Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations

Percha, Bethany; Baskerville, Edward B.; Johnson, Matthew; Dudley, Joel T.; Zimmerman, Noah

doi:10.2196/12641

Cited by 31 publications

(34 citation statements)

References 15 publications

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“…More stratification of patients leads to increasingly small patient (sub)groups, complicating the generation of (sufficiently) statistically powered data on treatment effectiveness through traditional randomised controlled trials (RCTs). Several alternative trial designs have been developed, including basket trials [ 31 , 32 ], umbrella trials [ 31 ], n-of-1 trials [ 33 ] and adaptive trials [ 34 ]. While some of these alternative designs still allow for controlled studies, many are single-arm designs.…”

Section: Resultsmentioning

confidence: 99%

Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine

et al. 2021

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Objective The objective of this study was to develop guidance contributing to improved consistency and quality in economic evaluations of personalised medicine (PM), given current ambiguity about how to measure the value of PM as well as considerable variation in the methodology and reporting in economic evaluations of PM. Methods A targeted literature review of methodological papers was performed for an overview of modelling challenges in PM. Expert interviews were held to discuss best modelling practice. A systematic literature review of economic evaluations of PM was conducted to gain insight into current modelling practice. The findings were synthesised and used to develop a set of draft recommendations. The draft recommendations were discussed at a stakeholder workshop and subsequently finalised. Results Twenty-two methodological papers were identified. Some argued that the challenges in modelling PM can be addressed within existing methodological frameworks, others disagreed. Eighteen experts were interviewed. They believed large uncertainty to be a key concern. Out of 195 economic evaluations of PM identified, 56% addressed none of the identified modelling challenges. A set of 23 recommendations was developed. Eight recommendations focus on the modelling of test-treatment pathways. The use of non-randomised controlled trial data is discouraged but several recommendations are provided in case randomised controlled trial data are unavailable. The parameterisation of structural uncertainty is recommended. Other recommendations consider perspective and discounting; premature survival data; additional value elements; patient and clinician compliance; and managed entry agreements. Conclusions This study provides a comprehensive list of recommendations to modellers of PM and to evaluators and reviewers of PM models. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01010-z.

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Section: Resultsmentioning

confidence: 99%

Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine

et al. 2021

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“…Such models may not be applicable to all N-of-1 studies. It would be of value to assess the influence of different serial correlation assumptions on the power of N-of-1 clinical trials, as well as the robustness of inferences drawn when incorrect assumptions about the serial correlation are made, as considered in a recent set of simulation studies [9]. Second, we considered only fixed intervention periods with a pre-specified number of response collections over the course of a trial comparing two interventions.…”

Section: Discussionmentioning

confidence: 99%

“…Clinical trials that focus on intervention responses exhibited by individual patients have been described in the literature [3,4,5,6,7,8,9]. Such single subject or ‘N-of-1′ clinical trials have been conducted in a variety of ways, and although some recent studies have leveraged very sophisticated designs and clinical outcome measures [9,10,11], many N-of-1 studies pursued to date have lacked sophisticated experimental designs, data analysis methods, and inferential procedures [9,12]. In this context, Gabler et al [13] performed a systematic review of the literature for N-of-1 studies and found that of the 108 N-of-1 trials they identified from the literature involving a total of 2154 individuals, only 49% of those trials used statistical criteria to determine a superior intervention, whereas others used a graphical comparison (25%) or a clinical significance cutoff (20%).…”

Section: Introductionmentioning

confidence: 99%

Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods

Yan-pin¹,

Schork

2019

Healthcare

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“N-of-1,” or single subject, clinical trials seek to determine if an intervention strategy is more efficacious for an individual than an alternative based on an objective, empirical, and controlled study. The design of such trials is typically rooted in a simple crossover strategy with multiple intervention response evaluation periods. The effect of serial correlation between measurements, the number of evaluation periods, the use of washout periods, heteroscedasticity (i.e., unequal variances among responses to the interventions) and intervention-associated carry-over phenomena on the power of such studies is crucially important for putting the yield and feasibility of N-of-1 trial designs into context. We evaluated the effect of these phenomena on the power of different designs for N-of-1 trials using analytical theory based on standard likelihood principles assuming an autoregressive lag 1, i.e., AR(1), serial correlation structure among the measurements as well as simulation studies. By evaluating the power to detect effects in many different settings, we show that the influence of serial correlation and heteroscedasticity on power can be substantial, but can also be mitigated to some degree through the use of appropriate multiple evaluation periods. We also show that the detection of certain types of carry-over effects can be heavily influenced by design considerations as well.

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“…However, in future work, it will be a further goal to also separate the dataset on the user level in a reasonable manner. That individual users play an important role in health care studies is emphasized by the emerging paradigm of N-of-1 studies [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

Pryss¹,

Schlee²,

Hoppenstedt³

et al. 2020

J Med Internet Res

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Background Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.

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Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations

Cited by 31 publications

References 15 publications

Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine

Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine

Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

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