第二个问题，数据源找不到，命令也不是很熟悉。
没事，11版本以上提供了数据源的链接，很棒。
首先点开软件，然后再COMMAND窗口输入
HELP MIDAS
这样她 会贴出所有的命令，和数据源的链接。
显示：
-------------------------------------------------------------------------------
help for midas (Ben Adarkwa Dwamena)
-------------------------------------------------------------------------------
midas -- Meta-analytical Integration of Diagnostic Accuracy Studies
Syntax
midas varlist [if exp] [in range] [, id(varname) year(varname)
modeling_options quality_assessment_options
reporting_options exploratory_graphics_options
publication_bias_Options forest_plot_options
heterogeneity_options roc_options
probability_revision_options general_graphing_options *]
modeling_options may be nip(integer 15) estimator()
quality_assessment_options may be qualitab qualibar qlab
reporting_options may be results() table()
exploratory_graphics_options may be chiplot bivbox qqplot cum inf
publication_bias_options may be pubbias funnel maxbias
forest_plot_options may be forest() fordata
heterogeneity_options may be galb() hetfor covars
roc_curve_options may be sroc1 sroc2 rocplane
probability_revision_options may be pddam() fagan prior()
lrmatrix
and general_graphing_options may be plottype(string)
testlab(string) csize(real 36) hsize(integer 6) vsize(integer 8)
level(integer 95) mscale(real 0.50) textscale(real 0.85) zcf(real
0.5)
by...: may be used with midas; see help by.
Description
midas is a comprehensive program of statistical and graphical
routines for undertaking meta-analysis of diagnostic test performance
in Stata.
Primary data synthesis is performed within the bivariate mixed-efects
binary regression modeling framework. Model specification, estimation
and prediction are carried out with xtmelogit in Stata release
10(Statacorp, 2007) or gllamm(Rabe-Hesketh et.al) in release 9, by
adaptive quadrature.
Using the model estimated coefficients and variance-covariance
matrices, midas calculates summary operating sensitivity and
specificity (with confidence and prediction contours in SROC space),
summary likelihood and odds ratios. Global and relevant test
performance metric-specific heterogeneity statistics are provided.
midas facilitates extensive statistical and graphical data synthesis
and exploratory analyses of heterogeneity, covariate effects,
publication bias and influence.
Bayes' nomograms and likelihood ratio matrices may be obtained and
used to guide clinical decision-making.
The minimum required varlist is the data from contingency tables of
test results. The user provides the data in a rectangular array
containing variables for the 2x2 elements a, b, c, and d:
2x2 +---------------------+
table | Test |
+---------+----------+----------+ where:
| Truth | Positive | Negative | a = true positives,
+---------+----------+----------+ b = false positives,
| Case | a | c | c = false negatives,
+---------+----------+----------+ d = true negatives.
| Noncase | b | d |
+---------+----------+----------+
Each data file row contains the 2x2 data for one observation (i.e.,
study). id(varname) year(varname), if provided , is concatenated to
create a study identification variable. Default uses observation
number for id.
The varlist MUST contain variables for a, b, c, and d in that order.
Note: midas requires release 10 to implement modeling with xtmelogit
or Stata version 9 for estimation with gllamm (mainly because of use
of paired coordinate arrow graphics not available before release 9);
User should install (if not installed) metan and mylabels for either
estimator and also gllamm if using release 9.
+----------+
----+ Modeling +---------------------------------------------------------
nip specifies the number of integration points used for maximum
likelihood estimation based on adaptive gaussian quadrature. Default
is set at 15 for midas even though the default in xtmelogit is 7.
Higher values improve accuracy at the expense of execution times.
The only values currently supported by gllamm are 5, 7, 9, 11 and 15
(Rabe-Hesketh & Skrondal 2005}, appendix B.)
Using xtmelogit with nip(1), model will be estimated by Laplacian
approximation. This decreases substantially computational time and
yet provides reasonably valid fixed effects estimates. It may,
however, produce biased estimates of the variance components.
estimator(g|x) provides a choice between estimation with xtmelogit in
release 10 versus gllamm in version 9 or earlier.
The following options MUST have an estimator inorder to work!
pddam(), fagan, forest(),rocplane, sroc1, sroc2, hetfor, results(),
table() and lrmatrix. if estimator is missing, midas will issue an
error message.
