fig. 1*
22 Each search strategy proved important, with 52 (24%) empirical applications identified
23 solely by the keyword search, 33 (15%) identified solely by the citation search, 30 (14%)
24 identified solely by the reference list search; and only 35 (16%) identified by all three search
1 strategies (Fig. 2). Most segmented regression papers (92 of 133, 69%) were identified by the
2 citation search, whereas most ARIMA papers (26 of 30, 84%) were identified by the keyword
3 search. One eligible paper that did not appear in our original search was identified by a reviewer
4 during the peer-review process.
5 *InsertFig. 2*
6 The first empirical application was published in 1984, yet relatively few (n=17, 8%) were
7 published before the year 2000 (Fig. 3). Since 2000, use has increased with an average of 15
8 (SD=8.2) applications published per year. Forty-one percent (n=90) were published in the last
9 four years, with a high of 31 articles published in 2013.
10 *Insert Fig. 3*
11 Table 1 summarizes the characteristics of the 220 empirical applications identified, of
12 which 92% utilized administrative pharmacy databases. Policy changes were the most common
13 interventions evaluated (51%), followed by new evidence (22%), safety advisories (18%), and
14 quality improvement interventions (16%). Seventy-one percent examined prescriptions
15 dispensed (22% number, 35%, proportion and 14% standard dose) as the primary outcome
16 measure, and 29% used the number or proportion of patients dispensed the drug of interest or
17 meeting an adherence target. Most applications examined drug utilization over monthly (76%) or
18 quarterly (14%) intervals. Of the 200 papers (91%) reporting detailed methods, segmented
19 regression (67%), ARIMA models (16%), and linear regression (11%) were the most commonly
20 applied analyses. Other analytical methods included generalized estimating equations, logistic,
21 nonlinear, and Poisson models. Fifty percent (n=67) of papers using segmented regression
22 applied a linear model.
1 Of all empirical studies, 146 (66%) reported testing for autocorrelation (77% of ARIMA
2 papers, and 73% of segmented regression papers), 68 (31%) reported adjusting for seasonality
3 (52% of ARIMA, and 29% of segmented regression), and 32 (15%) reported testing for non-
4 stationarity (65% of ARIMA, 9% of segmented regression). One-third (35%) of all empirical
5 studies reported the use of a comparison group, 70% reported absolute and/or relative impacts
6 with confidence intervals or standard errors, and 28% reported including lag periods in their
7 models. Most articles (85%) clearly reported the intervention time point(s) of interest, and 84%
8 of studies included a graph, yet only 39% reported the number of pre- and post-intervention data
9 points included in their analysis (range: 3 to 72 data points). One-fifth (21%) of applications
10 conducted a sensitivity analysis.
11 *Insert Table 1*
13 4. Discussion
14 We examined the application and reporting of interrupted time series analysis methods in drug
15 utilization research. Use of interrupted time series analysis has increased since the year 2000, a
16 finding noted in other recent reviews of innovative methods in pharmacoepidemiology [17, 18].
17 The most common interrupted time series methods were segmented regression (67%), ARIMA
18 models (16%), and linear regression (11%). When executing time series models, several
19 methodological aspects are important and may impact the validity of the model. We identified
20 that the majority of eligible articles employing ARIMA models addressed autocorrelation (77%),
21 non-stationarity (65%), seasonality (52%). Since ARIMA models inherently account for
22 autocorrelation, non-stationarity, and seasonality [19]; it is possible that authors may have
23 chosen not to report these considerations. In contrast, segmented regression models do not
24 intrinsically account for autocorrelation, non-stationarity, and seasonality; and thus it is
1 imperative to consider each [3]. However, only 73% of segmented regression studies reported
2 testing for autocorrelation, 29% reported adjustment for seasonality, and only 9% reported
3 consideration of non-stationarity.
