Supplementary appendix

This appendix has been provided by the authors to give readers additional information about their work. Supplement to: Web

search popularity, publicity and utilization of direct oral anticoagulants in the United States, 2008-2018: A STROBE-

compliant study

Table A: Methodological framework of the study

Purpose To monitor longitudinal changes in the popularity for direct oral anticoagulants in the United States; to validate popularity patterns and trends; to examine the effect of publicity; to associate popularity with their actual utilization

Time Period Searched 10-year span; Beginning July 1st , 2008; end June 30th , 2018

Time division Monthly intervals

Geographic division United States; nationwide coverage

Web data source Google Trends portal

Query category All categories

Query combination used 5 combinations used, including generic and brand names of FDA approved drugswarfarin+jantoven+coumadin ;dabigatran+pradaxa;rivaroxaban+xarelto ;apixaban+eliquis;edoxaban+savaysaA “+” sign between terms correspond to an “OR” Boolean operator in Google Trends ;

Search rationale To compare the relative popularity of the 4 competing direct oral anticoagulants and warfarin (the previous standard of care) in the U.S.; the search combines generic names with brand names of FDA-approved drugs

Primary findings Relative web-search popularity of competing drugs; patterns of change; critical dates; popularity trends

Analysis type Time series analysis, using Google Trends data to compare drug popularity across time periods

Comparison data - Publicity related to the novel drug competitors (Source: LexisNexis Academic; July 1st, 2008-June 30th, 2018 / Coverage: University Wire; WebNews – all languages; US Newspapers; Web Blogs; Magazine Stories, Combined; Markets and Industry News; Major World Publications; Newspaper Stories, Combined Papers; WebNews - Academic; Wire Service Stories / Location: United States

- National (U.S.) trends in oral anticoagulant use (2007-2014)- ref. [19,30]

Notes:

Web search popularity: Google Trends adjusts search query volume by total searches, geography and time, and scales

results. As a result, for any given geography and time, Google Trends computes the ratio the volume of a query to total

volume of searches, scales values proportionally to a range of 0-100, and marks estimates on a timeline series graph. The

methodology allows to simultaneously compare the trends for multiple search queries (up to five) in terms of their relative

popularity. The interface allows the extraction of output data in a spreadsheet format to be used for interpretation and

analysis.

We categorized search terms in five distinct queries, combining generic names and U.S. brand names of FDA-approved

DOACs and warfarin (details in the Supplementary appendix): Q1 for “warfarin+Jantoven+Coumadin”; Q2 for

“dabigatran+Pradaxa”; Q3 for “rivaroxaban+Xarelto”; Q4 for “apixaban+Eliquis”; Q5 for “edoxaban+Savaysa”. Google

Trends interprets the search queries of the form “term1+ term2+…+termn” as an “OR” Boolean operator, and retrieves results

containing any one of the terms specified, or any combination including two or more terms.

Google scales popularity using integers only, and minimal popularity is denoted as <1. We replaced <1 with zeros to mark

minimal interest for data analysis. Zero values are reserved by Google to denote insufficient searches above the privacy

threshold (missing values). Edoxaban was the only time series to have a missing values early (12, 10%) in time series that

were interpolated.

We exported the output graph and described the patterns of the relative popularity of the five competitor medications. We

extracted the pertinent spreadsheet record of the search results, which provides the numeric estimates by each medication, on

a month-to-month basis. We reconstructed data graphically on monthly intervals to have a more analytic view of longitudinal

trends and added reference lines to indicate the critical FDA approval dates for DOACs.

Media coverage (publicity): The search (Source: LexisNexis Academic) covered U.S. newspapers, major web news sources

and blogs, magazines, markets and industry news, magazine stories, major publications, and newspaper stories, from July 1st,

2008, to June 30th, 2018. We used generic and brand names for DOACs separated by “OR” Boolean operator as search terms.

Monthly lists were retrieved and after removal of similar publications, we studied the pertinent texts and assigned a score for

each one of the DOACs included in the text. If a single DOAC was mentioned in the publication, a score of one was assigned.

Otherwise the score was weighted: when two agents were mentioned 0.5 was assigned in each; 0.333 was assigned in each of

three; 0.25 when all four DOACs were mentioned.

Correlation between popularity and public interest: The input variable (media coverage) and the output variable (web

search popularity) series were examined for stationarity. Monthly Google Trends data were transformed to first-order

differences (taking the changes between two consecutive points in time) to achieve stationarity, a prerequisite before running

the cross correlation coefficient function, to avoid spurious associations. We implemented a pre-whitening strategy as an aid

to identify which lags of media coverage may predict web search popularity.

The cross correlation coefficient function determines whether there is a relationship between the observations of two time

series that are separated by k time units, allowing for lags (and leads) between time series. We examined whether the number

of news articles score (media coverage) (source: LexisNexis Academic) at different monthly lags were associated with

DOACs web search popularity (source: Google Trends portal). A significant correlation at lag 0 is considered synchronous. A

significant correlation at a positive lag, denotes that a change in web search popularity will follow after a change in media

coverage; that is, media coverage will impact web search popularity with a delay.