+--------------------+
----+ Quality_Assessment +-----------------------------------------------
qualitab creates, using optional varlist of study quality items
(presence=1, other=0) a table showing frequency of methodologic
quality items.
qualibar creates, combined with optional varlist of study quality
items (presence=1, other=0) calculates study-specific quality scores
and plots a bargraph of methodologic quality.
qlab may be combined with qualitab or qualibar to use variable labels
for table and bargraph of methodologic items.
+-----------+
----+ Reporting +--------------------------------------------------------
results(all) provides summary statistics for all performance indices,
group-specific between-study variances, likelihood rato test
statistics and other global homogeneity tests.
results(het) provides group-specific between-study variances,
likelihood rato test statistics and other global homogeneity tests.
results(sum) provides summary statistics for all performance indices
table(dss|dlr|dlor) will create a table of study specific performance
estimates with measure-specific summary estimates and results of
homogeneity (chi_squared) and inconsistency(I_squared) tests. dss,
dlr or dlor represent the paired performance measures
sensitivity/specificity, positive/negative likelihood ratios and
diagnostic score/odds ratios.
+----------------------+
----+ Exploratory Graphics +---------------------------------------------
bivbox implements a two-dimensional analogue of the boxplot for
univariate data similar to the bivariate boxplot (Goldberg and
Iglewicz,1992). It is used to assess distributional properties of
sensitivity versus specificity and for indentifying possible
outliers.
chiplot creates a chiplot (Fisher & Switzer, 1985, 2001) for judging
whether or nor the paired performance indices are independent by
augmenting the scatterplot with an auxiliary display. In the case of
independence, the points will be concentrated in the central region,
in the horizontal band indicated on the plot.
qqplot(dss|dlor|dlr) plots a normal quantile plot to (a) check the
normality assumption (b) investigate whether all studies come from a
single population (c) search for publication bias (Wang and Bushman,
1998).
cum produces a cumulative meta-analysis plot showing how evidence has
accumulated over time using metan (most current version must be
installed and may be obtained my typing {ssc install metan,
replace}). midas uses year of publication as measure for temporal
evolution of evidence.
Results are displayed graphically using serrbar. The ith line on the
plot is the summary produced by a meta-analysis of the first ith
trials.
inf investigates the influence of each individual study on the
overall meta-analysis summary estimate. This option presents a
serrbar of the results of an influence analysis in which the
meta-analysis is reestimated omitting each study in turn, using metan
(most current version must be installed).
+------------------+
----+ Publication Bias +-------------------------------------------------
pubbias When this option is invoked, midas performs linear regression
of log odds ratios on inverse root of effective sample sizes as a
test for funnel plot asymmetry in diagnostic metanalyses. A non-zero
slope coefficient is suggestive of significant small study
bias(pvalue < 0.10).
maxbias performs Copas' worst-case sensitivity analysis for
publication bias. It calculates the upper bound number of missing
studies that will overturn statistical significance and estimates the
minimun likely publication probability (Copas and Jackson, 2004).
funnel plots a funnel plot, a two-dimensional graph with sample size
on one axis and effect-size estimate on the other axis. The funnel
plot capitalizes on the well-known statistical principle that
sampling error decreases as sample size increases.
In a meta-analysis, the funnel plot can be used to investigate
whether all studies come from a single population and to search for
publication bias.
+--------------+
----+ Forest Plots +-----------------------------------------------------
forest(dss|dlr|dlor) creates summary graphs with study-specific(box)
and overall(diamond) point estimates and confidence intervals for
each performance index pair using graph combine. Confidence intervals
lines are allowed to extend between 0 and 1000 beyond which they are
truncated and marked by a leading arrow.
fordata adds study-specific performance estimates and 95% CIs to
right y-axis.
+---------------+
----+ Heterogeneity +----------------------------------------------------
galb(dss|dlr|dlor) The standardized effect measure (e.g. for lnDOR,
lnDOR/precision) is plotted (y-axis) against the inverse of the
precision(x-axis). A regression line that goes through the origin is
calculated, together with 95% boundaries (starting at +2 and -2 on
the y-axis). Studies outside these 95% boundaries may be considered
as outliers.
hetfor creates composite forest plot of all performance indices to
provide a general view of variability.Confidence intervals lines are
allowed to extend between 0 and 1000 beyond which they are truncated
and marked by a leading arrow.
covars combined with an optional varlist permits univariable
metaregression analysis of one or multiple covariables.
+------------+
----+ ROC Curves +-------------------------------------------------------
sroc1 plots observed datapoints, summary operating sensitivity and
specificity in SROC space.
sroc2 adds confidence and prediction contours.
rocplane plots observed data in receiver operating characteristic
space (ROC Plane) for visual assessment of threshold effect.
