Failure to address potential bias in non-randomised controlled clinical trials may cause lack of evidence on patient-reported outcomes: a method study

Peinemann, Frank; Labeit, Alexander; Thielscher, Christian; Pinkawa, Michael

doi:10.1136/bmjopen-2013-004720

Cited by 10 publications

(8 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, by including only patients who agreed to use mHealth technology, we acknowledge a significant methodological flaw, a selection or Berksonian bias that is often inherent in studying digital interventions—a phenomenon that arises when the sample is taken not from the general population but from a preselected subpopulation [ 33 ]. Patients who agree to participate are generally more motivated, have greater self-efficacy, are more literate, and have a variety of other attributes that make it likely that their outcomes would be better than nonagreeing patients [ 34 - 37 ].…”

Section: Discussionmentioning

confidence: 99%

Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes: Retrospective Observational Cohort Study

Bruce

Harrison

Nisar

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Despite the growth of and media hype about mobile health (mHealth), there is a paucity of literature supporting the effectiveness of widespread implementation of mHealth technologies. Objective This study aimed to assess whether an innovative mHealth technology system with several overlapping purposes can impact (1) clinical outcomes (ie, readmission rates, revisit rates, and length of stay) and (2) patient-centered care outcomes (ie, patient engagement, patient experience, and patient satisfaction). Methods We compared all patients (2059 patients) of participating orthopedic surgeons using mHealth technology with all patients of nonparticipating orthopedic surgeons (2554 patients). The analyses included Wilcoxon rank-sum tests, Kruskal-Wallis tests for continuous variables, and chi-square tests for categorical variables. Logistic regression models were performed on categorical outcomes and a gamma-distributed model for continuous variables. All models were adjusted for patient demographics and comorbidities. Results The inpatient readmission rates for the nonparticipating group when compared with the participating group were higher and demonstrated higher odds ratios (ORs) for 30-day inpatient readmissions (nonparticipating group 106/2636, 4.02% and participating group 54/2048, 2.64%; OR 1.48, 95% CI 1.03 to 2.13; P=.04), 60-day inpatient readmissions (nonparticipating group 194/2636, 7.36% and participating group 85/2048, 4.15%; OR 1.79, 95% CI 1.32 to 2.39; P<.001), and 90-day inpatient readmissions (nonparticipating group 261/2636, 9.90% and participating group 115/2048, 5.62%; OR 1.81, 95% CI 1.40 to 2.34; P<.001). The length of stay for the nonparticipating cohort was longer at 1.90 days, whereas the length of stay for the participating cohort was 1.50 days (mean 1.87, SD 2 vs mean 1.50, SD 1.37; P<.001). Patients treated by participating surgeons received and read text messages using mHealth 83% of the time and read emails 84% of the time. Patients responded to 60% of the text messages and 53% of the email surveys. Patients were least responsive to digital monitoring questions when the hospital asked them to do something, and they were most engaged with emails that did not require action, including informational content. A total of 96% (558/580) of patients indicated high satisfaction with using mHealth technology to support their care. Only 0.40% (75/2059) patients opted-out of the mHealth technology program after enrollment. Conclusions A novel, multicomponent, pathway-driven, patient-facing mHealth technology can positively impact patient outcomes and patient-reported experiences. These technologies can empower patients to play a more active and meaningful role in improving their outcomes. There is a deep need, however, for a better understanding of the interactions between patients, technology, and health care providers. Future research is needed to (1) help identify, address, and improve technology usability and effectiveness; (2) understand patient and provider attributes that support adoption, uptake, and sustainability; and (3) understand the factors that contribute to barriers of technology adoption and how best to overcome them.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes: Retrospective Observational Cohort Study

Bruce

Harrison

Nisar

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…The conjoint use of PS‐based methods and MLR (doubly robust approach) rather than their mutually exclusive implementation could therefore be a better choice for evaluating ATT in nonrandomized HRQoL studies, by improving the comparability between groups …”

Section: Introductionmentioning

confidence: 99%

“…† The conjoint use of PS-based methods and MLR (doubly robust approach) rather than their mutually exclusive implementation could therefore be a better choice for evaluating ATT in nonrandomized HRQoL studies, 5,16,17 by improving the comparability between groups. 18,19 In this work, we aim to compare optimal pair matching (OPM), optimal full matching (OFM), SBC, and IPTW both on their own and in combination with MLR, by investigating their performances in estimating ATT on HRQoL outcomes in a simulation study. We will not consider using PS as a variable in linear regression, because this approach is generally not recommended by previous literature.…”

