lunes, 16 de julio de 2018

Donepezil for dementia due to Alzheimer's disease - Birks - 2018 - The Cochrane Library - Wiley Online Library

Donepezil for dementia due to Alzheimer's disease - Birks - 2018 - The Cochrane Library - Wiley Online Library

Donepezil for dementia due to Alzheimer's disease



Alzheimer's disease is the most common cause of dementia in older people. One approach to symptomatic treatment of Alzheimer's disease is to enhance cholinergic neurotransmission in the brain by blocking the action of the enzyme responsible for the breakdown of the neurotransmitter acetylcholine. This can be done by a group of drugs known as cholinesterase inhibitors. Donepezil is a cholinesterase inhibitor.

This review is an updated version of a review first published in 1998.


To assess the clinical efficacy and safety of donepezil in people with mild, moderate or severe dementia due to Alzheimer's disease; to compare the efficacy and safety of different doses of donepezil; and to assess the effect of donepezil on healthcare resource use and costs.

Search methods

We searched Cochrane Dementia and Cognitive Improvement's Specialized Register, MEDLINE, Embase, PsycINFO and a number of other sources on 20 May 2017 to ensure that the search was as comprehensive and up-to-date as possible. In addition, we contacted members of the Donepezil Study Group and Eisai Inc.

Selection criteria

We included all double-blind, randomised controlled trials in which treatment with donepezil was administered to people with mild, moderate or severe dementia due to Alzheimer's disease for 12 weeks or more and its effects compared with those of placebo in a parallel group of patients, or where two different doses of donepezil were compared.

Data collection and analysis

One reviewer (JSB) extracted data on cognitive function, activities of daily living, behavioural symptoms, global clinical state, quality of life, adverse events, deaths and healthcare resource costs. Where appropriate and possible, we estimated pooled treatment effects. We used GRADE methods to assess the quality of the evidence for each outcome.

Main results

Thirty studies involving 8257 participants met the inclusion criteria of the review, of which 28 studies reported results in sufficient detail for the meta-analyses. Most studies were of six months' duration or less. Only one small trial lasted 52 weeks. The studies tested mainly donepezil capsules at a dose of 5 mg/day or 10 mg/day. Two studies tested a slow-release oral formulation that delivered 23 mg/day. Participants in 21 studies had mild to moderate disease, in five studies moderate to severe, and in four severe disease. Seventeen studies were industry funded or sponsored, four studies were funded independently of industry and for nine studies there was no information on source of funding.

Our main analysis compared the safety and efficacy of donepezil 10 mg/day with placebo at 24 to 26 weeks of treatment. Thirteen studies contributed data from 3396 participants to this analysis. Eleven of these studies were multicentre studies. Seven studies recruited patients with mild to moderate Alzheimer's disease, two with moderate to severe, and four with severe Alzheimer's disease, with a mean age of about 75 years. Almost all evidence was of moderate quality, downgraded due to study limitations.

After 26 weeks of treatment, donepezil compared with placebo was associated with better outcomes for cognitive function measured with the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog, range 0 to 70) (mean difference (MD) -2.67, 95% confidence interval (CI) -3.31 to -2.02, 1130 participants, 5 studies), the Mini-Mental State Examination (MMSE) score (MD 1.05, 95% CI 0.73 to 1.37, 1757 participants, 7 studies) and the Severe Impairment Battery (SIB, range 0 to 100) (MD 5.92, 95% CI 4.53 to 7.31, 1348 participants, 5 studies). Donepezil was also associated with better function measured with the Alzheimer's Disease Cooperative Study activities of daily living score for severe Alzheimer's disease (ADCS-ADL-sev) (MD 1.03, 95% CI 0.21 to 1.85, 733 participants, 3 studies). A higher proportion of participants treated with donepezil experienced improvement on the clinician-rated global impression of change scale (odds ratio (OR) 1.92, 95% CI 1.54 to 2.39, 1674 participants, 6 studies). There was no difference between donepezil and placebo for behavioural symptoms measured by the Neuropsychiatric Inventory (NPI) (MD -1.62, 95% CI -3.43 to 0.19, 1035 participants, 4 studies) or by the Behavioural Pathology in Alzheimer's Disease (BEHAVE-AD) scale (MD 0.4, 95% CI -1.28 to 2.08, 194 participants, 1 study). There was also no difference between donepezil and placebo for Quality of Life (QoL) (MD -2.79, 95% CI -8.15 to 2.56, 815 participants, 2 studies).