+-------------------------------+
----+ Probability Revision Options +------------------------------------
fagan creates a plot showing the relationship between the prior
probability, the likelihood ratio(combination of sensitivity and
specificity), and posterior test probability.
prior() combined with fagan allows user to specify a pretest
probability overriding the default of using disease prevalence
calculated from data when fagan is invoked alone.
pddam(p|r) produces a line graph of post-test probalities versus
prior probabilities between 0 and 1 using summary likelihood ratios
lrmatrix creates a scatter plot of positive and negative likelihood
ratios with combined summary point. Plot is divided into quadrants
based on strength-of-evidence thresholds to determine informativeness
of measured test.
+------------------+
----+ Graphing Options +-------------------------------------------------
plottype(string) will add type of plot to title of plot
testlab(string) will add any descriptive string to title of plot
csize() allows user to modify relative sizes of combined forest plots
along the x axis.
hsize() allows user to modify size of other plots along the x axis.
vsize() allows user to modify relative sizes of combined forest plots
along the y axis.
level() specifies the significance level for statistical tests,
confidence contours, prediction contours and confidnce intervals.
mscale() affects size of markers for point estimates on forest plots.
scheme(string) permits choice of scheme for graphs. The default is
s2color.
textscale() allows choice of text size for graphs especially
regarding labels for forest plots.
zcf() defines a fixed continuity correction to add in the case where
a study contains a zero cell. By default, midas adds 0.5 to each cell
of a study where a zero is encountered for logit and log
transformations, only to calculate study-specific likelihood ratios
and odds ratios. However, the zcf() option allows the use of other
constants between 0 and 1.
Remarks on test performance metrics:
Sensitivity and specificity , diagnostic odds ratio and likelihood
ratios with 95% confidence intervals, are recalculated for each
primary study from the contingency tables of true-positive [a],
false-positive , false-negative results [c], and true-negative
[d].
A four-fold (two by two contingency) table comparing test results for
a diagnostic/screening test is identical to a four-fold table
comparing outcomes of an experimental application of an intervention
(Skupski, Rosenberg and Eglinton, 2002).
For an interventional trial, the true positives are the experimental
group with the monitored outcome present [a].The false positives are
the control group with the outcome present . The false negatives
are the experimental group with the outcome absent [c]. The true
negatives are the control group with the outcome absent [d]. The
expression for the relative risk in the experimental group {[a/ (a +
c)]/ [b/ (b + d)]} is identical to the expression for the likelihood
ratio for a positive test in an evaluation of a diagnostic or a
screening methodology. Similarly, the expression for the relative
risk in the control group in an interventional trial is identical to
the expression for the likelihood ratio for a negative test(Skupski,
Rosenberg and Eglinton, 2002). The LRs indicate by how much a given
test would raise or lower the probability of having disease. In order
for diagnostic informativeness to be high, an LR of > 10 or < 0.1
would be required for a positive and negative test result,
respectively. Moderate informational value can be achieved with LR
values of 5-10 and 0.1-0.2; LRs of 2-5 and 0.2-0.5 have very small
informational value.
The diagnostic odds ratio of a test is the ratio of the odds of
positivity in disease relative to the odds of positivity in the
nondiseased (Glas, Lijmer, Prins, Bonsel and Bossuyt, 2003). The
expression for the odds ratio (DOR) is (a × d)/(b × c). The value of
a DOR ranges from 0 to infinity, with higher values indicating better
discriminatory test performance. A value of 1 means that a test does
not discriminate between patients with the disorder and those without
it. Values lower than 1 point to improper test interpretation (more
negative tests among the diseased). The diagnostic odds ratio (DOR)
may be used as a single summary measure with the caveat that the same
odds ratio may be obtained with different combinations of sensitivity
and specificity (Glas, Lijmer, Prins, Bonsel and Bossuyt, 2003)
The area under the curve (AUROC), obtained by trapezoidal
integration, serves as a global measure of test performance. The
AUROC is the average TPR over the entire range of FPR values. The
following guidelines have been suggested for interpretation of
intermediate AUROC values: low (0.5>= AUC <= 0.7), moderate (0.7 >=
AUC <= 0.9), or high (0.9 >= AUC <= 1) accuracy (Swets, 1988).
Remarks on Meta-analytic Model:
Primarily, midas uses an exact binomial rendition (Chu & Cole, 2006)
of the bivariate mixed-effects regression model developed by von
Houwelingen(von Houwelingen, 1993, 2001) for treatment trial
meta-analysis and modified for synthesis of diagnostic test data
(Reitsma, 2005; Riley, 2006).
