Systems Theory and the Sociology of Health and Illness #Luhmann

Today I got my copy from Systems Theory and the Sociology of Health and Illness – Observing Healthcare, a book edited by Morten Knudsen and Werner Vogd. The book contains very interesting chapters on this topic, and though it is not so cheap for private purchasing, you may consider ordering this book through your library.

Here is a short abstract of my contribution, Sustainability in Integrated Care Partnerships: a systems and network theoretical approach to analyse co-operation networks. The chapter highlights the importance and role of networks in the stabilization of meaningful arrangements between different contextures that refer to the basic conflict between medical and economic systems. Control mechanisms, which are applied to arrange these polycontextural conditions, have been analysed. Qualitative interviews were conducted with actors involved integrated care partnerships. Results show that loosely linked networks can take on important control functions in the sustainable balancing of financial, nursing and medical demands. Too tightly forged links prevent dynamic balancing and relating of different contextures. In such cases, networks can lose beneficial self-regulation capacities to eliminate drawbacks.

Systems Theory and the Sociology of Health and Illness #Luhmann

Visualizing (generalized) linear mixed effects models, part 2 #rstats #lme4

In the first part on visualizing (generalized) linear mixed effects models, I showed examples of the new functions in the sjPlot package to visualize fixed and random effects (estimates and odds ratios) of (g)lmer results. Meanwhile, I added further features to the functions, which I like to introduce here. This posting is based on the online manual of the sjPlot package.

In this posting, I’d like to give examples for diagnostic and probability plots of odds ratios. The latter examples, of course, only refer to the sjp.glmer function (generalized mixed models). To reproduce these examples, you need the version 1.59 (or higher) of the package, which can be found at GitHub. A submission to CRAN is planned for the next days…

Fitting example models

The following examples are based on two fitted mixed models:

# fit model
library(lme4)
# create binary response
sleepstudy$Reaction.dicho <- sju.dicho(sleepstudy$Reaction, 
                                       dichBy = "md")
# fit first model
fit <- glmer(Reaction.dicho ~ Days + (Days | Subject),
             sleepstudy,
             family = binomial("logit"))

data(efc)
# create binary response
efc$hi_qol <- sju.dicho(efc$quol_5)
# prepare group variable
efc$grp = as.factor(efc$e15relat)
levels(x = efc$grp) <- sji.getValueLabels(efc$e15relat)
# data frame for 2nd fitted model
mydf <- na.omit(data.frame(hi_qol = as.factor(efc$hi_qol),
                           sex = as.factor(efc$c161sex),
                           c12hour = as.numeric(efc$c12hour),
                           neg_c_7 = as.numeric(efc$neg_c_7),
                           grp = efc$grp))
# fit 2nd model
fit2 <- glmer(hi_qol ~ sex + c12hour + neg_c_7 + (1|grp),
              data = mydf,
              family = binomial("logit"))

Summary fit1

Formula: Reaction.dicho ~ Days + (Days | Subject)
   Data: sleepstudy

     AIC      BIC   logLik deviance df.resid 
   158.7    174.7    -74.4    148.7      175 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.2406 -0.2726 -0.0198  0.2766  2.9705 

Random effects:
 Groups  Name        Variance Std.Dev. Corr 
 Subject (Intercept) 8.0287   2.8335        
         Days        0.2397   0.4896   -0.19
Number of obs: 180, groups:  Subject, 18

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -3.8159     1.1728  -3.254 0.001139 ** 
Days          0.8908     0.2347   3.796 0.000147 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
     (Intr)
Days -0.694

Summary fit2

Formula: hi_qol ~ sex + c12hour + neg_c_7 + (1 | grp)
   Data: mydf

     AIC      BIC   logLik deviance df.resid 
  1065.3   1089.2   -527.6   1055.3      881 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7460 -0.8139 -0.2688  0.7706  6.6464 

Random effects:
 Groups Name        Variance Std.Dev.
 grp    (Intercept) 0.08676  0.2945  
Number of obs: 886, groups:  grp, 8

Fixed effects:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  3.179036   0.333940   9.520  < 2e-16 ***
sex2        -0.545282   0.178974  -3.047  0.00231 ** 
c12hour     -0.005148   0.001720  -2.992  0.00277 ** 
neg_c_7     -0.219586   0.024108  -9.109  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
        (Intr) sex2   c12hor
sex2    -0.410              
c12hour -0.057 -0.048       
neg_c_7 -0.765 -0.009 -0.116

Diagnostic plots

Two new functions are added to both sjp.lmer and sjp.glmer, hence they apply to linear and generalized linear mixed models, fitted with the lme4 package. The examples only refer to the sjp.glmer function.