Participants receiving donepezil were more likely to withdraw from the studies before the end of treatment (24% versus 20%, OR 1.25, 95% CI 1.05 to 1.50, 2846 participants, 12 studies) or to experience an adverse event during the studies (72% vs 65%, OR 1.59, 95% 1.31 to 1.95, 2500 participants, 10 studies).

There was no evidence of a difference between donepezil and placebo for patient total healthcare resource utilisation.

Three studies compared donepezil 10 mg/day to donepezil 5 mg/day over 26 weeks. The 5 mg dose was associated with slightly worse cognitive function on the ADAS-Cog, but not on the MMSE or SIB, with slightly better QoL and with fewer adverse events and withdrawals from treatment. Two studies compared donepezil 10 mg/day to donepezil 23 mg/day. There were no differences on efficacy outcomes, but fewer participants on 10 mg/day experienced adverse events or withdrew from treatment.

Authors' conclusions

There is moderate-quality evidence that people with mild, moderate or severe dementia due to Alzheimer's disease treated for periods of 12 or 24 weeks with donepezil experience small benefits in cognitive function, activities of daily living and clinician-rated global clinical state. There is some evidence that use of donepezil is neither more nor less expensive compared with placebo when assessing total healthcare resource costs. Benefits on 23 mg/day were no greater than on 10 mg/day, and benefits on the 10 mg/day dose were marginally larger than on the 5 mg/day dose, but the rates of withdrawal and of adverse events before end of treatment were higher the higher the dose.

Plain language summary

Donepezil for people with dementia due to Alzheimer's disease

Review question

What effects (benefits or harms) does donepezil have on people with dementia due to Alzheimer's disease?


Alzheimer's disease is the most common cause of dementia. As the disease progresses, people lose the ability to remember, communicate, think clearly and perform the activities of daily living. Their behaviour may also change. In severe Alzheimer's disease people lose the ability to care for themselves.

The most commonly used treatment for Alzheimer's disease are medicines known as acetylcholinesterase inhibitors. Donepezil is one of these medicines. It is taken as a pill once a day.

In Alzheimer's disease, one of the changes in the brain is a reduced number of nerve cells called cholinergic neurones. These are nerve cells that signal to other cells using a chemical called acetylcholine. Acetylcholinesterase inhibitors, such as donepezil, work by preventing acetylcholine from being broken down. This may improve the symptoms of dementia. However, acetylcholine is also found elsewhere in the body and so drugs of this type may have unwanted effects.

Review methods

In this review we examined evidence about benefits and harms from studies that compared donepezil, taken for at least 12 weeks, to placebo (a dummy pill), or that compared different doses of donepezil. The studies had to be double-blind and randomised, that is, the decision whether people taking part got donepezil or placebo had to be made randomly and neither they nor the researchers should have known which treatment they were getting while the trial was going on. This was to make the comparison as unbiased, or fair, as possible. We searched for studies up to May 2017. We assessed the quality of all the studies we included. When it was sensible to do so, we analysed the results of studies together to get an overall result.

Key results

We included 30 studies with 8257 participants. Most of the people in the studies had mild or moderate dementia due to Alzheimer's disease, but in nine studies they had moderate or severe dementia. Almost all of the studies lasted six months or less. The majority of the studies were known to have been funded by the manufacturer of donepezil.

We found that people with Alzheimer's disease who took 10 mg of donepezil a day for six months did slightly better than people taking placebo, on scales measuring their cognitive function (e.g. thinking and remembering), how well they could manage their daily activities, and the overall impression of a trained researcher. We did not find any effect on behaviour or quality of life.

People taking donepezil were more likely than those taking placebo to report side effects and to drop out of the studies. Most side effects were described as mild. Nausea, vomiting and diarrhoea were most common.

Comparing 5 mg of donepezil a day with 10 mg/day, people on 5 mg had fewer side effects, but did slightly less well on cognitive function tests. A higher dose (23 mg/day) offered no advantages and was associated with more side effects.

