META ANALYSIS DINESH CHAURASIYA

M.Sc BIOSTATISTICS and EPIDEMIOLOGY

META ANALYSIS• Analysis of analyses • Meta-Analysis is the process of using statistical methods to combine the

results of different studies.• It involves the systematic, organized, and structured evaluation of a

problem of interest using information (commonly in the form of statistical tables or other data) from a number of independent studies of the problem.

• The motivation of a meta-analysis is to aggregate information in order to achieve a higher statistical power for the measure of interest, as opposed to a less precise measure derived from a single study.

• In performing a meta-analysis, an investigator must make choices many of which can affect its results, including deciding how to search for studies, selecting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and accounting for or choosing not to account for publication bias.

Steps in a meta-analysis

1. Formulation of the problem2. Identify studies with relevant data 3. Define inclusion criteria for studies4. Statistical Analysis

Formulation of the problem

1. Specify the Research Objectives2. Review the Environment or Context of the

Research Problem3. Explore the Nature of the Problem4. Define the Variable Relationships

Identify studies with relevant data

• Meta-analysis requires a comprehensive search strategy which interrogates several electronic databases (for example, MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials).

• Hand searching of key journals and checking of the reference lists of papers obtained is also recommended.

• The search strategy the key terms used to search the database needs to be developed with care.

Define inclusion criteria for studies

Should be determined in advance, to reduceinvestigator bias.Inclusion criteria involves• Types of studies included (case control, cohort, etc)• Years of publication covered• Languages• Restrictions on sample size• Definition of disease, exposures• Confounders that must be measured• Dose response categories similar

Statistical Analysis

Two types of models are used to producesummary effect measures

• Fixed Effect Model• Random Effects Model

Fixed Effects Model

• In a fixed effect model, we assume that the effect sizes in our meta-analysis differ only because of sampling error and they all share a common mean.

• Our effect sizes differ from each other because each study used a different sample of participants – and that is the only reason for the differences among our estimates

• The fixed effect model provides a weighted average of a series of study estimates.

• The inverse of the estimate’s variance is commonly used as study weight, such that larger studies tend to contribute more than smaller studies to the weighted average.

• When studies within a meta-analysis are dominated by a very large study, the findings from smaller studies are practically ignored . Most importantly, the fixed effects model assumes that all included studies investigate the same population, use the same variable and outcome definitions, etc.

Random effects model• In a random effects model, we assume two components of

variation: –  Sampling variation as in our fixed-effect model assumption.–  Random variation because the effect sizes themselves are sampled from a population of effect size.

• In a random effects model, we know that our effect sizes will differ because they are sampled from an unknown distribution.

• Our goal in the analysis will be to estimate the mean and variance of the underlying population of effect sizes .

Advantages• Results can be generalized to a larger population.• The precision and accuracy of estimates can be improved as more

data is used. This, in turn may increase the statistical power to detect an effect.

• Inconsistency of results across studies can be quantified and analyzed. For instance, does inconsistency arise from sampling error, or are study results (partially) influenced by between study heterogeneity.

• Hypothesis testing can be applied on summary estimates.• Moderators can be included to explain variation between

studies.• The presence of publication bias can be investigated.

Limitation• Meta-analysis has a high chance of being perceptable

to publication bias, this is where the researcher collecting the data will pick specific studies that only provide the outcome that the researcher is looking for.

• Publication bias here can be done purposefully to manipulate results or by accident through unconsciously knowing that it is being done as they are unaware that they are looking for a certain outcome.

• Poor designs are also mixed with good ones which can skew the statistical result.

Overweight, Obesity, and Incident AsthmaA Meta-analysis of Prospective Epidemiologic Studies

• AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 175 2007

• Beuther and Sutherland: Obesity and Asthma Incidence

Objective of study

• To quantify the relationship between categories of body mass index (BMI) and incident asthma in adults and to evaluate the impact of sex on this relationship.

Studies with relevant data• Targeted studies were those in which the relationship

between BMI and incident asthma was evaluated.• MEDLINE• Cumulative Index to Nursing and Allied Health Literature• International Pharmaceutical Abstracts• all Evidence-Based Medicine Reviews (EBMR) (Cochrane

Database of Systematic Reviews, ACP Journal Club, Database of Abstracts of Reviews of Effects, and Cochrane Central Register of Controlled Trials)

• A date range of 1966 to May 2006, crossing keywords

• overweight and asthma• Obesity and asthma • body mass index and asthma• body weight and asthma• anthropometry and asthma• The systematic search yielded 2,006 total

references of which 1,569 were unique.

Inclusion criteria

Predetermined inclusion criteria included • (1) adult subjects, • (2) primary outcome of incident asthma, • (3) use of BMI as a measure of overweight or

obesity, • (4) minimum 1-year follow-up, • (5) follow-up of at least 70%, and • (6) data that could be categorized by standard

ranges of BMI obtained at study inception.

Statistical Analysis

• Stata 7.0 was used to generate summary ORs using inverse variance-weighted randomeffects meta-analysis.

• Random effects methodology was chosen to account for within-study and between-study variation.

• Heterogeneity of data was evaluated using the Q statistic .

• Summary ORs were represented as a point estimate and 95% confidence intervals in aggregate and stratified by sex.

ResultTotal Men Women

Comparison OR (95% CI ) OR (95% CI ) OR (95% CI )

Overweight vs. normal BMI

1.38 (1.17–1.62) 1.44 (1.01–2.04) 1.42 (1.18–1.72)

Obese vs. normal BMI

1.92 (1.43–2.59) 1.63 (0.92–2.89) 2.30 (1.88–2.82)

Overweight and obese (BMI 25) vs. normal BMI

1.51 (1.27–1.80) 1.46 (1.05–2.02) 1.68 (1.45–1.94)

Obese vs. overweight

1.49 (1.20–1.85) 1.17 (0.66–2.07) 1.58 (1.25–1.99)

Limitation • Many of the studies included in this meta-analysis relied on

self reported, these reports cast some doubt on the validity of self-reported asthma in large epidemiologic studies. It is reasonable to believe that some of these patients with “asthma” may have respiratory symptoms due to obesity but may not meet rigorous objective physiologic criteria for asthma .

• It is possible that asthma may be over diagnosed in an obese population.

• The calculated ORs may have been underestimated due to the grouping together of underweight and normal weight subjects.

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