4 Over 80% of interrupted time series analyses cited a clearly defined intervention time
5 point, and used figures to graphically display results; however, other methodological issues were
6 poorly reported. Explicit reporting of all methodological considerations may improve awareness
7 of their importance as well as the interpretation of interrupted time series studies. Therefore,
8 based on prior suggestions [3-6, 12, 20], we recommend the following be reported in all
9 interrupted time series applications: 1) autocorrelation, non-stationarity, and seasonality
10 considerations; 2) intervention time point(s) and lag periods; 3) the number of data points pre-,
11 post- and between-intervention(s); 4) specific statistical regression methods and the
12 appropriateness of a linear model when applied; and 5) absolute and/or relative changes from
13 baseline (intervention impact) with statistical significance. We also recommend that all
14 interrupted time series studies: 1) use graphical display with clearly defined time point(s) to
15 present results; 2) comment on: the minimum number of observations per data point, data
16 variability, ceiling or floor effects; and 3) consider the use of a comparison group. Authors are
17 also encouraged to discuss possible data or co-intervention confounding issues and provide a
18 rationale if no comparison group was considered. These recommendations are summarized in
19 Table 2, and build from the Strengthening the Reporting of Observational Studies in
20 Epidemiology (STROBE) statement [21].
21 * Insert Table 2 *
22 Our systematic review is subject to some limitations related to our literature search
23 strategy and inclusion criteria. First, we recognize that the lack of Medical Subject Headings
1 (MeSH terms) and standardized terminology to describe the interrupted time series design may
2 have resulted in some missed applications. Although segmented regression was first introduced
3 to healthcare research in 1981 [7], and a seminal method paper was published in 2002 [3], we
4 found that many papers did not use the term "segmented regression" to describe their analysis.
5 Therefore particular attention to each study's statistical analysis was required during data
6 abstraction to determine the type of interrupted time series method used. Second, our search was
7 limited in ability to identify applications that are not indexed in either of the databases used
8 (MEDLINE®, Web of Science®). Indeed, during the peer-review of our manuscript, a blind
9 reviewer identified one eligible article [B.109] that is not indexed in the databases used and
10 therefore was not identified in our original search.
11 Third, by restricting inclusion to studies that examined prescription drug utilization
12 defined by the number or proportion of prescription drugs dispensed or patients dispensed a
13 drug; we will have missed interrupted time series analyses with different drug outcomes, such as
14 illicit drug use, drug sales, or drug market share [22, 23]. Fourth, we acknowledge that studies
15 examining single institution interventions (n=59, Appendix C) were excluded so we could focus
16 on population-based interventions that may be more generalizable. Despite potentially missing
17 some applications, we feel that our results that identify an increase in the number of applications
18 in recent years, and conclusions of the general trends of methods and underreporting of statistical
19 considerations, would still hold. Indeed, 66% of the 59 single institution studies used segmented
20 regression analysis, similar to our finding that 67% of studies included in our review employed
21 segmented regression analysis.
22 Finally, we acknowledge that our review is limited by what authors have reported or
23 presented in their studies, which may not reflect the true methodological rigour of each study.
1 Therefore, the large variation in reporting that we identified may not indicate inappropriate use
2 of interrupted time series methods, but rather a need for reporting standards to facilitate quality
3 reporting, application, and interpretation of interrupted time series results.
4 A major strength of our systematic review is the use of multiple search strategies to
5 identify articles. Our keyword and citation search yielded 190 eligible articles (86% overall),
6 with only 31% identified in both. We attribute this small overlap to a lack of MeSH terms for
7 time series analysis. The additional reference list search of eligible articles identified another 30
8 (14% overall) eligible applications not captured in our prior searches. This observation
9 corroborates the importance of using multiple search strategies as identified in prior reviews of
10 new statistical methods [17, 18]. We encourage future systematic reviews to use a similar
11 proportional Venn diagram to clarify search strategy yield.
12 In summary, we identified an increase in the number of applications of interrupted time
13 series analysis to examine interventions in drug utilization, particularly in recent years. When
14 properly executed, interrupted time series analysis is a valuable method to evaluate the success,
15 failure, or unintended consequences of healthcare interventions on drug utilization [24].
16 However, there is large variation in the reporting of interrupted time series methods. Developing
17 methodological and reporting standards for interrupted time series analysis is important to
18 improve its application in drug utilization research. We provide a summary table of
19 methodological and reporting recommendations for researchers to consider when completing
20 interrupted time series analyses.
1 Acknowledgements
2 The authors declare no conflict of interest.
3 This research was supported by an Ontario Ministry of Research and Innovation Early
4 Research Award held by Dr. Suzanne Cadarette. Dr. Cadarette was supported by a Canadian
5 Institutes of Health Research (CIHR) New Investigator Award (MSH-95364). Racquel Jandoc
6 received support from the CIHR Training Program in Bridging Scientific Domains for Drug
7 Safety and Effectiveness. Authors thank Mina Tadrous, PharmD, MS, University of Toronto,
8 Gina Matesic, MA, MLIS, MEd, University of Toronto, Giulia Consiglio, BSc, University of
9 Toronto, Joanna Bielecki, Research Librarian, University of Toronto, and our anonymous
10 reviewers for insightful discussions or comments.