It fits a two-level model, with independent binomial distributions
for the true positives and true negatives conditional on the
sensitivity and specificity in each study and a bivariate normal
model for the logit transforms of sensitivity and specificity between
studies.
The standard output of the bivariate model includes: mean logit
sensitivity and specificity with their standard errors and 95%
confidence intervals; and estimates of the between-study variability
in logit sensitivity and specificity and the covariance between them.
Based on these parameters, we can calculate other measures of
interest such as the likelihood ratio for positive and negative test
results, the diagnostic odds ratio, the correlation between logit
sensitivity and specificity, several summary ROC linear regression
lines based on either the regression of logit sensitivity on
specificity, the regression of logit specificity on sensitivity, or
an orthogonal regression line by minimizing the perpendicular
distances. These lines can be transformed back to the originalROC
scale to obtain a summary ROC curve. Summary sensitivity,
specificity, and the corresponding positive likelihood, negative
likelihood and diagnostic odds ratios are drived as functions of the
estimated model parameters; The derived logit estimates of
sensitivity, specificity and respective variances are used to
construct a hierarchical summary ROC curve.
Remarks on assessment and exploration of heterogeneity:
Heterogeneity means that there is between study variation.
Galbraith(radial) plot is used to visually identify outliers. To
construct this plot, the standardized lnDOR = lnDOR/se is plotted
(y-axis) against the inverse of the se (1/se) (x-axis). A regression
line that goes through the origin is calculated, together with 95%
boundaries (starting at +2 and -2 on the y-axis). Studies outside
these 95% boundaries may be considered as outliers.
Many sources of heterogeneity can occur: characteristics of the study
population, variations in the study design (type of design, selection
prodedures, sources of information, how the information is
collected), different statistical methods, and different covariates
adjusted for (if relevant) (Dinnes, 2005). Heterogeneity (or absence
of homogeneity) of the results between the studies is assessed
graphically by forest plots and statistically using the quantity I2
that describes the percentage of total variation across studies that
is attributable to heterogeneity rather than chance (Higgins, 2003).
I2 can be calculated from basic results as I2 = 100% x (Q - df)/Q,
where Q is Cochran's heterogeneity statistic and df the degrees of
freedom. (Higgins, 2003). Negative values of I2 are made equal to 0
so that I2 lies between 0% and 100%. A value of 0% indicates no
observed heterogeneity, and values greater than 50% may be considered
substantial heterogeneity. The main advantage of I2 is that it does
not inherently depend on the number of the studies in the
meta-analysis.
Formal investigation of heterogeneity is performed by multiple
univariable bivariate meta-regression models. Covariates are
manipulated as mean-centered continuous or as dichotomous (yes=1, no=
0) fixed effects. The effect of each covariate on sensitivity is
estimated separately from that on specificity. Metaregression is a
collection of statistical procedures (weighted/unweighted linear,
logistic regression) to assess heterogeneity, in which the effect
size of study is regressed on one or several covariates, with a value
defined for each study.
Remarks on Publication bias:
Publication bias is produced when the published studies do not
represent adequately all the studies carried out on a specific topic
(Begg and Berlin). This bias may be caused by factors such as the
trend to publish statistically significant (p < 0.05) or clinically
relevant (high magnitude albeit non-significant) results. Other
variables influencing publication bias (Song, 2002) are sample size
(more in small studies), type of design, funding, conflict of
interest, prejudice against an observed association, sponsorship.
Publication bias is assessed visually by using a scatter plot (Light
and Pillemer, 1984) of the inverse of the square root of the
effective sample size (1/ESS1/2) versus the diagnostic log odds
ratio(lnDOR) which should have a symmetrical funnel shape when
publication bias is absent (Deeks, 2005).
Separate funnel plots for sensitivity and specificity (after logit
transformation) are unlikely to be helpful for detecting sample size
effects, because sensitivities and specificities will vary due to
both variability of threshold between the studies and random
variability. Simultaneous interpretation of two related funnel plots
and two tests for funnel plot asymmetry also presents challenges.
Formal testing for publication bias may be conducted by a regression
of lnDOR against 1/ESS1/2, weighting by ESS (Deeks, 2005), with P <
.05 for the slope coefficient indicating significant asymmetry.