Section: Introductionmentioning

confidence: 99%

Propensity score methods and regression adjustment for analysis of nonrandomized studies with health‐related quality of life outcomes

Cottone

Anota

Bonnetain

et al. 2019

Pharmacoepidemiology and Drug

View full text Add to dashboard Cite

Purpose The aim of this study was to investigate the potential added value of combining propensity score (PS) methods with multivariable linear regression (MLR) in estimating the average treatment effect on the treated (ATT) in nonrandomized studies with health‐related quality of life (HRQoL) outcomes. Methods We first used simulations to compare the performances of different PS‐based methods, either alone or in combination with further MLR adjustment, in estimating ATT. PS methods were, respectively, optimal pair (OPM) and full (OFM) PS matching, subclassification on the PS (SBC), and the inverse probability of treatment weighting (IPTW). We simulated several scenarios, according to different sample sizes, proportions of treated vs untreated subjects, and types of HRQoL outcomes. We also applied the same methods to a real clinical data set. Results OPM and IPTW provided the closest Type I error to the nominal threshold α = 0.05 across all scenarios. Overall, both methods showed also lower variability in estimates than SBC and OFM. SBC performed worst, generally providing the highest levels of bias. Further MLR adjustment lessened bias for all methods, however providing higher Type I error for SBC and OFM. In the real case, all methods provided similar ATT estimates except for one outcome. Conclusions Our findings suggest that for sample sizes up to n = 200, OPM and IPTW are to be preferred to OFM and SBC in estimating ATT on HRQoL outcomes. Specifically, OPM performed best in sample sizes of n ≥ 80, IPTW for smaller sample sizes. Additional MLR adjustment can further improve ATT estimates.

show abstract

“…In assessing the results of non-randomised studies, a number of issues will need consideration, including the sources of bias in the selection of participants, confounding effects of variability in baseline characteristics, issues relating to potential bias in participant-reported outcomes and the effects of attrition bias. 77–80 …”

Section: Discussionmentioning

confidence: 99%

Effectiveness of acupuncture for cancer pain: protocol for an umbrella review and meta-analyses of controlled trials

Liu

May

et al. 2017

BMJ Open

View full text Add to dashboard Cite

Introduction The National Comprehensive Cancer Network guidelines for adult cancer pain indicate that acupuncture and related therapies may be valuable additions to pharmacological interventions for pain management. Of the systematic reviews related to this topic, some concluded that acupuncture was promising for alleviating cancer pain, while others argued that the evidence was insufficient to support its effectiveness.Methods and analysisThis review will consist of three components: (1) synthesis of findings from existing systematic reviews; (2) updated meta-analyses of randomised clinical trials and (3) analyses of results of other types of clinical studies. We will search six English and four Chinese biomedical databases, dissertations and grey literature to identify systematic reviews and primary clinical studies. Two reviewers will screen results of the literature searches independently to identify included reviews and studies. Data from included articles will be abstracted for assessment, analysis and summary. Two assessors will appraise the quality of systematic reviews using Assessment of Multiple Systematic Reviews; assess the randomised controlled trials using the Cochrane Collaboration’s risk of bias tool and other types of studies according to the Newcastle-Ottawa Scale. We will use ‘summary of evidence’ tables to present evidence from existing systematic reviews and meta-analyses. Using the primary clinical studies, we will conduct meta-analysis for each outcome, by grouping studies based on the type of acupuncture, the comparator and the specific type of pain. Sensitivity analyses are planned according to clinical factors, acupuncture method, methodological characteristics and presence of statistical heterogeneity as applicable. For the non-randomised studies, we will tabulate the characteristics, outcome measures and the reported results of each study. Consistencies and inconsistencies in evidence will be investigated and discussed. Finally, we will use the Grading of Recommendations Assessment, Development and Evaluation approach to evaluate the quality of the overall evidence.Ethics and disseminationThere are no ethical considerations associated with this review. The findings will be disseminated in peer-reviewed journals or conference presentations.PROSPERO registration numberCRD42017064113.

show abstract

Failure to address potential bias in non-randomised controlled clinical trials may cause lack of evidence on patient-reported outcomes: a method study

Cited by 10 publications

References 76 publications

Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes: Retrospective Observational Cohort Study

Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes: Retrospective Observational Cohort Study

Propensity score methods and regression adjustment for analysis of nonrandomized studies with health‐related quality of life outcomes

Effectiveness of acupuncture for cancer pain: protocol for an umbrella review and meta-analyses of controlled trials

Contact Info

Product

Resources

About