Digital adherence technologies to improve tuberculosis treatment outcomes in China: a cluster-randomised superiority trial

Liu, Xiaoqiu; Thompson, Jennifer A.; Dong, Haiyan; Sweeney, Sedona; Li, Xue; Yuan, Yanli; He, Wangrui; Thomas, Bruce; Xu, Cheng; Hu, Dongmei; Vassall, Anna; Huan, Shitong; Zhang, Hui; Jiang, Shaobo; Fielding, Katherine; Zhao, Yanlin

doi:10.1016/s2214-109x(23)00068-2

Cited by 28 publications

(16 citation statements)

References 20 publications

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“…29,30 However, they do not guarantee full adherence and so the issues with ITT and PP analyses highlighted by the current study are likely to persist. [31][32][33] Our findings are also of interest beyond TB trials, where non-adherence to treatment is likely to remain a concern for the foreseeable future.…”

Section: Discussionmentioning

confidence: 75%

Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?

Dodd,

Carpenter,

Thompson

et al. 2024

Statistics in Medicine

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BackgroundNon‐inferiority trials comparing different active drugs are often subject to treatment non‐adherence. Intention‐to‐treat (ITT) and per‐protocol (PP) analyses have been advocated in such studies but are not guaranteed to be unbiased in the presence of differential non‐adherence.MethodsThe REMoxTB trial evaluated two 4‐month experimental regimens compared with a 6‐month control regimen for newly diagnosed drug‐susceptible TB. The primary endpoint was a composite unfavorable outcome of treatment failure or recurrence within 18 months post‐randomization. We conducted a simulation study based on REMoxTB to assess the performance of statistical methods for handling non‐adherence in non‐inferiority trials, including: ITT and PP analyses, adjustment for observed adherence, multiple imputation (MI) of outcomes, inverse‐probability‐of‐treatment weighting (IPTW), and a doubly‐robust (DR) estimator.ResultsWhen non‐adherence differed between trial arms, ITT, and PP analyses often resulted in non‐trivial bias in the estimated treatment effect, which consequently under‐ or over‐inflated the type I error rate. Adjustment for observed adherence led to similar issues, whereas the MI, IPTW and DR approaches were able to correct bias under most non‐adherence scenarios; they could not always eliminate bias entirely in the presence of unobserved confounding. The IPTW and DR methods were generally unbiased and maintained desired type I error rates and statistical power.ConclusionsWhen non‐adherence differs between trial arms, ITT and PP analyses can produce biased estimates of efficacy, potentially leading to the acceptance of inferior treatments or efficacious regimens being missed. IPTW and the DR estimator are relatively straightforward methods to supplement ITT and PP approaches.

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Section: Discussionmentioning

confidence: 75%

Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?

Dodd,

Carpenter,

Thompson

et al. 2024

Statistics in Medicine

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“…Two cohort studies [22,30]and one RCT [31]examined the frequency of recurrence after treatment for TB disease. Of these, only one cohort study suggested a lower rate of recurrence in the DAT group.…”

Section: Resultsmentioning

confidence: 99%

“…Results for loss to follow-up and adverse event reporting were inconsistent: one study suggested that VOT was associated with an increase in loss to follow-up [29] , while another suggested that a two-way SMS-based intervention increased the number of adverse events reported. [26] Long-term clinical outcomes Two cohort studies [22,30]and one RCT [31]examined the frequency of recurrence after treatment for TB disease. Of these, only one cohort study suggested a lower rate of recurrence in the DAT group.…”

Section: Short-term Clinical Outcomes In Tb Infectionmentioning

confidence: 99%

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The Impact of Digital Adherence Technologies on Health Outcomes in Tuberculosis: A Systematic Review and Meta-Analysis

Mohamed,

Zary,

Kafie

et al. 2024

Preprint

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Background: Suboptimal tuberculosis (TB) treatment adherence may lead to unsuccessful treatment and relapse. Digital adherence technologies (DATs) may allow more person-centric approaches for supporting treatment. We conducted a systematic review (PROSPERO- CRD42022313166) to evaluate the impact of DATs on health outcomes in TB. Methods: We searched MEDLINE, Embase, CENTRAL, CINAHL, Web of Science and preprints from medRxiv, Europe PMC, and clinicaltrials.gov for relevant literature from January 2000 to April 2023. We considered experimental or cohort studies reporting quantitative comparisons of clinical outcomes between a DAT and the standard of care in each setting. Results: Seventy studies (total 58,950 participants) met inclusion criteria. They reported SMS-based interventions (k=18 studies), feature phone-based interventions (k=7), medication sleeves with phone calls (branded as “99DOTS,” k=5), video-observed therapy (VOT; k=17), smartphone-based interventions (k=5), digital pillboxes (k=18), ingestible sensors (k=1), and interventions combining 2 DATs (k=1). Overall, the use of DATs was associated with more frequent treatment success in TB disease (OR = 1.18 [1.06, 1.33]; I2 = 66%, k = 46), a decrease in loss to follow up (OR = 0.71 [0.53, 0.94]; I2 = 80%, k = 36) and an increase in adverse event reporting (OR = 1.53 [1.26, 1.86]; I2 = 0%, k = 9). VOT was associated with an increased likelihood of treatment success in TB disease (OR 1.54 [1.09; 2.19]; I2 = 0%, k = 8) and treatment completion in TB infection (OR 4.69 [2.08; 10.55]; I2 = 0%, k = 2) as well as an increased frequency of adverse event reporting (OR = 1.79 [1.27; 2.52]; I2 = 34%, k = 4). Other interventions involving smartphone technologies were associated with increased treatment success in TB disease (OR 1.98 [1.07; 3.65]; I2 =56%, k = 5) and a decreased frequency of loss to follow up(OR = 0.31 [0.13; 0.77]; I2 = 36%, k = 5). Digital pillboxes were also associated with an improvement in treatment success (OR = 1.32 [1.00; 1.73]; I2 = 71%, k = 11). However, improved treatment success was only seen in high- and upper middle-income countries. SMS-based interventions, feature-phone interventions and 99DOTS were not associated with improvements in short-term clinical outcomes. Conclusion: Certain DATs--notably VOT and smartphone-based interventions, in higher income settings and sometimes combined with other supportive measures—may be associated with improvements in treatment success and losses to follow-up, compared to standard care. However, evidence remains highly variable, and generalizability limited. Higher quality data are needed.

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“…Patients were assigned to one of four adherence categories (<70%, 70–85%, 85–90%, or ≥90% of mean doses taken per week). These categories were selected on the basis of data from adherence-monitoring studies, 21 , 22 , 23 which were used to parameterise the standard-of-care adherence distribution. The effects of regimen forgiveness were modelled as a step function, on the basis of an individual data synthesis suggesting an 18% lower probability of cure with 6HRZE for patients with adherence lower than 90%.…”

Section: Methodsmentioning

confidence: 99%

Economic implications of novel regimens for tuberculosis treatment in three high-burden countries: a modelling analysis

Ryckman,

Schumacher,

Lienhardt

et al. 2024

The Lancet Global Health

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Digital adherence technologies to improve tuberculosis treatment outcomes in China: a cluster-randomised superiority trial

Cited by 28 publications

References 20 publications

Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?

Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?

The Impact of Digital Adherence Technologies on Health Outcomes in Tuberculosis: A Systematic Review and Meta-Analysis

Economic implications of novel regimens for tuberculosis treatment in three high-burden countries: a modelling analysis

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