Limits...
Bias modelling in evidence synthesis.

Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG - J R Stat Soc Ser A Stat Soc (2009)

Bottom Line: The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies.Adjustment had the effect of shifting the combined estimate away from the by approximately 10%, and the variance of the combined estimate was almost tripled.Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.

View Article: PubMed Central - PubMed

ABSTRACT
Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias-adjusted meta-analysis. Adjustment had the effect of shifting the combined estimate away from the by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.

No MeSH data available.


Related in: MedlinePlus

Elicitation scale for quantifying additive bias
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2667303&req=5

fig02: Elicitation scale for quantifying additive bias

Mentions: The assessor is given a copy of an elicitation scale (Fig. 2) for each bias and asked to mark a 67% range for the relative risk of the adverse outcome chosen in that study such that they feel that the true answer to the question is twice as likely to lie inside rather than outside this range. Our decision to elicit an interval corresponding to moderate rather than high probability is based on findings that peoples’ performance at assessing intervals tends to be better for lower levels of certainty (O’Hagan et al., 2006).


Bias modelling in evidence synthesis.

Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG - J R Stat Soc Ser A Stat Soc (2009)

Elicitation scale for quantifying additive bias
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2667303&req=5

fig02: Elicitation scale for quantifying additive bias
Mentions: The assessor is given a copy of an elicitation scale (Fig. 2) for each bias and asked to mark a 67% range for the relative risk of the adverse outcome chosen in that study such that they feel that the true answer to the question is twice as likely to lie inside rather than outside this range. Our decision to elicit an interval corresponding to moderate rather than high probability is based on findings that peoples’ performance at assessing intervals tends to be better for lower levels of certainty (O’Hagan et al., 2006).

Bottom Line: The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies.Adjustment had the effect of shifting the combined estimate away from the by approximately 10%, and the variance of the combined estimate was almost tripled.Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.

View Article: PubMed Central - PubMed

ABSTRACT
Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias-adjusted meta-analysis. Adjustment had the effect of shifting the combined estimate away from the by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.

No MeSH data available.


Related in: MedlinePlus