Veröffentlichung: Patientenorientierung und vernetzte Versorgung

Mein Promotionsverfahren ist endlich erfolgreich abgeschlossen, und das möchte ich zum Anlass nehmen, um mein Buch zu bewerben. In meiner Arbeit geht es um Steuerungsmechanismen von Versorgungsnetzwerken (also Kooperation von Leistungserbringern im Gesundheitssystem) und die Frage, wie sich solche Versorgungsnetze stabilisieren und Versorgungsqualität sicherstellen. Der Gegenstand wird aus einer systemtheoretischen und netzwerktheoretischen Perspektive analysiert, ergänzt durch qualitativ-empirische Analysen. Ich zitierte den Klappentext:

untitled

Patientenorientierung gewinnt zunehmend an Bedeutung und wird als wesentlicher Bestandteil zur Verbesserung der Versorgungsqualität angesehen. Für Leistungserbringer liegt die Herausforderung in der Sicherstellung einer patientenorientierten Versorgung bei finanziell begrenzten Ressourcen. Steuerungsmechanismen in der vernetzten Versorgung müssen sicherstellen, dass dies nicht zu Gunsten des Profitstrebens vernachlässigt wird. In der vorliegenden Arbeit wird der Frage nachgegangen, wie sich Versorgungsnetze koordinieren lassen und beteiligte Organisationen Patientenorientierung umsetzen.

Lüdecke D (2014) Patientenorientierung und vernetzte Versorgung. Eine qualitative Studie. Berlin, Münster: LIT-Verlag (Homepage)

Veröffentlichung: Patientenorientierung und vernetzte Versorgung

Beautiful table-outputs: Summarizing mixed effects models #rstats

The current version 1.8.1 of my sjPlot package has two new functions to easily summarize mixed effects models as HTML-table: sjt.lmer and sjt.glmer. Both are very similar, so I focus on showing how to use sjt.lmer here.

# load required packages
library(sjPlot) # table functions
library(sjmisc) # sample data
library(lme4) # fitting models

Linear mixed models summaries as HTML table

The sjt.lmer function prints summaries of linear mixed models (fitted with the lmer function of the lme4-package) as nicely formatted html-tables. First, some sample models are fitted:

# load sample data
data(efc)
# prepare grouping variables
efc$grp = as.factor(efc$e15relat)
levels(x = efc$grp) <- get_val_labels(efc$e15relat)
efc$care.level <- as.factor(rec(efc$n4pstu, "0=0;1=1;2=2;3:4=4"))
levels(x = efc$care.level) <- c("none", "I", "II", "III")

# data frame for fitted model
mydf <- data.frame(neg_c_7 = as.numeric(efc$neg_c_7),
                   sex = as.factor(efc$c161sex),
                   c12hour = as.numeric(efc$c12hour),
                   barthel = as.numeric(efc$barthtot),
                   education = as.factor(efc$c172code),
                   grp = efc$grp,
                   carelevel = efc$care.level)

# fit sample models
fit1 <- lmer(neg_c_7 ~ sex + c12hour + barthel + (1|grp), data = mydf)
fit2 <- lmer(neg_c_7 ~ sex + c12hour + education + barthel + (1|grp), data = mydf)
fit3 <- lmer(neg_c_7 ~ sex + c12hour + education + barthel +
              (1|grp) +
              (1|carelevel), data = mydf)

The simplest way of producing the table output is by passing the fitted models as parameter. By default, estimates (B), confidence intervals (CI) and p-values (p) are reported. The models are named Model 1 and Model 2. The resulting table is divided into three parts:

  • Fixed parts – the model’s fixed effects coefficients, including confidence intervals and p-values.
  • Random parts – the model’s group count (amount of random intercepts) as well as the Intra-Class-Correlation-Coefficient ICC.
  • Summary – Observations, AIC etc.

Continue reading “Beautiful table-outputs: Summarizing mixed effects models #rstats”

Beautiful table-outputs: Summarizing mixed effects models #rstats

Designvertrauen

Ursprünglich veröffentlicht auf The Catjects Project:

Stammeskulturen hatten Vertrauen in die Magie, antike Hochkulturen in die Götter und die Moderne in die Technik. Die nächste Gesellschaft hat nur noch Vertrauen in das Design. Aber was heißt “nur”? Das Design ermöglicht beides, eine Beobachtung im Umgang mit der Welt und eine Beobachtung der Beobachter im Umgang mit der Welt. In dieser doppelten Funktion tritt es an die Stelle der Magie, der Götter und der Technik, ohne diese restlos zu ersetzen. Im Gegenteil, es übernimmt Aspekte dieser früheren Mechanismen der Ungewissheitsabsorption und entwickelt sich nur in der Hinsicht über sie hinaus, als es bestimmte Aspekte der Vernetzung von Mensch, Umwelt, Technik und Gesellschaft reflexiver behandelt, als dies möglicherweise früher der Fall war.

Denn das ist die These, die wir hier verfolgen. Jede Gesellschaft bedarf eines Mechanismus der Ungewissheitsabsorption; und das Design übernimmt diese Funktion in unserer, der nächsten nach der modernen Gesellschaft… Weiterlesen: pdf.

Thesenpapier zum Symposium…

Original ansehen noch 15 Wörter

Designvertrauen

sjmisc – package for working with (labelled) data #rstats

The sjmisc-package

My last posting was about reading and writing data between R and other statistical packages like SPSS, Stata or SAS. After that, I decided to bundle all functions that are not directly related to plotting or printing tables, into a new package called sjmisc.