An alternative graphical test of publication bias may be derived by
assessing the linearity of the Normal quantile plot (Wang and
Bushman, 1998). This plot compares the quantiles of an observed
distribution against the quantiles of the standard Normal
distribution. In a meta-analysis, such a plot can be used to check
the Normality assumption, investigate whether all studies come from a
single population, and search for publication bias (Wang and Bushman,
1998).
Remarks on Cumulative meta-analysis:
Cumulative meta-analysis is a type of meta-analysis in which studies
are sequentially pooled by adding each time one new study according
to an ordered variable. For instance, if the ordered variable is the
year of publication, studies will be ordered by it; then, a pooling
analysis will be done every time a new article appears. It shows the
evolution of the pooled estimate according to the ordered variable.
Other common variables used in cumulative meta-analysis are the study
quality, the risk of the outcome in the control group, the size of
the difference between the groups, and other covariates.
Remarks on Clinical Application:
The clinical or patient-relevant utility of diagnostic test is
evaluated using the likelihood ratios to calculate post-test
probability based on Bayes' theorem as follows (Jaeschke, 1994):
Pretest Probability=Prevalence of target condition
Post-test probability= likelihood ratio x pretest
probability/[(1-pretest probability) x (1-likelihood ratio)]
Assuming that the study samples are representative of the entire
population, an estimate of the pretest probability of target
condition is calculated from the global prevalence of this disorder
across the studies.
In this way, likelihood ratios are more clinically meaningful than
sensitivities or specificities. This approach would be useful for
the clinicians who might use the likelihood ratios generated from
here to calculate the post-test probabilities of nodal disease based
on the prevalence rates of their own practice population.
Thus, this approach permits individualization of diagnostic evidence.
This concept is depicted visually with Fagan's nomograms. When Bayes
theorem is expressed in terms of log-odds, the posterior log-odds are
linear functions of the prior log-odds and the log likelihood ratios.
fagan plots an axis on the left with the prior log-odds, an axis in
the middle representing the log likelihood ratio and an axis on the
right representing the posterior log-odds. Lines are then drawn from
the prior probability on the left through the likelihood ratios in
the center and extended to the posterior probabilities on the right.
The likelihood ratio matrix defines quadrants of informativeness
based on established evidence-based thresholds:
Left Upper Quadrant, Likelihood Ratio Positive > 10, Likelihood Ratio
Negative <0.1: Exclusion & Confirmation
Right Upper Quadrant, Likelihood Ratio Positive >10, Likelihood Ratio
Negative >0.1: Confirmation Only
Left Lower Quadrant, Likelihood Ratio Positive <10, Likelihood Ratio
Negative <0.1: Exclusion Only
Right Lower Quadrant, Likelihood Ratio Positive <10, Likelihood Ratio
Negative >0.1: No Exclusion or Confirmation
Examples
. use http://repec.org/nasug2007/midas_example_data.dta
Summary Statistics
. midas tp fp fn tn, es(x) res(all)
(click to run)
Table of index-specific results
. midas tp fp fn tn, es(x) table(dlr)
(click to run)
Summary ROC Curve with prediction and confidence Contours
. midas tp fp fn tn, es(x) plot sroc2
(click to run)
Linear regression test of funnel plot asymmetry
. midas tp fp fn tn, pubbias
(click to run)
Funnel plot assessment of publication and other small study biases
. midas tp fp fn tn, fun
(click to run)
Forest plot to demonstrate variability
. midas tp fp fn tn,
id(author) year(year) ms(0.75)
for(dss) es(x) texts(0.80)
(click to run)
Forest plot to demonstrate study-specific on right y-axis
.midas tp fp fn tn, id(author) year(year)
es(x) ms(0.75) ford for(dss) texts(0.80)
(click to run)
Fagan's plot
.midas tp fp fn tn, es(x) fagan prior(0.20)
(click to run)
Likelihood Matrix
.midas tp fp fn tn, es(x) lrmat
(click to run)
Bivariate Boxplot
.midas tp fp fn tn, bivbox scheme(s2color)
(click to run)
Quality Assessment
.midas tp fp fn tn prodesign ssize30 fulverif testdescr
refdescr subjdescr report brdspect blinded, qualib
(click to run)
Meta-regression
.midas tp fp fn tn prodesign ssize30 fulverif testdescr
refdescr subjdescr report brdspect blinded, es(x) covars
(click to run)
Saved results
midas saves the following in r():
Scalars
r(fsens) fixed effects estimate of summary sensitivity
