Baseline differences in the SAVOR trial

Assessing risk of bias: a proposal for a unified framework for observational studies and randomized trials

BACKGROUND

Evidence based medicine aims to integrate scientific evidence, clinical experience, and patient values and preferences. Individual health care professionals need to appraise the evidence from randomized trials and observational studies when guidelines are not yet available. To date, tools for assessment of bias and terminologies for bias are specific for each study design. Moreover, most tools appeal only to methodological knowledge to detect bias, not to subject matter knowledge, i.e. in-depth medical knowledge about a topic. We propose a unified framework that enables the coherent assessment of bias across designs.

METHODS

Epidemiologists traditionally distinguish between three types of bias in observational studies: confounding, information bias, and selection bias. These biases result from a common cause, systematic error in the measurement or common effect of the intervention and outcome respectively. We applied this conceptual framework to randomized trials and show how it can be used to identify bias. The three sources of bias were illustrated with graphs that visually represent researchers' assumptions about the relationships between the investigated variables (causal diagrams).

RESULTS

Critical appraisal of evidence started with the definition of the research question in terms of the population of interest, the compared interventions and the main outcome. Next, we used causal diagrams to illustrate how each source of bias can lead to over- or underestimated treatment effects. Then, we discussed how randomization, blinded outcome measurement and intention-to-treat analysis minimize bias in trials. Finally, we identified study aspects that can only be appraised with subject matter knowledge, irrespective of study design.

CONCLUSIONS

The unified framework encompassed the three main sources of bias for the effect of an assigned intervention on an outcome. It facilitated the integration of methodological and subject matter knowledge in the assessment of bias. We hope that graphical diagrams will help clarify debate among professionals by reducing misunderstandings based on different terminology for bias.

Baseline imbalances and clinical outcomes of atypical antipsychotics in dementia: A meta-epidemiological study of randomized trials

OBJECTIVES

To assess baseline imbalances in placebo-controlled trials of atypical antipsychotics in dementia, and their association with neuropsychiatric symptoms (NPS), extrapyramidal symptoms (EPS), and mortality.

METHOD

We searched for trials in multiple sources. Two reviewers extracted baseline characteristics and outcomes per treatment group. We calculated direction, range, pooled mean, and heterogeneity in the baseline differences, and used meta-regression for the relationship with the outcomes.

RESULTS

We identified 23 trials. Baseline type of dementia, cognitive impairment and NPS were poorly reported. The drug group had a higher mean age than the placebo group in nine trials and lower mean age in three trials (p = 0.073). The difference in percentage men between the drug and placebo group ranged from -9.7% to 4.4%. There were no statistically significant pooled baseline differences, but heterogeneity was present for age. Higher mean age at baseline in the drug versus placebo group was significantly associated with greater reduction in NPS, and higher percentage of non-White persons with lower risk of EPS. Imbalances were not significantly associated with risk of mortality.

CONCLUSION

Randomized trials of atypical antipsychotics in dementia showed baseline imbalances that were associated with higher efficacy and lower risk of EPS for atypical antipsychotics versus placebo.