Currently, there are two type options to plot diagnostic plots: type = "fe.cor" to plot a correlation matrix between fixed effects and type = "re.qq" to plot a qq-plot of random effects.

Correlation matrix of fixed effects

To plot a correlation matrix of the fixed effects, use type = "fe.cor".

# plot fixed effects correlation matrix
sjp.glmer(fit2, type = "fe.cor")

unnamed-chunk-11-1

qq-plot of random effects

Another diagnostic plot is the qq-plot for random effects. Use type = "re.qq" to plot random against standard quantiles. The dots should be plotted along the line.

# plot qq-plot of random effects
sjp.glmer(fit, type = "re.qq")

unnamed-chunk-13-1

Probability curves of odds ratios

These plotting functions have been implemented to easier interprete odds ratios, especially for continuous covariates, by plotting the probabilities of predictors.

Probabilities of fixed effects

With type = "fe.pc" (or type = "fe.prob"), probability plots for each covariate can be plotted. These probabilties are based on the fixed effects intercept. One plot per covariate is plotted.

The model fit2 has one binary and two continuous covariates:

# plot probability curve of fixed effects
sjp.glmer(fit2, type = "fe.pc")

unnamed-chunk-9-1

Probabilities of fixed effects depending on grouping level (random intercept)

With type = "ri.pc" (or type = "ri.prob"), probability plots for each covariate can be plotted, depending on the grouping level from the random intercept. Thus, for each covariate a plot for each grouping levels is plotted. Furthermore, with the show.se the standard error of probabilities can be shown. In this example, only the plot for one covariate is shown, not for all.

# plot probability curves for each covariate
# grouped by random intercepts
sjp.glmer(fit2,
          type = "ri.pc",
          show.se = TRUE)

unnamed-chunk-8-2

Instead of faceting plots, all grouping levels can be shown in one plot:

# plot probability curves for each covariate
# grouped by random intercepts
sjp.glmer(fit2,
          type = "ri.pc",
          facet.grid = FALSE)

unnamed-chunk-10-2

Outlook

These will be the new features for the next package update. For later updates, I’m also planning to plot interaction terms of (generalized) linear mixed models, similar to the existing function for visualizing interaction terms in linear models.

Visualizing (generalized) linear mixed effects models, part 2 #rstats #lme4

Patient centredness in integrated care (from systems theoretical perspective) #Luhmann #Systemstheory

My paper Patient centredness in integrated care: results of a qualitative study based on a systems theoretical framework has just been published in the International Journal of Integrated Care. It’s an open acces journal, so there’s no paywall to read it.
Let me provide you the abstract:

Introduction: Health care providers seek to improve patient-centred care. Due to fragmentation of services, this can only be achieved by establishing integrated care partnerships. The challenge is both to control costs while enhancing the quality of care and to coordinate this process in a setting with many organisations involved. The problem is to establish control mechanisms, which ensure sufficiently consideration of patient centredness.

Theory and methods: Seventeen qualitative interviews have been conducted in hospitals of metropolitan areas in northern Germany. The documentary method, embedded into a systems theoretical framework, was used to describe and analyse the data and to provide an insight into the specific perception of organisational behaviour in integrated care.

Results: The findings suggest that integrated care partnerships rely on networks based on professional autonomy in the context of reliability. The relationships of network partners are heavily based on informality. This correlates with a systems theoretical conception of organisations, which are assumed autonomous in their decision-making.

Conclusion and discussion: Networks based on formal contracts may restrict professional autonomy and competition. Contractual bindings that suppress the competitive environment have negative consequences for patient-centred care. Drawbacks remain due to missing self-regulation of the network. To conclude, less regimentation of integrated care partnerships is recommended.

The full text is also available as PDF file.

Patient centredness in integrated care (from systems theoretical perspective) #Luhmann #Systemstheory

Visualizing (generalized) linear mixed effects models with ggplot #rstats #lme4

In the past week, colleagues of mine and me started using the lme4-package to compute multi level models. This inspired me doing two new functions for visualizing random effects (as retrieved by ranef()) and fixed effects (as retrieved by fixef()) of (generalized) linear mixed effect models.

The upcoming version of my sjPlot package will contain two new functions to plot fitted lmer and glmer models from the lme4 package: sjp.lmer and sjp.glmer (not that surprising function names). Since I’m new to mixed effects models, I would appreciate any suggestions on how to improve the functions, which results are important to report (plot) and so on. Furthermore, I’m not sure whether my approach of computing confident intervals for random effects is the best?

