http://blog.gmane.org/gmane.comp.lang.r.epi hourly 1 1901-01-01T00:00+00:00 http://gmane.org/img/gmane-25t.png http://gmane.org http://comments.gmane.org/gmane.comp.lang.r.epi/130 <pre>R friends, with some help of a friend and the original author I turn available this function which is helping me in some analysis. Perhaps it may be useful to others... :-) 1. Gwet KL. Computing inter-rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol. 2008 ;61(Pt 1):29–48. # AC1 statistic for 2 raters special case # table = k x k table which represents table(rater1,rater2), must have equal number of rows and columns # N = population size which will be stick in standard error correction, N=Inf is no correction. # conflev = Confidence Level associated with the confidence interval (0.95 is the default value) AC1 &lt;- function(table,conflev=0.95,N=Inf,print=TRUE){ if(dim(table)[1] != dim(table)[2]){ stop('The table should have the same number of rows and columns!') } n &lt;- sum(table) f &lt;- n/N pa &lt;- sum(diag(table))/n # formula 18 q &lt;- ncol(table) # number of categories pkk &lt;- diag(table)/n pak &lt;- sapply(1:q,function(i)sum(table[i,]))/n pbk &lt;- sapply(1:q,function(i)sum(table[,i]))/n pik &lt;- (pak + pbk)/2 pegama &lt;- (sum(pik*(1-pik)))/(q-1) gama &lt;- (pa - pegama)/(1 - pegama) # AC1 statistics # 2 raters special case variance pkl &lt;- table/n soma &lt;- 0; for(k in 1:q){ for(l in 1:q){ soma &lt;- soma + (pkl[k,l]*((1-(pik[k]+pik[l])/2)^2)) } } vgama &lt;- ((1-f)/(n*(1-pegama)^2)) * (pa*(1-pa) - 4*(1-gama)*((1/(q-1))*sum(pkk*(1-pik)) - pa*pegama) + 4*((1-gama)^2) * ((1/((q-1)^2))*soma - pegama^2)) epgama &lt;- sqrt(vgama)# AC1 standard error lcb &lt;- max(0,gama - epgama*qnorm(1-(1-conflev)/2,0,1)) # lower confidence bound ucb &lt;- min(1,gama + epgama*qnorm(1-(1-conflev)/2,0,1)) # upper confidence bound if(print==TRUE){ cat('Raw agreement:',pa,'Chance-independent agreement:',pegama,'\n') cat('Agreement coeficient (AC1):',gama,'AC1 standard error:',epgama,'\n') cat(conflev*100,'% Confidence Interval (AC1): (',lcb,',',ucb,')\n') } invisible(c(pa,pegama,gama,epgama,lcb,ucb)) } # table3 &lt;- matrix(c(118,2,5,0),nrow=2,ncol=2) # AC1(table3) # x &lt;- AC1(table3,print=F) # print(x) Regards, Dr. Pedro Emmanuel A. A. do Brasil Curriculum Lattes: http://lattes.cnpq.br/6597654894290806 Instituto de Pesquisa Clínica Evandro Chagas Fundação Oswaldo Cruz Rio de Janeiro - Brasil Av. Brasil 4365, CEP 21040-360, Tel 55 21 3865-9648 email: pedro.brasil-am19qAsr2QmNrImTOV1V8w&lt; at &gt;public.gmane.org email: emmanuel.brasil-Re5JQEeQqe8AvxtiuMwx3w&lt; at &gt;public.gmane.org ---Apoio aos softwares livres www.zotero.org - gerenciamento de referências bibliográficas. www.broffice.org ou www.libreoffice.org - textos, planilhas ou apresentações. www.epidata.dk - entrada de dados. www.r-project.org - análise de dados. www.ubuntu.com - sistema operacional [[alternative HTML version deleted]] </pre> Pedro Emmanuel Alvarenga Americano do Brasil 2012-05-03T17:58:17 http://comments.gmane.org/gmane.comp.lang.r.epi/127 <pre> Hello! I have a doubt in relation with the use of the cosinor model with R when I have not only multiple components, but also linear, quadratic and cubic trends. I explain the details: In simple cosinor: Y = M + A * cos(2 * π* t / T + φ) + error T knowed: y = M + β* X1 + ϒ* X2 + error,   Where X1 = cos (2 * π* t / T) and X2 = sin(2 * π* t / T)   In this case, I use lm(y ~ X1 + X2) .   For the multiple components case:   y = M + ∑Aj * cos(2 * π * t /Tj + φj) + error   In this case I use:  lm(y ~ X1tot + X2tot)   However, in the generalized model case (linear, quadratic and cubic trends), I don’t know how to analyze with R.   I include the situacion formula:   y = M + α1 * t + α2 * t2 + α3 * t3+ ∑Aj * cos(2 *π* t /Tj + φ2) + error   What function in R can I use, as I used lm for the simple cosinor model and the multiple components model?.   How can I obtain global information and separated information?. I refer to obtain information for each contribution (linear tren, quadratic trend, … and so on).   Thank you very much for your help. [[alternative HTML version deleted]] </pre> Iva P 2012-03-18T10:21:11 http://comments.gmane.org/gmane.comp.lang.r.epi/123 <pre>Dear List, I am attempting to carry out linear excess relative risk modelling for the purposes of a nested case-control study of nuclear industry workers. The majority of similar analyses in the literature have been carried out using EPICURE, SAS or STATA, or have been implemented directly in FORTRAN. For a number of reasons, it would be preferable for me to carry out the analysis in R, but I am having difficulty finding an appropriate R package. Many thanks in advance for any advice on this matter, James Grellier James Grellier PhD CREAL-Centre for Research in Environmental Epidemiology Parc de Recerca Biomèdica de Barcelona (despatx 183.01.A) Doctor Aiguader, 88 | 08003 Barcelona Tel. +34 932 147 345 jgrellier-MstSlu9blcReoWH0uzbU5w&lt; at &gt;public.gmane.org &lt;http://www.creal.cat/&gt; www.creal.cat [[alternative HTML version deleted]] </pre> Grellier, James 2012-01-11T13:08:55 http://comments.gmane.org/gmane.comp.lang.r.epi/122 <pre>Course on Age-Period-Cohort models. =================================== Place: Lisbon Time: Monday 19th - Wednesday 21st September 2011. Deadline: 1 September 2011 Fee: 380 EUR. Teacher: Bendix Carstensen, Steno