11 This research was presented at the International Conference on Pharmacoepidemiology
12 and Therapeutic Risk Management (ICPE) in Montreal QC, Canada, August 2013, the Canadian
13 Association for Population Therapeutics (CAPT) Annual Conference in Toronto ON, Canada,
14 November 2013, and the Canadian Association for Health Services and Policy Research
15 (CAHSPR) Annual Conference in Toronto ON, Canada, May 2014. Participation at ICPE was
16 supported by an ICPE Travel Scholarship, participation at CAPT was supported by a CAPT
17 Student Bursary and the Leslie Dan Faculty of Pharmacy Student Experience Fund, and
18 participation at CAHSPR was supported by a University of Toronto School of Graduate Studies
19 Conference Grant.
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1 Table 1. Characteristics of interrupted time series applications in drug utilization, n=220a
Characteristic_n_%
Intervention
Drug policy changes 112 50.9
Co-payments or cost-sharing 33 15.0
New drug or drug withdrawal 15 6.8
Prior authorization 21 9.5
Reimbursement changes 43 19.5
New evidence 48 21.8
Guideline changes 15 6.8
Major publications 33 15.0
Quality improvement interventions 35 15.9
Educational 18 8.2
Other quality improvement 17 7.7
Safety advisories 39 17.7 Data source
Administrative data 203 92.3
Medical charts 10 4.5
Survey data 7 3.2 Time point intervals
Monthly 166 75.5
Quarterly 31 14.1
Other (e.g. annually, bi-annually, bi-weekly, weekly) 23 10.5 Primary outcome measure
Patient 64 29.1
Number 15 6.8
Proportion 49 22.3
Prescriptions 157 71.4
Number 49 22.3
Proportion 77 35.0
Standardized dose (e.g. daily defined dose) 31 14.1
Interrupted time series method reportedb 200 90.9
ARIMA models 31 15.5
Reported invervention function (e.g. point, ramp, step)c 18 58.1
Linear regression 22 11.0
Segmented regression 134 67.0
Linear modelsd 67 50.0
Other modelsd (e.g. GEE, logistic, nonlinear) 9 6.7
Not specifiedd 58 43.3
Other regression (e.g. GEE, logistic, nonlinear, Poisson) 14 7.0 Statistical considerations reported
Autocorrelation 146 66.4
Comparison group 77 35.0
Confidence intervals or standard errors reported with estimates 153 69.5
Forecasting using pre-intervention trends 64 29.1
Graphical figures to display results 184 83.6
Lag periods 61 27.7
Non-stationarity 32 14.5
Number of pre- and post-intervention data points 86 39.1
Outliers 17 7.7
Seasonality 68 30.9
Sensitivity analysis 45 20.5
Time point clearly defined 186 84.5
1 ARIMA: Autoregressive integrated moving average; GEE: Generalized estimating equation
2 aSome characteristics are not mutually exclusive, thus proportions add to greater than 100%
3 bFor papers reporting detailed time series methods only, n=200
4 cFor papers reporting ARIMA only, n=31
5 dForpapers conducting segmented regression only, n=134
Table 2. Methodological and reporting recommendations for interrupted time series studiesa Item No
Recommendation
Title and abstract
Introduction
Background/ rationale
Objectives
1 Indicate the study design (interrupted time series) in the title or abstract
Provide background regarding the intervention and setting under investigation to support the study rationale and methods
(a) State specific objectives and any pre-specified hypotheses
(b) Distinguish between primary and secondary objectives
Methods
Intervention
Participants
Data sources and measurement
Variables
4 Define the intervention time point(s) used in the analysis
5 (a) List eligibility criteria and methods of selection
(b) Define any subgroups
(c) Consider including a comparison group not exposed to the intervention as a secondary group of participants
6 (a) List data source(s)
(b) Comment on data completeness, validity, and changes in data coverage over time
Statistical methods
Results
Participants
(b) (c)
(f) (g)
(b) (c)
Define all variables Outcome variable(s) Descriptive and stratifying variable(s) Comment on change in variable coding over time Consider including details of variable coding in supplemental material, e.g., Appendix or research website
Report all statistical methods
• Study time intervals, e.g., monthly, quarterly
• Regression model, e.g., ARIMA, linear, segmented
o For ARIMA models, indicate the intervention function, e.g.,
point, ramp, or step o Indicate the appropriateness of linear model(s) when applied