I have used following code to compute confident intervals for the estimates returned by the lme4::ranef() function (bases on this stackoverflow answer):

coev <- as.matrix(lme4::vcov.merMod(fit))
tmp <- as.data.frame(cbind(OR = exp(mydf.ef[,i]),
                     lower.CI = exp(mydf.ef[,i] - (1.96 * sqrt(diag(coev))[i])),
                     upper.CI = exp(mydf.ef[,i] + (1.96 * sqrt(diag(coev))[i]))))

The update to version 1.6 of sjPlot is still in development (feature-freeze, mostly fixes now), however, you can download the latest snapshot from GitHub (see also this post for further information). Now to some examples. First, an example model is fitted and the random effects (default) for each predictor are plotted as “forest plot”:

# fit model
library(lme4)
fit <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
# simple plot
sjp.lmer(fit)

lmer1

Sorting a predictor (i.e. estimates of a facet) is done by specifying the predictor’s name as sort parameter.

sjp.lmer(fit, sort = "Days")

lmer2

Each facet plot can also be plotted as single plot, when facet.grid is set to FALSE. In this case, it is possible to sort the estimates for each plots. See following example from the sjp.glmer function:

library(lme4)
# create binary response
sleepstudy$Reaction.dicho <- sju.dicho(sleepstudy$Reaction, 
                                       dichBy = "md")
# fit model
fit <- glmer(Reaction.dicho ~ Days + (Days | Subject),
             sleepstudy,
             family = binomial("logit"))
sjp.setTheme(theme = "forest")
sjp.glmer(fit, 
          facet.grid = FALSE, 
          sort = "sort.all")

glmer1 glmer2

Plotting the fixed effects is not much spectacular, because we only have one estimate beside intercept here.

sjp.glmer(fit, 
          type = "fe", 
          sort = TRUE)

glmer3

To summarize, you can plot random and fixed effects in the way as shown above. Are there any other or better plot options for visualizing mixed effects models?

Any suggestions are welcome…

Disclaimer: all misspellings belong to Safari’s autocorrect feature!

Visualizing (generalized) linear mixed effects models with ggplot #rstats #lme4

sjPlot 1.6 – major revisions, anyone for beta testing? #rstats

In the last couple of weeks I have rewritten some core parts of my sjPlot-package and also revised the package- and online documentation.

Most notably are the changes that affect theming and appearance of plots and figures. There’s a new function called sjp.setTheme which now sets theme-options for all sjp-functions, which means

  1. you only need to specify theme / appearance option once and no longer need to repeat these parameter for each sjp-function call you make
  2. due to this change, all sjp-functions have much less parameters, making the functions and documentation clearer

Furthermore, due to some problems with connecting / updating to the RPubs server, I decided to upload my online documentation for the package to my own site. You will now find the latest, comprehensive documentation and examples for various functions of the sjPlot package at www.strengejacke.de/sjPlot/. For instance, take a look at customizing plot appearance and see how the new theming feature of the package allows both easier customization of plots as well as better integration of theming packages like ggthemr or ggthemes.

Updating the sjPlot package to CRAN is planned soon, however, I kindly ask you to test the current development snapshot, which is hosted on GitHub. You can easily install the package from there using the devtools-package (devtools::install_github("devel", "sjPlot")). The current snapshot is (very) stable and I appreciate any feedbacks or bug reports (if possible, use the issue tracker from GitHub).

The current change log with all new function, changes and bug fixes can also be found on GitHub.

sjPlot 1.6 – major revisions, anyone for beta testing? #rstats

Visualize pre-post comparison of intervention #rstats

My sjPlot-package was just updated on CRAN, introducing a new function called sjp.emm.int to plot estimated marginal means (least-squares means) of linear models with interaction terms. Or: plotting adjusted means of an ANCOVA.

The idea to this function came up when we wanted to analyze the effect of an intervention (an educational programme on knowledge about mental disorders and associated stigma) between two groups: a “treatmeant” group (city) where a campaign on mental disorders was conducted and another city without this campaign. People from both cities were asked about their attitudes and knowledge about specific mental disorders at t0 before the campaign started in the one city. Some month later (t1), again people from both cities were asked the same questions. The intention was to see a) whether there were differences in knowledge and pro-social attidutes of people towards mental disorders and b) if the compaign successfully reduces stigma and increases knowledge.

To analyse these questions, we used an ANCOVA with knowledge and stigma score as dependent variables, “city” and “time” (t0 versus t1) as predictors and adjusted for covariates like age, sex, education etc. The estimated marginal means (or least-squares means) show you the differences of the dependent variable.

Here’s an example plot, quickly done with the sjp.emm.int function:
sjpemmint

Since the data is not publicly available, I’ve set an an documentation with reproducable examples (though those example do not fit very well…).

The latest development snapshot of my package is available on GitHub.

