Observer bias in randomized clinical trials with measurement scale outcomes: a systematic review of trials with both blinded and nonblinded assessors
Bibliographic record
Abstract
BACKGROUND: Clinical trials are commonly done without blinded outcome assessors despite the risk of bias. We wanted to evaluate the effect of nonblinded outcome assessment on estimated effects in randomized clinical trials with outcomes that involved subjective measurement scales. METHODS: We conducted a systematic review of randomized clinical trials with both blinded and nonblinded assessment of the same measurement scale outcome. We searched PubMed, EMBASE, PsycINFO, CINAHL, Cochrane Central Register of Controlled Trials, HighWire Press and Google Scholar for relevant studies. Two investigators agreed on the inclusion of trials and the outcome scale. For each trial, we calculated the difference in effect size (i.e., standardized mean difference between nonblinded and blinded assessments). A difference in effect size of less than 0 suggested that nonblinded assessors generated more optimistic estimates of effect. We pooled the differences in effect size using inverse variance random-effects meta-analysis and used metaregression to identify potential reasons for variation. RESULTS: We included 24 trials in our review. The main meta-analysis included 16 trials (involving 2854 patients) with subjective outcomes. The estimated treatment effect was more beneficial when based on nonblinded assessors (pooled difference in effect size -0.23 [95% confidence interval (CI) -0.40 to -0.06]). In relative terms, nonblinded assessors exaggerated the pooled effect size by 68% (95% CI 14% to 230%). Heterogeneity was moderate (I(2) = 46%, p = 0.02) and unexplained by metaregression. INTERPRETATION: We provide empirical evidence for observer bias in randomized clinical trials with subjective measurement scale outcomes. A failure to blind assessors of outcomes in such trials results in a high risk of substantial bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.354 | 0.627 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.023 | 0.026 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".