There is some evidence that use of donepezil is neither more nor less expensive than placebo when total health care costs are taken into account.

Quality of the evidence

In general, we thought that the quality of the evidence was moderate. The main factor reducing our confidence was concern that the results of some studies might have been biased by the way they were done. We cannot be sure that the results apply to treatment longer than six months.


After six months of treatment, there are benefits of donepezil that are large enough to measure in studies. It is associated with side effects that are mainly mild, but that may cause people to stop treatment.

Being able to stabilise cognitive performance or ability to maintain activities of daily living may be important clinically. In terms of total healthcare costs the use of donepezil appears cost neutral. However, there does not appear to be an effect on quality of life. More data are still required from longer-term clinical studies examining measures of disease progression or time to needing full time care.

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martes, 10 de julio de 2018

Risks and benefits of direct oral anticoagulants versus warfarin in a real world setting: cohort study in primary care


Risks and benefits of direct oral anticoagulants versus warfarin in a real world setting: cohort study in primary care

BMJ 2018362 doi: (Published 04 July 2018)Cite this as: BMJ 2018;362:k2505
  1. Yana Vinogradova, research fellow1,
  2. Carol Coupland, professor11,
  3. Trevor Hill, research statistician1,
  4. Julia Hippisley-Cox, professor1
    Author affiliations
  1. Correspondence to: Y Vinogradova
  • Accepted 22 May 2018

Combined Treatment With Chondroitin Sulfate and Glucosamine Sulfate Shows No Superiority Over Placebo

Free Access

Combined Treatment With Chondroitin Sulfate and Glucosamine Sulfate Shows No Superiority Over Placebo for Reduction of Joint Pain and Functional Impairment in Patients With Knee Osteoarthritis: A Six‐Month Multicenter, Randomized, Double‐Blind, Placebo‐Controlled Clinical Trial

First published: 31 July 2016
Cited by: 17 identifier: NCT01893905.
Supported by Tedec Meiji Farma SA, Madrid, Spain.

lunes, 9 de julio de 2018

Qualitative Data Science: Using RQDA to analyse interviews | R-bloggers

Qualitative Data Science: Using RQDA to analyse interviews | R-bloggers

Qualitative Data Science: Using RQDA to analyse interviews

Qualitative data science sounds like a contradiction in terms. Data scientists generally solve problems using numerical solutions. Even the analysis of text is reduced to a numerical problem using Markov chains, topic analysis, sentiment analysis and other mathematical tools.

Scientists and professionals consider numerical methods the gold standard of analysis. There is, however, a price to pay when relying on numbers alone. Numerical analysis reduces the complexity of the social world. When analysing people, numbers present an illusion of precision and accuracy. Giving primacy to quantitative research in the social sciences comes at a high price. The dynamics of reality are reduced to statistics, losing the narrative of the people that the research aims to understand.

Being both an engineer and a social scientist, I acknowledge the importance of both numerical and qualitative methods. My dissertation used a mixed-method approach to review the relationship between employee behaviour and customer perception in water utilities. This article introduces some aspects of qualitative data science with an example from my dissertation.

In this article, I show how I analysed data from interviews using both quantitative and qualitative methods and demonstrate why qualitative data science is better to understand text than numerical methods. The most recent version of the code is available on my GitHub repository. Unfortunately I cannot share the data set as this contains personally identifying data.

Qualitative Data Science

Qualitative Data Science

The often celebrated artificial intelligence of machine learning is impressive but does not come close to human intelligence and ability to understand the world. Many data scientists are working on automated text analysis to solve this issue (the topicmodels package is an example of such an attempt). These efforts are impressive but even the smartest text analysis algorithm is not able to derive meaning from text. To fully embrace all aspects of data science we need to be able to methodically undertake qualitative data analysis.

The capabilities of R in numerical analysis are impressive but it can also assist with Qualitative Data Analysis (QDA). Huang Ronggui from Hong Kong developed the RQDA package to analyse texts in R. RQDA assists with qualitative data analysis using a GUI front-end to analyse collections texts. The video below contains a complete course in using this software. Below the video, I share an example from my dissertation which compares qualitative and quantitative methods for analysing text.

For my dissertation about water utility marketing, I interviewed seven people from various organisations. The purpose of these interviews was to learn about the value proposition for water utilities. The data consists of the transcripts of six interviews which I manually coded using RQDA. For reasons of agreed anonymity, I cannot provide the raw data file of the interviews through GitHub.

Numerical Text Analysis

Word clouds are a popular method for exploratory analysis of texts. The wordcloud is created with the text mining and wordcloud packages. The transcribed interviews are converted to a text corpus (the native format for the tm package) and whitespace, punctuation etc is removed. This code snippet opens the RQDA file and extracts the texts from the database. RQDA stores all text in an SQLite database and the package provides a query command to extract data.

library(tidyverse)  library(RQDA)  library(tm)  library(wordcloud)  library(topicmodels)  library(igraph)  library(RQDA)  library(tm)    openProject(&quot;stakeholders.rqda&quot;)  interviews <- RQDAQuery(&quot;SELECT file FROM source&quot;)  interviews$file <- apply(interviews, 1, function(x) gsub("…", "...", x))  interviews$file <- apply(interviews, 1, function(x) gsub("'", "", x))  interviews <- Corpus(VectorSource(interviews$file))  interviews <- tm_map(interviews, stripWhitespace)  interviews <- tm_map(interviews, content_transformer(tolower))  interviews <- tm_map(interviews, removeWords, stopwords("english"))  interviews <- tm_map(interviews, removePunctuation)  interviews <- tm_map(interviews, removeNumbers)  interviews <- tm_map(interviews, removeWords, c("interviewer", "interviewee"))    library(wordcloud)  set.seed(1969)  wordcloud(interviews, min.freq = 10, max.words = 50, rot.per=0.35,             colors = brewer.pal(8, "Blues")[-1:-5])  
Word cloud of interview transcripts.

This word cloud makes it clear that the interviews are about water businesses and customers, which is a pretty obvious statement. The interviews are also about the opinion of the interviewees (think). While the word cloud is aesthetically pleasing and provides a quick snapshot of the content of the texts, they cannot inform us about their meaning.

Topic modelling is a more advanced method to extract information from the text by assessing the proximity of words to each other. The topic modelling package provides functions to perform this analysis. I am not an expert in this field and simply followed basic steps using default settings with four topics.

library(topicmodels)  dtm <- DocumentTermMatrix(interviews)  dtm <- removeSparseTerms(dtm, 0.99)  ldaOut <- LDA(dtm, k = 4)  terms(ldaOut, 6)  

This code converts the corpus created earlier into a Document-Term Matrix, which is a matrix of words and documents (the interviews) and the frequency at which each of these words occurs. The LDA function applies a Latent Dietrich Allocation to the matrix to define the topics. The variable k defines the number of anticipated topics. An LDA is similar to clustering in multivariate data. The final output is a table with six words for each topic.

Topic 1 Topic 2 Topic 3 Topic 4
water water customers water
think think water think
actually inaudible customer companies
customer people think yeah
businesses service business issues
customers businesses service don't

This table does not tell me much at all about what was discussed in the interviews. Perhaps it is the frequent use of the word "water" or "think"—I did ask people their opinion about water-related issues. To make this analysis more meaningful I could perhaps manually remove the words water, yeah, and so on. This introduces bias in the analysis and reduces the reliability of the topic analysis because I would be interfering with the text.

Numerical text analysis sees a text as a bag of words instead of a set of meaningful words. It seems that any automated text mining needs a lot of manual cleaning to derive anything meaningful. This excursion shows that automated text analysis is not a sure-fire way to analyse the meaning of a collection of words. After a lot of trial and error to try to make this work, I decided to go back to my roots of qualitative analysis using RQDA as my tool.

Qualitative Data Science Using RQA

To use RQDA for qualitative data science, you first need to manually analyse each text and assign codes (topics) to parts of the text. The image below shows a question and answer and how it was coded. All marked text is blue and the codes are shown between markers. Coding a text is an iterative process that aims to extract meaning from a text. The advantage of this method compared to numerical analysis is that the researcher injects meaning into the analysis. The disadvantage is that the analysis will always be biased, which in the social sciences is unavoidable. My list of topics was based on words that appear in a marketing dictionary so that I analysed the interviews from that perspective.

