Are Biases Related to Attrition, Missing Data, and the Use of Intention to Treat Related to the Magnitude of Treatment Effects in Physical Therapy Trials?

Abstract

The objective of this study was to determine the association between biases related to attrition, missing data, and the use of intention to treat and changes in effect size estimates in physical therapy randomized trials. A meta-epidemiological study was conducted. A random sample of randomized controlled trials included in meta-analyses in the physical therapy discipline were identified. Data extraction including assessments of the use of intention to treat principle, attrition-related bias, and missing data was conducted independently by two reviewers. To determine the association between these methodological issues and effect sizes, a two-level analysis was conducted using a meta-meta-analytic approach. Three hundred ninety-three trials included in 43 meta-analyses, analyzing 44,622 patients contributed to this study. Trials that did not use the intention-to-treat principle (effect size = −0.13, 95% confidence interval = −0.26 to 0.01) or that were assessed as having inappropriate control of incomplete outcome data tended to underestimate the treatment effect when compared with trials with adequate use of intention to treat and control of incomplete outcome data (effect size = −0.18, 95% confidence interval = −0.29 to −0.08).Researchers and clinicians should pay attention to these methodological issues because they could provide inaccurate effect estimates. Authors and editors should make sure that intention-to-treat and missing data are properly reported in trial reports.

The influence of bias in randomized controlled trials on rehabilitation intervention effect estimates: what we have learned from meta-epidemiological studies

“…We included seven eligible meta-epidemiological studies (included data on 227,806 participants) 21 - 27 in a rehabilitation context. Armijo-Olivo and De Almeida 23 , 24 addressed the influence of sequence generation and allocation concealment on treatment effect, Armijo-Olivo and Liu 21 , 27 on blinding of participants, clinicians and assessors, Armijo-Olivo 22 on attrition bias, Hayden 26 on reporting bias and Fuentes 25 on sponsorship bias. The rehabilitation interventions considered were physical therapy (PT).…”

“…Two studies 21 , 23 evaluated the association between attrition bias and/or intention-to-treat (ITT) analysis. Armijo-Olivo 22 including 43 meta-analysis, 393 RCTs with 44,622 participants, evaluated the association between attrition bias and treatment effect estimates when evaluating physical therapy interventions using continuous outcomes. This study 22 showed that trials which did not use the ITT principle, or which were assessed as having inappropriate control of incomplete outcome data, tended to underestimate the effect estimates when compared with trials with adequate use of ITT (ES difference: -0.13; 95% CI -0.26 to 0.01) and control of incomplete outcome data (ES difference: -0.18; 95% CI -0.29 to -0.08).…”

Selection, Confounding, and Attrition Biases in Randomized Controlled Trials of Rehabilitation Interventions

“…Significant beneficial effects were found until 20% missing data as a cutoff. 47 The overall findings described suggest that attrition, missing data, and related biases influence TEE in rehabilitation trials.…”

“…However, other studies show an opposite direction. 47 Therefore, these results should be taken into consideration when designing, conducting, and reporting trials in the rehabilitation field.…”

Impact of trial attrition rates on treatment effect estimates in chronic inflammatory diseases: A meta‐epidemiological study

“…Some studies have reported that attrition bias and exclusion of patients from analyses tend to result in a more beneficial effect of the intervention, 4 while others find inconclusive evidence [5][6][7] or even a tendency to underestimate the treatment effect. 8 More evidence on the impact of attrition (absolute and differential) on bias in randomised trials is needed. This information would benefit the design of new studies and be useful for clinicians interpreting trial outcomes.…”