The use of regular text messaging over one year to collect primary outcome data in an RCT Reuben Ogollah1,2, Martyn Lewis1,2, Kika Konstantinou1 Sarah.

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Presentation transcript:

The use of regular text messaging over one year to collect primary outcome data in an RCT Reuben Ogollah1,2, Martyn Lewis1,2, Kika Konstantinou1 Sarah Lawton2, Jamie Garner2, Nadine Foster1,2 1Arthritis Research UK Primary Care Centre, Research Institute for Primary Care and Health Sciences, David Weatherall Building, Keele University, Staffordshire, ST5 5BG 2Keele Clinical Trials Unit, David Weatherall Building, Keele University, Staffordshire, ST5 5BG

RCTs with time-to-event outcome data The primary endpoint of interest is time-to-event or failure time Ideally individuals are observed until event has occurred Frequent repeated data collection needed to accurately capture the event of interest with reduced risk of recall bias A ubiquitous and inevitable problem of missing data

Time-to-event outcome data Loss to follow-up Study end Censored Lost to follow-up: event time censored 0 2 4 6 8 10 12 Time (months)

Minimising dropout at design stage Postal or electronically-sent questionnaire surveys are not ideal for collecting frequent data Often yield poor response rates even after several reminder mailings Alternative: short message service (SMS) to obtain prospective, real-time data

Objectives Describe the use of SMS to obtain weekly primary outcome data Describe the response patterns to SMS Implications for data analysis

Study design (the SCOPiC trial) Usual, non-stratified care No systematic subgrouping or targeting treatment Stratified care Group 1: Advice and support to self-manage Group 2: Physiotherapist-led treatment Group 3: Fast-tracked, with MRI, to spinal specialists 470 adults with sciatica/suspected sciatica from ~30 GP practices

Primary outcome Total followed up to date, n= 422 (target, n= 470) “Compared to how you were at the SCOPiC clinic X weeks/months ago, how are your back and leg symptoms today?” Completely recovered Much better Better Same/ no change Worse Much worse Total followed up to date, n= 422 (target, n= 470) 90% (n=380) 10% (n=42)

Participant receive postcard on Thursday Primary outcome Compared to how you were at the SCOPiC clinic X weeks/months ago, how are your back and leg symptoms today?” Completely recovered Much better Better Same/ no change Worse Much worse First weekly SMS sent on Sunday following the SCOPIC clinic Non-responders after 48 hours sent SMS reminders (Tuesday) Non-responders receive post-card reminders after 48 hours (mailed Wednesday 1st class) Participant responds 7 days Second weekly SMS to collect outcome data (2nd Sunday) Participant receive postcard on Thursday Weekly for first 4 months Monthly until recovered, or up to 12 months

Weekly/ monthly response rates 7,125 out of 7,975 (89%) valid responses received 78% Phone call 91% SMS [P<0.001] Pattern of non-response: both intermittent and dropout

Weekly/ monthly response rates All participants (n=422; 380 SMS, 42 phone calls) Percentage of complete responses

Weekly response rates Median (IQR) weekly response rates for the first 16 weeks SMS Phone call

Implications for data analysis High response rate: reduced amount of missing outcome data Increased power Minimise bias Improve generalizability of the results Strengthen intention to treat principle - data from all randomised participants utilised Plausible non-informative censoring (ignorable missing) and reduced problems around interval censoring Non-informative censoring: the possibly unknown true time to the event for a patient is the same regardless of whether or not it is actually observed (or whether censoring occurs or not prior to it).

Conclusions Collecting frequent follow-up outcome data with SMS is feasible in a RCT High response rate (>90%) for weekly text data collection very promising This could be an additional and/or alternative strategy for collecting regular primary outcome data (especially time-to-event outcome) in large pragmatic trials Future work: build a dynamic workflow of questions, presenting the next item based on previous responses Response modifies the next question Alternative when you ask short key outcomes

Acknowledgments This study is funded by the National Institute for Health Research Health Technology Assessment Programme (NIHR HTA project number 12/201/09) and will be published in full in Health Technology Assessment. Nadine Foster is supported through an NIHR Research Professorship (NIHR-RP-011-015) and NIHR Senior Investigator award. Kika Konstantinou is supported through a HEFCE Senior Clinical Lecturer award.  The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the HTA programme, NIHR, NHS or the Department of Health.

Keele Clinical Trials Unit David Wetherall Building Keele University Newcaslte-under-Lyme ST5 5BG Tel: 01782 732916 Fax: 01782 734719 ​www.keele.ac.uk/kctu//

Weekly/ monthly response rates Those who have been followed up for at least 16 weeks (n=359; 321 SMS, 38 phone calls)

Weekly/ monthly response rates ··· W16 M5 M10 M11 M12 X 100% • Y% Z% 7,125 out of 7,975 (89%) valid responses received 78% Phone call, 91% SMS Predominantly intermittent pattern of non-response

Weekly/ monthly response rates Median (IQR) monthly response rates from months 5 to 12