Dataset Information

Comparing internal and external validation in the discovery of qualitative treatment-subgroup effects using two small clinical trials.

ABSTRACT: In a two-arm randomized trial where both arms receive active treatment (i.e., treatments A and B), often the primary goal is to determine which of the treatments, on average, is more effective. A supplementary objective is to understand possible heterogeneity in the treatment effect by identifying multivariable subgroups of patients for whom A is more effective than B and, conversely, patients for whom B is more effective than A, known as a qualitative interaction. This is the objective of the qualitative interaction trees (QUINT) algorithm developed by Dusseldorp et al (Statistics in Medicine, 2014). We apply QUINT to a small randomized trial comparing facilitated relaxation meditation to facilitated life completion and preparation among patients with life-limiting illness (n?=?135). We then conduct an internal validation of the QUINT solution using bootstrap resampling and compare it to an external validation with another, similarly conducted small randomized trial. Internal and external validation showed the apparent range in effect sizes was over-estimated, and subgroups identified were not consistent between the two trials. While the qualitative interaction trees algorithm is a promising area of data-driven multivariable subgroup discovery, our analyses illustrate the importance of validating the solution, particularly for trials with smaller numbers of participants.

SUBMITTER: Olsen MK

PROVIDER: S-EPMC6523033 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparing internal and external validation in the discovery of qualitative treatment-subgroup effects using two small clinical trials.

Olsen Maren K MK Stechuchak Karen M KM Steinhauser Karen E KE

Contemporary clinical trials communications 20190428

In a two-arm randomized trial where both arms receive active treatment (i.e., treatments A and B), often the primary goal is to determine which of the treatments, on average, is more effective. A supplementary objective is to understand possible heterogeneity in the treatment effect by identifying multivariable subgroups of patients for whom A is more effective than B and, conversely, patients for whom B is more effective than A, known as a qualitative interaction. This is the objective of the q ...[more]

PMID: 31193216

Similar Datasets

Project description:ObjectivesTo assess aspects of the internal validity of recently published cluster randomised trials and explore the reporting of information useful in assessing the external validity of these trials.DesignReview of 34 cluster randomised trials in primary care published in 2004 and 2005 in seven journals (British Medical Journal, British Journal of General Practice, Family Practice, Preventive Medicine, Annals of Internal Medicine, Journal of General Internal Medicine, Pediatrics).Data sourcesNational Library of Medicine (Medline) via PubMed.Data extractionTo assess aspects of internal validity we extracted data on appropriateness of sample size calculations and analyses, methods of identifying and recruiting individual participants, and blinding. To explore reporting of information useful in assessing external validity we extracted data on cluster eligibility, cluster inclusion and retention, cluster generalisability, and the feasibility and acceptability of the intervention to health providers in clusters.Results21 (62%) trials accounted for clustering in sample size calculations and 30 (88%) in the analysis; about a quarter were potentially biased because of procedures surrounding recruitment and identification of patients; individual participants were blind to allocation status in 19 (56%) and outcome assessors were blind in 15 (44%). In almost half the reports, information relating to generalisability of clusters was poorly reported, and in two fifths there was no information about the feasibility and acceptability of the intervention.ConclusionsCluster randomised trials are essential for evaluating certain types of interventions. Issues affecting their internal validity, such as appropriate sample size calculations and analysis, have been widely disseminated and are now better addressed by researchers. Blinding of those identifying and recruiting patients to allocation status is recommended but is not always carried out. There may be fewer barriers to internal validity in trials in which individual participants are not recruited. External validity seems poorly addressed in many trials, yet is arguably as important as internal validity in judging quality as a basis for healthcare intervention.