Example of text coded with RQDA.

My first step was to look at the occurrence of codes (themes) in each of the interviews.

codings <- getCodingTable()[,4:5]  categories <- RQDAQuery("SELECT AS category, AS filename                            FROM treefile, filecat, source                            WHERE treefile.catid = filecat.catid AND treefile.fid = AND treefile.status = 1")  codings <- merge(codings, categories, all.y = TRUE)  head(codings)  reorder_size <- function(x) {      factor(x, levels = names(sort(table(x))))  }  ggplot(codings, aes(reorder_size(codename), fill=category)) + geom_bar() +       facet_grid(~filename) + coord_flip() +       theme(legend.position = "bottom", legend.title = element_blank()) +       ylab("Code frequency in interviews") + xlab("Code")  

The code uses an internal RQDA function getCodingTable to obtain the primary data. The RQDAQuery function provides more flexibility and can be used to build more complex queries of the data. You can view the structure of the RQDA database using the RQDATables function.

The occurrence of themes from six interviews.

This bar chart helps to explore the topics that interviewees discussed but it does not help to understand how these topic relate to each other. This method provides a better view than the 'bag of words' approach because the text has been given meaning.

RQDA provides a facility to assign each code to a code category. This structure can be visualised using a network. The network is visualised using the igraph package and the graph shows how codes relate to each other.

Qualitative data analysis can create value from a text by interpreting it from a given perspective. This article is not even an introduction to the science and art of qualitative data science. I hope it invites you to explore RQA and similar tools.

If you are interested in finding out more about how I used this analysis, then read chapter three of my dissertation on customer service in water utilities.

Network diagram with communities of interview topics.
edges <- RQDAQuery("SELECT, FROM codecat, freecode, treecode                       WHERE codecat.catid = treecode.catid AND = treecode.cid")  g <- graph_from_edgelist(as.matrix(edges), directed = FALSE)  V(g)$name <- gsub(" ", "\n", V(g)$name)    c <-  par(mar=rep(0,4))  set.seed(666)  plot(c, g,        vertex.size=10,       vertex.color=NA,       vertex.frame.color=NA,       edge.width=E(g)$weight,       layout=layout.drl)  

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Falls and Frailty in Prostate Cancer Survivors: Current, Past, and Never Users of Androgen Deprivation Therapy - Winters‐Stone - 2017 - Journal of the American Geriatrics Society - Wiley Online Library

Falls and Frailty in Prostate Cancer Survivors: Current, Past, and Never Users of Androgen Deprivation Therapy - Winters‐Stone - 2017 - Journal of the American Geriatrics Society - Wiley Online Library

Falls and Frailty in Prostate Cancer Survivors: Current, Past, and Never Users of Androgen Deprivation Therapy

Journal of the American Geriatrics Society



To compare the prevalence of and association between falls and frailty of prostate cancer survivors (PCSs) who were current, past or never users of androgen deprivation therapy (ADT).




Mail and electronic survey.


PCSs (N = 280; mean age 72 ± 8).


Cancer history, falls, and frailty status (robust, prefrail, frail) using traditionally defined and obese phenotypes.


Current (37%) or past (34%) ADT users were more than twice as likely to have fallen in the previous year as never users (15%) (P = .002). ADT users had twice as many recurrent falls (P < .001) and more fall‐related injuries than unexposed men (P = .01). Current (43%) or past (40%) ADT users were more likely to be classified as prefrail or frail than never users (15%) (P < .001), and the prevalence of combined obese frailty + prefrailty was even greater in current (59%) or past (62%) ADT users than never users (25%) (P < .001). Traditional and obese frailty significantly increased the likelihood of reporting falls in the previous year (odds ratio (OR) = 2.15, 95% CI = 1.18–3.94 and OR = 2.97, 95% CI = 1.62–5.58, respectively) and was also associated with greater risk of recurrent falls (OR = 3.10, 95% CI = 1.48–6.5 and OR = 3.99, 95% CI = 1.79–8.89, respectively).


Current and past exposure to ADT is linked to higher risk of falls and frailty than no treatment. PCSs should be appropriately counseled on fall prevention strategies, and approaches to reduce frailty should be considered.

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