Improving the reporting and interpretation of clinical trial outcomes

Froud, Robert; Underwood, Martin; Eldridge, Sandra

doi:10.3399/bjgp12x657008

Cited by 11 publications

(9 citation statements)

References 14 publications

(4 reference statements)

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“…We note that when conducting responder analyses the minimal detectable change of the outcome measure must also be examined, but we consider a detailed discussion of this to be outside the scope of this paper and on this point refer the reader to other material. [5] , [8] …”

Section: Considerationsmentioning

confidence: 99%

“…MIC is usually defined as smallest magnitude of change that can be considered important (at the level of the individual), and in the absence of troublesome side-effects and excessive costs, mandates a change in a patient's management. [4] , [5] In 1986, Deyo and Centor suggested that PROMs could be viewed as diagnostic tests; in the sense that they can be thought to be diagnostic of improvement. [6] Using an external criterion – for example a health transition question or an accepted gold standard diagnostic test for improvement – continuous, or quasi-continuous, PROM change scores can be plotted on a ROC curve, facilitating a choice of cut-point associated with optimal sensitivity and specificity.…”

Section: Introductionmentioning

confidence: 99%

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Using ROC Curves to Choose Minimally Important Change Thresholds when Sensitivity and Specificity Are Valued Equally: The Forgotten Lesson of Pythagoras. Theoretical Considerations and an Example Application of Change in Health Status

Froud

Abel

2014

PLoS ONE

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BackgroundReceiver Operator Characteristic (ROC) curves are being used to identify Minimally Important Change (MIC) thresholds on scales that measure a change in health status. In quasi-continuous patient reported outcome measures, such as those that measure changes in chronic diseases with variable clinical trajectories, sensitivity and specificity are often valued equally. Notwithstanding methodologists agreeing that these should be valued equally, different approaches have been taken to estimating MIC thresholds using ROC curves.Aims and objectivesWe aimed to compare the different approaches used with a new approach, exploring the extent to which the methods choose different thresholds, and considering the effect of differences on conclusions in responder analyses.MethodsUsing graphical methods, hypothetical data, and data from a large randomised controlled trial of manual therapy for low back pain, we compared two existing approaches with a new approach that is based on the addition of the sums of squares of 1-sensitivity and 1-specificity.ResultsThere can be divergence in the thresholds chosen by different estimators. The cut-point selected by different estimators is dependent on the relationship between the cut-points in ROC space and the different contours described by the estimators. In particular, asymmetry and the number of possible cut-points affects threshold selection.ConclusionChoice of MIC estimator is important. Different methods for choosing cut-points can lead to materially different MIC thresholds and thus affect results of responder analyses and trial conclusions. An estimator based on the smallest sum of squares of 1-sensitivity and 1-specificity is preferable when sensitivity and specificity are valued equally. Unlike other methods currently in use, the cut-point chosen by the sum of squares method always and efficiently chooses the cut-point closest to the top-left corner of ROC space, regardless of the shape of the ROC curve.

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Section: Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using ROC Curves to Choose Minimally Important Change Thresholds when Sensitivity and Specificity Are Valued Equally: The Forgotten Lesson of Pythagoras. Theoretical Considerations and an Example Application of Change in Health Status

Froud

Abel

2014

PLoS ONE

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“…[17] We emphasise that our MIC thresholds relate to the degree of change may be considered important for an individual, and not what degree of difference may be considered important at a population-level. [15,74,75] We note that negative confidence intervals imply consistency of the data with the true MIC thresholds in the opposite direction. This is likely an artifact of low power and we suggest inflated sample sizes below for future studies based on the bootstrapped standard error observations.…”

Section: Comparison With Prior Workmentioning

confidence: 94%

“…[11][12][13] To do this it is necessary to know both (1) the minimum thresholds considered important to an individual participant -the minimally important change (MIC); and (2) what magnitudes of change can be detected beyond the inherent measurement error of the instrument -the minimal detectable change (MDC). [14,15] These thresholds may be population specific. [16,17] We aimed to determine reliability and responsiveness, MIC and MDC for electronic versions of the VAS, RMDQ, and NRS as administered to people with low back pain, using a web browser, Android or iOS app on their own computers, smart phones, or tablets.…”