Project description:BACKGROUND: Controlled clinical trials of health care interventions are either explanatory or pragmatic. Explanatory trials test whether an intervention is efficacious; that is, whether it can have a beneficial effect in an ideal situation. Pragmatic trials measure effectiveness; they measure the degree of beneficial effect in real clinical practice. In pragmatic trials, a balance between external validity (generalizability of the results) and internal validity (reliability or accuracy of the results) needs to be achieved. The explanatory trial seeks to maximize the internal validity by assuring rigorous control of all variables other than the intervention. The pragmatic trial seeks to maximize external validity to ensure that the results can be generalized. However the danger of pragmatic trials is that internal validity may be overly compromised in the effort to ensure generalizability. We are conducting two pragmatic randomized controlled trials on interventions in the management of hypertension in primary care. We describe the design of the trials and the steps taken to deal with the competing demands of external and internal validity. DISCUSSION: External validity is maximized by having few exclusion criteria and by allowing flexibility in the interpretation of the intervention and in management decisions. Internal validity is maximized by decreasing contamination bias through cluster randomization, and decreasing observer and assessment bias, in these non-blinded trials, through baseline data collection prior to randomization, automating the outcomes assessment with 24 hour ambulatory blood pressure monitors, and blinding the data analysis. SUMMARY: Clinical trials conducted in community practices present investigators with difficult methodological choices related to maintaining a balance between internal validity (reliability of the results) and external validity (generalizability). The attempt to achieve methodological purity can result in clinically meaningless results, while attempting to achieve full generalizability can result in invalid and unreliable results. Achieving a creative tension between the two is crucial.

Project description:ImportanceAcute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, and few prediction models have been externally validated.ObjectiveTo internally and externally validate a machine learning risk score to detect AKI in hospitalized patients.Design, setting, and participantsThis diagnostic study included 495 971 adult hospital admissions at the University of Chicago (UC) from 2008 to 2016 (n = 48 463), at Loyola University Medical Center (LUMC) from 2007 to 2017 (n = 200 613), and at NorthShore University Health System (NUS) from 2006 to 2016 (n = 246 895) with serum creatinine (SCr) measurements. Patients with an SCr concentration at admission greater than 3.0 mg/dL, with a prior diagnostic code for chronic kidney disease stage 4 or higher, or who received kidney replacement therapy within 48 hours of admission were excluded. A simplified version of a previously published gradient boosted machine AKI prediction algorithm was used; it was validated internally among patients at UC and externally among patients at NUS and LUMC.Main outcomes and measuresPrediction of Kidney Disease Improving Global Outcomes SCr-defined stage 2 AKI within a 48-hour interval was the primary outcome. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC).ResultsThe study included 495 971 adult admissions (mean [SD] age, 63 [18] years; 87 689 [17.7%] African American; and 266 866 [53.8%] women) across 3 health systems. The development of stage 2 or higher AKI occurred in 15 664 of 48 463 patients (3.4%) in the UC cohort, 5711 of 200 613 (2.8%) in the LUMC cohort, and 3499 of 246 895 (1.4%) in the NUS cohort. In the UC cohort, 332 patients (0.7%) required kidney replacement therapy compared with 672 patients (0.3%) in the LUMC cohort and 440 patients (0.2%) in the NUS cohort. The AUCs for predicting at least stage 2 AKI in the next 48 hours were 0.86 (95% CI, 0.86-0.86) in the UC cohort, 0.85 (95% CI, 0.84-0.85) in the LUMC cohort, and 0.86 (95% CI, 0.86-0.86) in the NUS cohort. The AUCs for receipt of kidney replacement therapy within 48 hours were 0.96 (95% CI, 0.96-0.96) in the UC cohort, 0.95 (95% CI, 0.94-0.95) in the LUMC cohort, and 0.95 (95% CI, 0.94-0.95) in the NUS cohort. In time-to-event analysis, a probability cutoff of at least 0.057 predicted the onset of stage 2 AKI a median (IQR) of 27 (6.5-93) hours before the eventual doubling in SCr concentrations in the UC cohort, 34.5 (19-85) hours in the NUS cohort, and 39 (19-108) hours in the LUMC cohort.Conclusions and relevanceIn this study, the machine learning algorithm demonstrated excellent discrimination in both internal and external validation, supporting its generalizability and potential as a clinical decision support tool to improve AKI detection and outcomes.

Dataset Information

Comparing internal and external validation in the discovery of qualitative treatment-subgroup effects using two small clinical trials.

Publications

Comparing internal and external validation in the discovery of qualitative treatment-subgroup effects using two small clinical trials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets