Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey

Cheung, Ying Kuen; Wood, Dallas; Zhang, Kangkang; Ridenour, Ty; Derby, Lilly; Onge, Tara St.; Duan, Naihua; Duer-Hefele, Joan; Davidson, Karina W.; Kronish, Ian M.; Moise, Nathalie

doi:10.1136/bmjopen-2019-036056

Cited by 15 publications

(11 citation statements)

References 32 publications

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“…N‐of‐1 trials are multi‐period crossover studies that compare two or more interventions in single individuals, and are suitable for evaluating personalized treatment effects in those with chronic conditions where the outcome is relatively stable 1 . Advances in mobile and sensor technology 2 and better understanding of patient preferences 3 have improved the implementation of N‐of‐1 trials. However, their uptake remains very small in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Analysis of N‐of‐1 trials using Bayesian distributed lag model with autocorrelated errors

Liao

Qian

Kronish

et al. 2023

Statistics in Medicine

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An N-of-1 trial is a multi-period crossover trial performed in a single individual, with a primary goal to estimate treatment effect on the individual instead of population-level mean responses. As in a conventional crossover trial, it is critical to understand carryover effects of the treatment in an N-of-1 trial, especially when no washout periods between treatment periods are instituted to reduce trial duration. To deal with this issue in situations where a high volume of measurements are made during the study, we introduce a novel Bayesian distributed lag model that facilitates the estimation of carryover effects, while accounting for temporal correlations using an autoregressive model. Specifically, we propose a prior variance-covariance structure on the lag coefficients to address collinearity caused by the fact that treatment exposures are typically identical on successive days. A connection between the proposed Bayesian model and penalized regression is noted. Simulation results demonstrate that the proposed model substantially reduces the root mean squared error in the estimation of carryover effects and immediate effects when compared to other existing methods, while being comparable in the estimation of the total effects. We also apply the proposed method to assess the extent of carryover effects of light therapies in relieving depressive symptoms in cancer survivors.

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Section: Introductionmentioning

confidence: 99%

Analysis of N‐of‐1 trials using Bayesian distributed lag model with autocorrelated errors

Liao

Qian

Kronish

et al. 2023

Statistics in Medicine

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“…Then, a comparison is made about how each patient's outcomes differ from effects pooled across all patients in the series. Graphical displays or statistical tests of the data can establish the HTEs Cheung et al, 2020). Procedures to calculate these values will be described below.…”

Section: Introductionmentioning

confidence: 99%

An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial

Chandereng

2022

Harvard Data Science Review

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The call for personalized medicine highlights the need for personalized (N-of-1) trials to find what treatment works best for individual patients. Conventional (between-subject) randomized controlled trials (RCT) yield effects for the 'average patient,' but a personalized trial administers all treatments within-subject, so benefits or harms to the individual patient can be identified. The design and analysis of personalized trials involve different strategies from the conventional RCT. These include how to adjust for any carryover effects from one intervention to another, how to handle missing data, and how to provide patients with insight into their data. In addition, a comprehensible report about trial results should be created for each patient and their clinician to facilitate their decision-making. This article describes strategies to address these design and analytic issues, and introduces an R shiny app to facilitate their solution, to explain the use of each of the design and statistical strategies. To illustrate, we also provide a concrete example of a personalized trial series designed to increase activity (i.e., walking steps) in patients with chronic lower back pain (CLBP).

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“…Some studies have quantified how trial design features impact willingness to participate, most commonly finding that participation increases with higher remuneration for participating [12,18], lower risk of adverse events [10,12], smaller time commitment associated with participating [18,19], and involvement of a clinician in the trial or sharing of reports back with a clinician [18,19]. However, this research does not help inform some of the key design questions currently being asked by study designers, such as how willingness to participate varies with strategies to decentralize trials (i.e., involving fewer hours at a clinical site and more hours at home), additional support for patients, and different methods of participant-completed data collection.…”

Section: Introductionmentioning

confidence: 99%

How do study design features and participant characteristics influence willingness to participate in clinical trials? Results from a choice experiment

Thomas

Mulnick

Krucien

et al. 2022

BMC Med Res Methodol

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Background Research about the decision to participate in a clinical study has tended to be limited to single indications and has focused on narrow sets of study and participant characteristics. This study applied stated preference methods to understand the clinical trial design attributes that most influence willingness to participate and how this varied with participant characteristics. Methods Adults residing in the US, China, or Poland with a self-reported diagnosis of cancer, heart disease, migraine, rheumatoid arthritis, or multiple sclerosis completed an online survey. Participants were asked whether they would participate in clinical studies defined by seventeen attributes within five categories (payment/support, administration/procedures, treatment-related, study location/time commitment, and data collection/feedback). Participants saw six different hypothetical clinical study profiles. Depending on their participation decision to an initial clinical study profile, the subsequent five questions had one design attribute (randomly selected per question) consecutively improved or deteriorated to elicit preferences. A logistic regression was used to determine which participant characteristics influenced participation decisions. A latent class logit model was used to identify how the influence of study design features varied between participants and whether groups of participants with similar preferences could be identified. Results The survey was completed by 487 participants (32% China, 35% Poland, 33% US; 8%–19% per indication). Willingness to participate was found to be a function of participant age, certain elements of quality of life, and previous treatment experience, in particular number of lines of treatment received and experience of adverse events. Willingness to participate was influenced by study design features such as payment, study duration, and time commitment – both the overall time and whether the time was at home or away from home, with the latter being particularly relevant to participants experiencing fatigue due to their disease. Conclusions This study quantifies how study designs influence willingness to participate and how this varies with participant types. These findings suggest that it is how an indication influences quality of life and treatment experience, rather than the indication alone, that impacts participation rates, opening the way for insights that are transferrable across indications, which may be particularly useful when considering rare diseases.

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Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey

Cited by 15 publications

References 32 publications

Analysis of N‐of‐1 trials using Bayesian distributed lag model with autocorrelated errors

Analysis of N‐of‐1 trials using Bayesian distributed lag model with autocorrelated errors

An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial

How do study design features and participant characteristics influence willingness to participate in clinical trials? Results from a choice experiment

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