Section: Introductionmentioning

confidence: 99%

Responsiveness, Reliability, and Minimally Important and Minimal Detectable Changes of 3 Electronic Patient-Reported Outcome Measures for Low Back Pain: Validation Study (Preprint)

Froud¹,

Fawkes²,

Foss³

et al. 2018

Preprint

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“…Despite the availability and implementation of these well-established reporting guidelines for trials, significant concerns regarding the quality of the reporting of trial outcomes remain [13][14][15][16][17][18]. In the context of a clinical trial, an outcome refers to what is being measured on trial participants to examine the effect of exposure to a health intervention [19].…”

Section: Introductionmentioning

confidence: 99%

Improving outcome reporting in clinical trial reports and protocols: study protocol for the Instrument for reporting Planned Endpoints in Clinical Trials (InsPECT)

Butcher¹,

Monsour²

2018

Preprint

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Background: Inadequate and poor quality outcome reporting in clinical trials is a well-documented problem that impedes the ability of researchers to evaluate, replicate, synthesize and build upon study findings and impacts evidence-based decision making by patients, clinicians, and policy makers. To facilitate harmonized and transparent reporting of outcomes in trial protocols and published reports, the Instrument for reporting Planned Endpoints in Clinical Trials (InsPECT), is being developed. The final product will provide unique InsPECT Extensions to the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines.Methods: The InsPECT CONSORT and SPIRIT Extensions will be developed in accordance with the methodological framework created by the EQUATOR (Enhancing Quality and Transparency of Health Research Quality) Network for reporting guideline development. Development will consist of: (1) the creation of an initial list of candidate outcome reporting items synthesized from expert consultations and a scoping review of existing guidance for reporting outcomes in trial protocols and reports; (2) a three-round international Delphi study to identify additional candidate items and assess candidate item importance on a nine-point Likert scale, completed by trial report and protocol authors, systematic review authors, biostatisticians and epidemiologists, reporting guideline developers, clinicians, journal editors, and research ethics board representatives; and (3) an in-person expert consensus meeting to finalize the set of essential outcome reporting items for trial protocols and reports, respectively. The consensus meeting discussions will be independently facilitated and informed by the empirical evidence identified in the primary literature and through the opinions (aggregate rankings and comments) collected via the Delphi study. An integrated knowledge translation approach will be used throughout InsPECT development to facilitate implementation and dissemination, in addition to standard post-development activities. Discussion: InsPECT will provide evidence-informed and consensus-based standards focused on outcome reporting in clinical trials that can be applied across diverse disease areas, study populations, and outcomes. InsPECT will support the standardization of trial outcome reporting, which will maximize trial usability, reduce bias, foster trial replication, improve trial design and execution, and ultimately reduce research waste and help improve patient outcomes.

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Improving the reporting and interpretation of clinical trial outcomes

Cited by 11 publications

References 14 publications

Using ROC Curves to Choose Minimally Important Change Thresholds when Sensitivity and Specificity Are Valued Equally: The Forgotten Lesson of Pythagoras. Theoretical Considerations and an Example Application of Change in Health Status

Using ROC Curves to Choose Minimally Important Change Thresholds when Sensitivity and Specificity Are Valued Equally: The Forgotten Lesson of Pythagoras. Theoretical Considerations and an Example Application of Change in Health Status

Responsiveness, Reliability, and Minimally Important and Minimal Detectable Changes of 3 Electronic Patient-Reported Outcome Measures for Low Back Pain: Validation Study (Preprint)

Improving outcome reporting in clinical trial reports and protocols: study protocol for the Instrument for reporting Planned Endpoints in Clinical Trials (InsPECT)

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