Assessing Time-Varying Causal Effect Moderation in Mobile Health

Boruvka, Audrey; Almirall, Daniel; Witkiewitz, Katie; Murphy, Susan A.

doi:10.1080/01621459.2017.1305274

Cited by 138 publications

(159 citation statements)

References 32 publications

(55 reference statements)

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“…The causal effects they consider are a special case of our p lag causal effect Blackwell and Glynn (2016). defined the "blip" effect as a special case of the p > 0 lag causal effect, and their "contemporaneous" effect as a special case of our contemporaneous causal effect when the weights equal the reciprocal adapted propensity Boruvka et al (2017). defined the "lagged effect" as our p lag causal effect with the weights equal to the reciprocal adapted propensity.…”

mentioning

confidence: 99%

Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading

Bojinov

Shephard

2019

Journal of the American Statistical Association

110

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We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization based p-values for testing causal effects, without imposing stringent assumptions. We further derive a general central limit theorem that can be used to conduct conservative tests and build confidence intervals for causal effects. Finally, we provide three methods for generalizing our approach to multiple units that are receiving the same class of treatment, over time. We test our methodology on simulated "potential autoregressions,"which have a causal interpretation. Our methodology is partially inspired by data from a large number of experiments carried out by a financial company who compared the impact of two different ways of trading equity futures contracts. We use our methodology to make causal statements about their trading methods.

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mentioning

confidence: 99%

Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading

Bojinov

Shephard

2019

Journal of the American Statistical Association

110

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“…In summary, heterogenous treatment analysis with high-dimensional observational data offers a wealth of research opportunities. New research in this area is emerging (see, e.g., Athey et al, 2017, Wang, Li and Hopp, 2017a& 2017b, Boruvka et al, 2017 and has the potential to sharpen precision medicine protocols for a vast range of patients.…”

Section: Issues Related To Treatment Effect Analysismentioning

confidence: 99%

Big Data and the Precision Medicine Revolution

Hopp

Wang

2018

SSRN Journal

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The big data revolution is making vast amounts of information available in all sectors of the economy including health care. One important type of data that is particularly relevant to medicine is observational data from actual practice. In comparison to experimental data from clinical studies, observational data offers much larger sample sizes and much broader coverage of patient variables. Properly combining observational data with experimental data can facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of health care to the specific needs of individuals. However, because it is high-dimensional and uncontrolled, observational data presents unique methodological challenges. The modeling and analysis tools of the production and operations management field are well-suited to these challenges and hence POM scholars are critical to the realization of precision medicine with its many benefits to society.

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“…A proximal measure for the 4PM inspirational message is whether or not the participant self-reports later on the same day. For more details on MRTs, we refer the readers to Klasnja et al [78,79,80]. In the following, we describe the MRT protocol for SARA.…”

Section: Objective Of the Studymentioning

confidence: 99%

“…A key issue is that covariates (measures of the participant's context) at a time point can be affected by past engagement strategies. For such a setting, Boruvka et al [80] proposed a method to estimate the causal effects of interventions on continuous outcomes. In SARA, however, we are dealing with a binary outcome (whether participants self-reported or not).…”

Section: Statistical Analysesmentioning

confidence: 99%

Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults (Preprint)

Rabbi¹,

Philyaw-Kotov²,

Cunningham³

et al. 2018

Preprint

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BACKGROUND Substance use is an alarming public health issue associated with significant morbidity and mortality. Adolescents and emerging adults are at particularly high risk because substance use typically initiates and peaks during this developmental period. Mobile health apps are a promising data collection and intervention delivery tool for substance-using youth as most teens and young adults own a mobile phone. However, engagement with data collection for most mobile health applications is low, and often, large fractions of users stop providing data after a week of use. OBJECTIVE Substance Abuse Research Assistant (SARA) is a mobile application to increase or sustain engagement of substance data collection overtime. SARA provides a variety of engagement strategies to incentivize data collection: a virtual aquarium in the app grows with fish and aquatic resources; occasionally, funny or inspirational contents (eg, memes or text messages) are provided to generate positive emotions. We plan to assess the efficacy of SARA’s engagement strategies over time by conducting a micro-randomized trial, where the engagement strategies will be sequentially manipulated. METHODS We aim to recruit participants (aged 14-24 years), who report any binge drinking or marijuana use in the past month. Participants are instructed to use SARA for 1 month. During this period, participants are asked to complete one survey and two active tasks every day between 6 pm and midnight. Through the survey, we assess participants’ daily mood, stress levels, loneliness, and hopefulness, while through the active tasks, we measure reaction time and spatial memory. To incentivize and support the data collection, a variety of engagement strategies are used. First, predata collection strategies include the following: (1) at 4 pm, a push notification may be issued with an inspirational message from a contemporary celebrity; or (2) at 6 pm, a push notification may be issued reminding about data collection and incentives. Second, postdata collection strategies include various rewards such as points which can be used to grow a virtual aquarium with fishes and other treasures and modest monetary rewards (up to US $12; US $1 for each 3-day streak); also, participants may receive funny or inspirational content as memes or gifs or visualizations of prior data. During the study, the participants will be randomized every day to receive different engagement strategies. In the primary analysis, we will assess whether issuing 4 pm push-notifications or memes or gifs, respectively, increases self-reporting on the current or the following day. RESULTS The microrandomized trial started on August 21, 2017 and the trial ended on February 28, 2018. Seventy-three participants were recruited. Data analysis is currently underway. CONCLUSIONS To the best of our knowledge, SARA is the first mobile phone app that systematically manipulates engagement strategies in order to identify the best sequence of strategies that keep participants engaged in data collection. Once the optimal strategies to collect data are identified, future versions of SARA will use this data to provide just-in-time adaptive interventions to reduce substance use among youth. CLINICALTRIAL ClinicalTrials.gov NCT03255317; https://clinicaltrials.gov/show/NCT03255317 (Archived by WebCite at http://www.webcitation.org/70raGWV0e) REGISTERED REPORT IDENTIFIER RR1-10.2196/9850

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Assessing Time-Varying Causal Effect Moderation in Mobile Health

Cited by 138 publications

References 32 publications

Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading

Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading

Big Data and the Precision Medicine Revolution

Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults (Preprint)

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