meta 分析素材
1conducting research about previous research
tenet：
common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies
aim：
use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived
effect size： a standard error so that we can proceed with computing a weighted average of that common measure.
However, in performing a meta-analysis, an investigator must make choices which can affect the 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
use "meta-analysis" to refer to statistical methods of combining evidence, leaving other aspects of 'research synthesis' or 'evidence synthesis', such as combining information from qualitative studies, for the more general context of systematic reviews.
2Steps in a meta-analysis[edit]
Formulation of the problem
Search of literature
Selection of studies ('incorporation criteria')
Based on quality criteria, e.g. the requirement of randomization and blinding in a clinical trial
Selection of specific studies on a well-specified subject, e.g. the treatment of breast cancer.
Decide whether unpublished studies are included to avoid publication bias (file drawer problem)
Decide which dependent variables or summary measures are allowed. For instance:
Differences (discrete data)
Means (continuous data)
Hedges' g is a popular summary measure for continuous data that is standardized in order to eliminate scale differences, but it incorporates an index of variation between groups:
δ
μ
t
μ
c
σ
in which
μ
t
is the treatment mean,
μ
c
is the control mean,
σ
2
the pooled variance.
Selection of a meta-regression statistical model: e.g. simple regression, fixed-effect meta-regression or random-effect meta-regression. Meta-regression is a tool used in meta-analysis to examine the impact of moderator variables on study effect size using regression-based techniques. Meta-regression is more effective at this task than are standard regression techniques.
For reporting guidelines, see the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.[28]
3.。。。approaches ：
For example, if treatment A and treatment B were directly compared vs placebo in separate meta-analyses, we can use these two pooled results to get an estimate of the effects of A vs B in an indirect comparison as effect A vs Placebo minus effect B vs Placebo.
IPD evidence？？？？？？？？
IPD evidence represents raw data as collected by the study centers. This distinction has raised the needs for different meta-analytic methods when evidence synthesis is desired, and has led to the development of one-stage and two-stage methods. In one-stage methods the IPD from all studies are modeled simultaneously whilst accounting for the clustering of participants within studies. Two-stage methods first compute summary statistics for AD from each study and then calculate overall statistics as a weighted average of the study statistics. By reducing IPD to AD, two-stage methods can also be applied when IPD is available; this makes them an appealing choice when performing a meta-analysis. Although it is conventionally believed that one-stage and two-stage methods yield similar results, recent studies have shown that they may occasionally lead to different conclusions.[29]
tenet：
common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies
aim：
use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived
effect size： a standard error so that we can proceed with computing a weighted average of that common measure.
However, in performing a meta-analysis, an investigator must make choices which can affect the 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
use "meta-analysis" to refer to statistical methods of combining evidence, leaving other aspects of 'research synthesis' or 'evidence synthesis', such as combining information from qualitative studies, for the more general context of systematic reviews.
2Steps in a meta-analysis[edit]
Formulation of the problem
Search of literature
Selection of studies ('incorporation criteria')
Based on quality criteria, e.g. the requirement of randomization and blinding in a clinical trial
Selection of specific studies on a well-specified subject, e.g. the treatment of breast cancer.
Decide whether unpublished studies are included to avoid publication bias (file drawer problem)
Decide which dependent variables or summary measures are allowed. For instance:
Differences (discrete data)
Means (continuous data)
Hedges' g is a popular summary measure for continuous data that is standardized in order to eliminate scale differences, but it incorporates an index of variation between groups:
δ
μ
t
μ
c
σ
in which
μ
t
is the treatment mean,
μ
c
is the control mean,
σ
2
the pooled variance.
Selection of a meta-regression statistical model: e.g. simple regression, fixed-effect meta-regression or random-effect meta-regression. Meta-regression is a tool used in meta-analysis to examine the impact of moderator variables on study effect size using regression-based techniques. Meta-regression is more effective at this task than are standard regression techniques.
For reporting guidelines, see the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.[28]
3.。。。approaches ：
For example, if treatment A and treatment B were directly compared vs placebo in separate meta-analyses, we can use these two pooled results to get an estimate of the effects of A vs B in an indirect comparison as effect A vs Placebo minus effect B vs Placebo.
IPD evidence？？？？？？？？
IPD evidence represents raw data as collected by the study centers. This distinction has raised the needs for different meta-analytic methods when evidence synthesis is desired, and has led to the development of one-stage and two-stage methods. In one-stage methods the IPD from all studies are modeled simultaneously whilst accounting for the clustering of participants within studies. Two-stage methods first compute summary statistics for AD from each study and then calculate overall statistics as a weighted average of the study statistics. By reducing IPD to AD, two-stage methods can also be applied when IPD is available; this makes them an appealing choice when performing a meta-analysis. Although it is conventionally believed that one-stage and two-stage methods yield similar results, recent studies have shown that they may occasionally lead to different conclusions.[29]
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