Basically, this package covers three domains of functionality:

  • reading and writing data between other statistical packages (like SPSS) and R, based on the haven and foreign packages; hence, sjmisc also includes function to work with labelled data.
  • frequently used statistical tests, or at least convenient wrappers for such test functions
  • frequently applied recoding and variable conversion tasks

In this posting, I want to give a quick and short introduction into the labeling features.

Continue reading “sjmisc – package for working with (labelled) data #rstats”

sjmisc – package for working with (labelled) data #rstats

Reading from and writing to SPSS, SAS and STATA with R #rstats #sjPlot

On CRAN now

My sjPlot-package was updated on CRAN (binaries will be available soon, I guess). This update contains, besides many small improvements and fixes, two major features:

  1. First, new features to print table summaries of linear models and generalized linear models (for sjt.glm, the same new features were added as to sjt.lm – however, the manual page is not finished yet). I have introduced these features in a former posting.
  2. Second, functions for reading data from and writing to other statistical packages like SPSS, SAS or STATA have been revamped or new features have been added. Furthermore, there are improved getters and setters to extract and set variable and value labels. A short introduction is available online.

Continue reading “Reading from and writing to SPSS, SAS and STATA with R #rstats #sjPlot”

Reading from and writing to SPSS, SAS and STATA with R #rstats #sjPlot

CRAN download statistics of any packages #rstats

Hadley Wickham announced at Twitter that RStudio now provides CRAN package download logs. I was wondering about the download numbers of my package and wrote some code to extract that information from the logs…

Continue reading “CRAN download statistics of any packages #rstats”

CRAN download statistics of any packages #rstats

Beautiful tables for linear model summaries #rstats

Beautiful HTML tables of linear models

In this blog post I’d like to show some (old and) new features of the sjt.lm function from my sjPlot-package. These functions are currently only implemented in the development snapshot on GitHub. A package update is planned to be submitted soon to CRAN.

There are two new major features I added to this function: Comparing models with different predictors (e.g. stepwise regression) and automatic grouping of categorical predictors. There are examples below that demonstrate these features.

The sjt.lm function prints results and summaries of linear models as HTML-table. These tables can be viewed in the RStudio Viewer pane, web browser or easily exported to office applications. See also my former posts on the table printing functions of my package here and here.

Please note: The following tables may look a bit cluttered – this is because I just pasted the HTML-code created by knitr into this blog post, so style sheets may interfere. The original online-manual for this function can be found here.

Continue reading “Beautiful tables for linear model summaries #rstats”

Beautiful tables for linear model summaries #rstats

sjPlot package and related online manuals updated #rstats # ggplot

My sjPlot package for data visualization has just been updated on CRAN. I’ve added some features to existing function, which I want to introduce here.

Plotting linear models

So far, plotting model assumptions of linear models or plotting slopes for each estimate of linear models were spread over several functions. Now, these plot types have been integrated into the sjp.lm function, where you can select the plot type with the type parameter. Furthermore, plotting standardized coefficients now also plot the related confidence intervals.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.lm

Plotting generalized linear models

Beside odds ratios, you now can also plot the predicted probabilities of the outcome for each predictor of generalized linear models. In case you have continuous variables, these kind of plots may be more intuitive than an odds ratio value.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.glm

Plotting (generalized) linear mixed effects models

The plotting function for creating plots of (generalized) linear mixed effects models (sjp.lmer and sjp.glmer) also got new plot types over the course of the last weeks.

For sjp.lmer, we have

  • re (default) for estimates of random effects
  • fe for estimates of fixed effects
  • fe.std for standardized estimates of fixed effects
  • fe.cor for correlation matrix of fixed effects
  • re.qq for a QQ-plot of random effects (random effects quantiles against standard normal quantiles)
  • fe.ri for fixed effects slopes depending on the random intercept.

and for sjp.glmer, we have

  • re (default) for odds ratios of random effects
  • fe for odds ratios of fixed effects
  • fe.cor for correlation matrix of fixed effects
  • re.qq for a QQ-plot of random effects (random effects quantiles against standard normal quantiles)
  • fe.pc or fe.prob to plot probability curves (predicted probabilities) of all fixed effects coefficients. Use facet.grid to decide whether to plot each coefficient as separate plot or as integrated faceted plot.
  • ri.pc or ri.prob to plot probability curves (predicted probabilities) of random intercept variances for all fixed effects coefficients. Use facet.grid to decide whether to plot each coefficient as separate plot or as integrated faceted plot.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.lmer and www.strengejacke.de/sjPlot/sjp.glmer

Plotting interaction terms of (generalized) linear (mixed effects) models

Another function, where new features were added, is sjp.int (formerly known as sjp.lm.int). This function is now kind of generic and can plot interactions of

  • linar models (lm)
  • generalized linar models (glm)
  • linar mixed effects models (lme4::lmer)
  • generalized linar mixed effects models (lme4::glmer)

For linear models (both normal and mixed effects), slopes of interaction terms are plotted. For generalized linear models, the predicted probabilities of the outcome towards the interaction terms is plotted.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.int

Plotting Likert scales

Finally, a comprehensive documentation for the sjp.likert function is finsihed, which can be found here:
www.strengejacke.de/sjPlot/sjp.likert

sjPlot package and related online manuals updated #rstats # ggplot

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