r(fspec) fixed effects estimate of summary specificity
r(flrn) fixed effects estimate of summary likelihood
ratio of a negative test
r(flrp) fixed effects estimate of summary likelihood
ratio of a positive test
r(fdor) fixed effects estimate of summary diagnostic
odds ratio
r(fldor) fixed effects estimate of summary diagnostic
score
r(mtpr) mixed effects estimate of summary sensitivity
r(mtprse) standard error of mixed effects estimate of
summary sensitivity
r(mtprlo) lower bound of mixed effects estimate of
summary sensitivity
r(mtprhi) upper bound of mixed effects estimate of
summary sensitivity
r(mtnr) mixed effects estimate of summary specificity
r(mtnrse) standard error of mixed effects estimate of
summary specificity
r(mtnrlo) lower bound of mixed effects estimate of
summary specificity
r(mtnrhi) upper bound of mixed effects estimate of
summary specificity
r(mlrp) mixed effects estimate of summary likelihood
ratio of a positive test result
r(mlrpse) standard error of mixed effects estimate of
summary likelihood ratio of a positive test
result
r(mlrplo) lower bound of mixed effects estimate of
summary likelihood ratio of a positive test
result
r(mlrphi) upper bound of mixed effects estimate of
summary likelihood ratio of a positive test
result
r(mlrn) mixed effects estimate of summary likelihood
ratio of a negative test result
r(mlrnse) standard error of summary likelihood ratio of a
negative test result
r(mlrnlo) lower bound of summary likelihood ratio of a
negative test result
r(mlrnhi) mixed effects estimate of summary likelihood
ratio of a negative test result
r(mdor) mixed effects estimate of summary diagnostic
odds ratio
r(mdorse) standard error of summary diagnostic odds ratio
r(mdorlo) lower bound of summary diagnostic odds ratio
r(mdorhi) upper bound of summary diagnostic odds ratio
r(mldor) mixed effects estimate of summary diagnostic
score
r(mldorse) standard error of summary diagnostic score
r(mldorlo) lower bound of summary diagnostic score
r(mldorhi) upper bound of summary diagnostic score
r(AUC) Area under summary ROC curve
r(AUClo) lower bound of area under summary ROC curve
r(AUChi) upper bound of area under summary ROC curve
r(covar) covariance of logits of sensitivity and
specificity
r(rho) correlation between logits of sensitivity and
specificity
r(rholo) lower bound of correlation
r(rhohi) upper bound of correlation
r(reffs1) variance of logit of sensitivity
r(reffs1se) standard error of variance of logit of
sensitivity
r(reffs1lo) lower bound variance of logit of sensitivity
r(reffs1hi) upper bound variance of logit of sensitivity
r(reffs2) variance of logit of specificity
r(reffs2se) standard error of variance of logit of
specificity
r(reffs2lo) lower bound variance of logit of specificity
r(reffs2hi) upper bound variance of logit of specificity
r(Islrt) global inconsistency index from likelihood
ratio rest
r(Islrtlo) lower bound global inconsistency index
r(Islrthi) upper bound global inconsistency index
References
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Author
Ben A. Dwamena, Division of Nuclear Medicine, Department of
Radiology, University of Michigan, USA Email bdwamena@umich.edu for
problems, comments and suggestions
Citation
Users should please reference program in any published work as:
Dwamena, Ben A.(2007) midas: Computational and Graphical Routines for
Meta-analytical Integration of Diagnostic Accuracy Studies in Stata.
Division of Nuclear Medicine, Department of Radiology, University of
Michigan Medical School, Ann Arbor, Michigan.
Acknowledgement
Thanks to
-Roberto Gutierrez and the Stata Development team for xtmelogit and
all that Stata offers....
-Richard Sylvester and Ruth Carlos for encouragement, suggestions and
testing of midas
-Richard Riley for trusting me with pre-prints of his work on
bivariate meta-analysis.
-Joseph Coveney for posting syntax for the bivariate model using
gllamm on Statalist.
-Sophia-Rabe-Hesketh and other authors of gllamm for their work.
-Derek Wenger for assistance with coding Fagan's plot.
-Nick Cox for his polarsm which was adapted for bivbox option in
midas and for mylabels.
-Roger Harbord for his metareg, metafunnel and metamodbias programs
which provided very useful ideas for midas.
-Mike Bradburn ( and R Harris) for metan which was used to implement
cumulative and influence meta-analyses in midas.
Also see
On-line: help for metan (if installed), gllamm (if installed),
mylabels (if installed)
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发表于 2012-3-23 13:55
发表于 2012-3-23 15:09
发表于 2012-3-23 15:48