BTW: You may have noticed that this function is quite similar to the sjp.lm.int function for visually interpreting interaction terms in linear models…

Visualize pre-post comparison of intervention #rstats

sjPlot: New options for creating beautiful tables and documentation #rstats

A new update of my sjPlot package was just released on CRAN. This release focused on improving existing functions and bug fixes again. Especially the table output functions (see my previous blog posts on table output functions here and here) improved a lot. Tables now have more and better possibilities for style customization and knitr integration. A basic introduction into the new features is given in this document.

To make it easier to understand all features, I started to setup comprehensive documentations for all sjPlot functions on strengejacke.de.

sjPlot: New options for creating beautiful tables and documentation #rstats

Organizational Behaviour im Kooperationsnetzwerk

eine systemtheoretisch-qualitative Analyse von Kooperationsnetzwerken unter den Bedingungen polykontexturaler Verhältnisse

Das ist das Thema meines Beitrags zum frisch bewilligten DFG-Netzwerkantrag Organizational Behaviour in health care institutions in Germany – theoretical approaches, methods and empirical results im Rahmen der DGMS-AG-Versorgungsforschung.

Weiter zum Abstract…

Organizational Behaviour im Kooperationsnetzwerk

sjPlot 1.3 available #rstats #sjPlot

I just submitted my package update (version 1.3) to CRAN. The download is already available (currently source, binaries follow). While the last two updates included new functions for table outputs (see here and here for details on these functions), the current update mostly provides small helper functions. The focus of this update was to improve existing functions and make their handling easier and more comfortable.

Automatic label detection

One major feature is that many functions now automatically detect variables and value labels, if possible. For instance, if you have imported an SPSS dataset (e.g. with the function sji.SPSS), value labels are automatically attached to all variables of the data frame. With the autoAttachVarLabels parameter set to TRUE, even variable labels will be attached to the data frame after importing the SPSS data. These labels are automatically detected by most functions of the package now. But this does not only apply to importet SPSS-data. If you have factors with specified factor levels, these will also automatically be used as value labels. Furthermore, you can manually attach value and variable labels using the new function sji.setVariableLabels and sji.setValueLabels.

But what are the exactly the benefits of this new feature? Let me give an example. To plot a proportional table with axis and legend labels, prior to sjPlot 1.3 you needed following code:

data(efc)
efc.val <- sji.getValueLabels(efc)
efc.var <- sji.getVariableLabels(efc)
sjp.xtab(efc$e16sex,
         efc$e42dep,
         axisLabels.x=efc.val[['e16sex']],
         legendTitle=efc.var['e42dep'],
         legendLabels=efc.val[['e42dep']])

Since version 1.3, you only need to write:

data(efc)
sjp.xtab(efc$e16sex, efc$e42dep)

Reliability check for index scores

One new table output function included in this update is sjt.itemanalysis, which helps performing an item analysis on a scale or data frame if you want to develop index scores.

Let’s say you have several items and you want to compute a principal component analysis in order to identify different components that can be composed to an index score. In such cases, you might want to perform reliability and item discrimination tests. This is shown in the following example, which performs a PCA on the COPE-Index-scale, followed by a reliability and item analysis of each extracted “score”:

data(efc)
# recveive first item of COPE-index scale
start <- which(colnames(efc)=="c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc)=="c90cop9")
# create data frame of cope-index-items
df <- as.data.frame(efc[,c(start:end)])
colnames(df) <- sji.getVariableLabels(efc)[c(start:end)]
# compute PCA on cope index and return
# "group classifications" of factors
factor.groups <- sjt.pca(df, no.output=TRUE)$factor.index
# perform item analysis
sjt.itemanalysis(df, factor.groups)

The result is following table, where two components have been extracted via the PCA, and the variables belonging each component are treated as one “index score” (note that you don’t need to define groups, you can also treat a data frame as one single “index”):
relia

The output of the computed PCA was suppressed by no.output=TRUE. To better understand the above figure, take a look at the PCA results, where two components have been extracted:
pca_item_reli

Beside that, many functions – especially the table output functions – got new parameters to change the appearance of the output (amount of digits, including NA’s, additional information in tables etc.). Refer to the package news to get a complete overview of what was changed since the last version.

The latest developer build can be found on github.

sjPlot 1.3 available #rstats #sjPlot

Developer snapshots of #sjPlot-package now on #Github #rstats

Finally, I managed to setup a GitHub repository. From now on, the latest developer snapshot of my sjPlot-package will be published right here: https://github.com/sjPlot/devel.

Please post issues there, download the latest developer build for testing purposes or help developing the wiki-page with examples for package usage etc.

Btw, if somebody knows, why I can’t get GitHub running with RStudio, let me know… I always get this issue, which was already reported by other users. Currently, I’m using the GitHub.app to commit changes.

Developer snapshots of #sjPlot-package now on #Github #rstats