Simon Thornley Meta-analysis: pooling study results

Simon Thornley

Meta-analysis: pooling study results

Objective• Understand the philosophy of meta-analysis and its

contribution to epidemiology and science.

• Understand the limitations of meta-analysis

Introduction• Systematic quantitative integration of results several

independent studies

• Distinct from a narrative review “expert”

• Synthesis of published information.

• Usually considered only appropriate for RCTs

• Still controversial even in this context.

• Google search on “meta-analysis” 8 million hits!

Criticism • “statistical alchemy” for the 21st Century

• “The intellectual allure of making mathematical models and aggregating collections of studies has been used as an escape from the more fundamental scientific challenges”

• -Feinstein.

Purposes of meta analysis• Inefficiency of traditional narrative reviews.

• Allow researchers to keep abreast of accumulating evidence

• Resolution of uncertainty when research disagrees?

• Increase statistical power, enhances precision of effect estimates – especially small effects

• Allows exploratory analysis (subgroups)

Inadequate sample size? (Deal with type-2 error)• Single trials too small to detect moderate effects

• (low power – high chance of Type-2 error (false-negative))

• Investigators often over enthusiastic about size of treatment effects and sample size

• Meta-analysis doesn’t deal with other threats to study validity (bias, measurement error; in fact, may increase)

• e.g. CVD death vs. total mortality

Accept H0 Reject H0

Statistical Test result

H0

True

False

OK

OK

Type-1 error

Type-2 error

Prob of a type 1 error = alpha a (usually fixed, say 0.05)Prob of a type 2 error = beta b= 1-power

Random error lecture

Average odds ratio is 21?? Consistency??

Which studies?• Need defined question, state MESH terms• Reproducible• Exhaustive search• Unpublished and published studies• Variety of databases.

• Difference in means,

• Standardized differences in means

• Survival measures

• Relative risk • Odds ratio• Risk difference• NNT [=1/RD]• Incidence rate ratios

(person time data)

Typical summary outcome measuresBinary: Continuous:

• Assume distribution of true effects

• Aim is to measure mean of distribution of true effects

• Greater heterogeneity --> greater variation

• Gives greater weight to small studies than fixed effect method of analysis.

• More conservative (wider confidence interval around effect estimate, compared to fixed effect method)

• Mantel-Haenszel method

• treat each trial as a “stratum” take weighted average of effects.

• O-E (Peto) method

• Binary outcome (e.g. death)

• Oi =observed # deaths on treatment in trial i

• Ei=expected # deaths (assuming no treat effect)

• look at average of Oi - Ei over all trials

• Assumes underlying true effect for each study and differences only due to random error

Methods of analysisFixed effect Random effect

Dietary fat and cholesterol

Reduced or modified dietary fat and all-cause mortality

Publication bias

When meta-analysis goes bad…• In CVD drug research, CVD outcomes

often favoured over total mortality• Which would you prefer????

Publication bias: other methods • Ioannidis JPA, Trikalinos TA. An exploratory test for an

excess of significant findings. Clin. Trials 2007;4(3):245-53.

• Calculate expected number of positive studies, given:

• Sample size of individual studies

• Number of events in controls

• Summary effect (assumed true)

Statin meta-analysis

Problems• Combining heterogeneous studies (apples and oranges)

• Combining good and bad studies (good and bad apples) (study quality)

• Publication bias (tasty apples only)

• The "Flat Earth" criticism – reductionism –(Braeburns only)

• Combining data (individual v summary data stewed apples have different character to raw)

• Application to randomized studies only?

• Type-2 error only one problem with epi studies

Meta analysis in observational studies• MA often applied in observational studies

• As often as RCTs (Egger et al)

• …. with controversy ….• Confounding and bias unlikely to “cancel out”

• Publication bias and “research initiation bias” (i.e. studies only done when there is an association)

• Different ways of reporting/analysing result (e.g different outcome measures, confounders, models, exposure levels)

Summary• Meta-analyses increasingly used

• Logical only for RCTs?

• Summarise medical literature

• Reduce type-2 error by increasing sample size.

• Don’t deal with other types of epidemiological error (confounding/measurement error)

• Prone to unique type of error (Publication bias)

• Can be difficult to detect