• Number of pre-, post- and between intervention data points Define the study period and number of pre-intervention data points used in forecasting
Indicate how autocorrelation, non-stationarity, and seasonality were tested and handled
Consider a lag period if intervention effects are gradual or delayed Define and distinguish between primary and secondary or sensitivity analyses
Consider use of comparison outcome(s) and/or population(s) not exposed to the intervention(s) as secondary analyses Report statistical software used for analysis
Report the number of individuals and/or observations in each group analyzed
Consider use of a flow diagram
Describe characteristics and indicate any missing data
Item No
Recommendation
Outcome data
Main results
Other analyses
Discussion
Key results
Context
Limitations
10 (a) Report the number of outcomes examined over the study period
(b) Report the average, minimum, and maximum number of outcomes across time intervals
(c) Report on data variability
(d) Comment on outliers, and floor or ceiling effects where relevant
11 (a) Present results using a graphical display with intervention time
point(s) clearly defined
(b) Consider including forecasted results graphically
(c) Report absolute and/or relative change(s) and their significance, e.g. clinical or policy and statistical
12 Report additional results (secondary and sensitivity analyses) in the manuscript, Appendix, or research website
13 Summarize key results with reference to study objectives
14 (a) Provide context related to possible confounding
• Discuss relevant co-interventions that occurred during the study period
• Comment on the stability of participant characteristics over time
• Comment on the stability of outcome coding over time
(b) Discuss results of comparison analyses, or provide a rationale if no comparison group was considered
15 (a) Discuss limitations of the study
(b) Comment on data variability and appropriateness of the number of data points
(c) Comment on ceiling or floor effects, and outliers where relevant
(d) Discuss direction and magnitude of any potential bias
16 Provide overall interpretation of results considering objectives, limitations, results from similar studies, and other relevant evidence
Interpretation
Other information
Funding 17 List funding source(s) and role of funders
References 18 Reference methodological papers that support statistical methods _employed_
1 ARRIMA: Autoregressive integrated moving average; GEE: Generalized estimating equation
2 aItems adapted from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement[2l]
10 11 12
20 21 22 23
Fig. 1. Flow diagram of systematic search results. MEDLINE was used for the keyword search
using search terms (Appendix A), and Web of Science was used for the citation search [3, 5, 7, 16]
Reviewer, n=1
9 Fig. 2. Proportional Venn diagram of search result yield of empirical applications by search
10 strategy, n=220. The size of each circle is proportional to the relative number of articles
11 identified. MEDLINE® keyword search terms are listed in Appendix A, and four papers were
12 used in the Web of Science citation search [3, 5, 7, 16]. The reference search included all
13 eligible empirical applications (n=190), methods (n=10), and reviews (n=4) identified by the
14 keyword and citation searches. One article not indexed in MEDLINE® or Web of Science®
15 databases was identified by a reviewer during the peer-review process.
-«tmtDis~oocnoT-(NcO'«tmtDis~oocn 0000000000000)010)010)010)010)01
OT-(NCO'«tli)tDls~OOCnOT-(NCO OOOOOOOOOOt-T-T-T-
oooooooooooooo
CNCNCNCNCNCNCNCNCNCNCNCNCNCN
Fig. 3. Number of interrupted time series empirical applications in drug utilization research, by publication year, n=220.
1 Appendices
2 Appendix A: List of keyword terms used for MEDLINE® search
3 1. Time series.tw.
4 2. Time trend$.tw.
5 3. Trend analys$.tw.
6 4. Time series analys$.tw.
7 5. Forecast model$.tw.
8 6. Intervention analys$.tw.
9 7. Drug.mp.
10 8. Medicat$.mp.
11 9. Pharmaceutic?. mp.
12 10. Prescri$.mp.
13 11. Pharmacoepidemiolog$.mp.
14 12. Dispens$.mp.
15 13. Drug utili#ation.mp.
16 14. Or/1-6
17 15. Or/7-13
18 16. 14 and 15
19 17. Limit 16 to English language
20 Search terms 8 and 9 were adopted from Green et al.
1 Appendix B: List of identified references (methodological papers, reviews, and empirical
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