As a first step, we transformed the media coverage time series, generating the residuals from an autoregressive integrated

moving average process (ARIMA modeling) and adjusted for outliers. ARIMA modeling explains the dependent variable as a

function of p past values (autoregressive component), integrated model of order d (differencing order), and the q most recent

past white noise (moving average component). The model may expand to include seasonal components (SARIMA). Outliers

represent irregularities in time series data that may affect model estimations and predictions, and may be aberrant values or

signify the presence of an underlying temporary process (e.g. a policy change, advertising or other intervention events). The

presence of a common underlying process in two time series may hamper cross-correlation analysis, resulting in spurious

associations. We examined three types of outliers: additive outliers (AO), events which mark a pulse effect lasting for single

period; temporary changes (TC), events with exponential decay over time; and level shifts (LS), events that alter time series

permanently thereafter. A model with a good fit requires that the residuals have no remaining autocorrelation and are

scattered around zero (as a sequence of uncorrelated random variables with zero mean, “white noise”), and low AIC (Akaike

Information Criterion) and BIC (Bayesian Information Criterion) values.

In a following step, we filtered the web search popularity series using the media coverage model coefficients and computed

the residuals. Finally, we examined the cross correlation function between the residuals of the first two steps.

Table B. Calendar of FDA approval dates and specific indications

NVAF=non-valvular atrial fibrillation; DVT=deep vein thrombosis; PE=pulmonary embolism

Date Drug IndicationOct 19, 2010 Dabigatran (Pradaxa) First approval; to prevent stroke in NVAFJul  1, 2011 Rivaroxaban (Xarelto) First approval; to prevent DVT after hip/knee replacement surgeryNov 4, 2011 Rivaroxaban (Xarelto) To prevent stroke in NVAF

Nov 2, 2012 Rivaroxaban (Xarelto) To treat DVT/PE and reduce risk of recurrence

Dec 28, 2012 Apixaban (Eliquis) First approval; to prevent stroke in NVAF

Mar 18, 2014 Apixaban (Eliquis) To prevent DVT after hip/knee replacement surgery

Apr 7, 2014 Dabigatran (Pradaxa) To treat DVT and PE

Aug 21, 2014 Apixaban (Eliquis) To treat DVT/PE and reduce risk of recurrence

Jan 8, 2015 Edoxaban (Savaysa) First approval; to prevent stroke in NVAF

Nov 23, 2015 Dabigatran (Pradaxa) For DVT prophylaxis after hip replacement surgery

Oct 30, 2017 Rivaroxaban (Xarelto) To reduce risk of recurrent venous thromboembolism,>6 months administration

Fig A. Dabigatran/Pradaxa media coverage

Fig B. Rivaroxaban/Xarelto media coverage

Fig C. Apixaban/Eliquis media coverage

Fig D. Edoxaban/Savaysa media coverage

Table C. Fitted (S)ARIMA models and outliers in media coverage time series

DOAC media coverage Fitted Model

ARIMA (p,d,q) / SARIMA (p,d,q) x (P,D,Q)s

Type/position

of outlier

σ2 adjusted

(unadjusted)

dabigatran/Pradaxa ARIMA(0,1,1) 71 TC ( 5 2014)*

27 LS ( 9 2010)*

40.09 (53.54)

apixaban/Eliquis ARIMA (3,0,0) 48 AO ( 6 2012) **

54 AO (12 2012)†

55 AO ( 1 2013)†

61 AO ( 7 2013)††

44.89 (79.44)

rivaroxaban/Xarelto ARIMA(0,1,1) 9 AO ( 3 2009) ^

39 AO ( 9 2011)^^

41 AO (11 2011)‡

47 TC ( 5 2012)O

73.49 (118.38)

edoxaban/Savaysa SARIMA(0,1,1)x(0,0,1) 65 TC (11 2013)

67 AO ( 1 2014)

76 TC (10 2014)#

79 TC ( 1 2015) ##

84 AO ( 6 2015)

92 LS ( 2 2016)

98 AO ( 8 2016)¶

114 AO (12 2017)¶¶

3.00 (12.02)

(S)ARIMA= (seasonal) autoregressive integrated moving average; p,d,q= order of autoregressive, order of

differencing, and moving average component; P,D,Q = order of autoregressive, order of differencing, and moving

average component of the seasonal part of the ARIMA model, s = number of periods in each season. AO=additive

outlier; TC=temporary change; LS=level shift; σ2 adjusted= estimated error variance including outliers; σ2

unadjusted = estimated error variance without outliers in the model.

*mark dabigatran FDA approval dates (±1 month) ; ** marks FDAs decision to delay approval for apixaban ; †

marks apixaban first approval date (±1 month); ††marks i) apixaban FDA approval to review for DVT prophylaxis

and ii) apixaban positive outcome of AMPLIFY landmark trial for treatment of acute venous thrombembolism ;

^FDA panel backs rivaroxaban for approval; ^^FDA panel backs rivaroxaban for approval; ‡ marks rivaroxaban

first FDA approval date(±1 month) ; o FDA panel votes against rivaroxaban approval in acute coronary syndromes

#FDA panel backs edoxaban for approval; ## marks FDA approval date for edoxaban; ¶Phase 3b ENSURE-AF

Study positive results for edoxaban ; ¶¶phase 3b Hokusai-VTE CANCER Study positive results for edoxaban