Diabetes Center, Copenhagen URL: http://www.ceaul.fc.ul.pt/seeevent.html?id=206 http://staff.pubhealth.ku.dk/~bxc/APC/Lisboa-2011 ( The latter contains links to various material on the topic ) Audience: Epidemiologists and Statisticians. Age-Period-Cohort models for mortality and morbidity rates is a useful tool for description of age- and time-trends. But the use and in particular the reporting is not straightforward. The course will cover both the basics of tabulation of data, modeling and choice of parametrization as well as the more advanced aspects of Age-Period-Cohort models, including a substantial practical component, based on computer exercises in R. Specifically the apc-tools in the Epi package for R will be covered in some depth. Formally no prerequisites are required, but familiarity with regression models in epidemiology and some exposure to R will prove advantageous. Participants are expected to bring their own laptop, and can expect to go home with a couple of thorough analyses of example datasets. Instructions on what to install (and how) will be given shortly after the application deadline. _________________________________________ Bendix Carstensen Senior Statistician Steno Diabetes Center A/S Niels Steensens Vej 2-4 DK-2820 Gentofte Denmark +45 44 43 87 38 (direct) +45 30 75 87 38 (mobile) bxc-77mEMlKuB9s&lt; at &gt;public.gmane.org www.biostat.ku.dk/~bxc www.steno.dk </pre> BXC (Bendix Carstensen 2011-07-23T12:07:31 http://comments.gmane.org/gmane.comp.lang.r.epi/121 <pre>Dear epidemiological data analysis and R experts, cloudnumbers.com provides researchers and companies with the resources to perform high performance calculations in the cloud. As cloudnumbers.com's community manager I may invite you to register and test R on a computer cluster in the cloud for free: http://my.cloudnumbers.com/register Our aim is to change the way of research collaboration is done today by bringing together scientists and businesses from all over the world on a single platform. cloudnumbers.com is a Berlin (Germany) based international high-tech startup striving for enabling everyone to benefit from the High Performance Computing related advantages of the cloud. We provide easy access to applications running on any kind of computer hardware: from single core high memory machines up to 1000 cores computer clusters. Our platform provides several advantages: * Turn fixed into variable costs and pay only for the capacity you need. Watch our latest saving costs with cloudnumbers.com video: http://www.youtube.com/watch?v=ln_BSVigUhg&amp;feature=player_embedded * Enter the cloud using an intuitive and user friendly platform. Watch our latest cloudnumbers.com in a nutshell video: http://www.youtube.com/watch?v=0ZNEpR_ElV0&amp;feature=player_embedded * Be released from ongoing technological obsolescence and continuous maintenance costs (e.g. linking to libraries or system dependencies) * Accelerated your R, C, C++, Fortran, Python, ... calculations through parallel processing and great computing capacity - more than 1000 cores are available and GPUs are coming soon. * Share your results worldwide (coming soon). * Get high speed access to public databases. * We have developed a security architecture that meets high requirements of data security and privacy. Read our security white paper: http://d1372nki7bx5yg.cloudfront.net/wp-content/uploads/2011/06/cloudnumberscom-security.whitepaper.pdf This is only a selection of our top features. To get more information check out our web-page (http://www.cloudnumbers.com/) or follow our blog about cloud computing, HPC and HPC applications (with R): http://cloudnumbers.com/blog Register and test for free now at cloudnumbers.com: http://my.cloudnumbers.com/register We are looking forward to get your feedback and consumer insights. Best Markus </pre> Markus Schmidberger 2011-07-07T18:11:25 http://comments.gmane.org/gmane.comp.lang.r.epi/120 <pre>Many of the subscribers to R-SIG-Epi will be familiar with the course Statistical Practice in Epidemiology with R, which normally takes place in Tartu, Estonia. This year, the course will take place in Lyon, France 5-9 September. The course is intended for epidemiologists and statisticians who wish to use R for statistical modelling and analysis of epidemiological data. The aim of the course is to give participants access to a set of tools in current use by statisticians in epidemiology. The course requires fairly good understanding of statistical principles and some familiarity with epidemiological concepts and study types. The course is mainly practically oriented with more than half the time at the computer. For more information see http://www.iarc.fr/en/education-training/R/ http://www.pubhealth.ku.dk/~BXC/SPE/ To apply for the course, download and fill in the application form. The deadline for applications is 21 May. ----------------------------------------------------------------------- This message and its attachments are strictly confidenti...{{dropped:8}} </pre> Martyn Plummer 2011-04-26T13:48:04 http://comments.gmane.org/gmane.comp.lang.r.epi/119 <pre>Dear All, I was wondering how I can generate adjusted odds ratio confidence intervals using the hglm package? I know you can simply use the exponential of the estimate to get the OR, but I am not sure how you can get the equivalent of the " logistic.display" in epicalc that will also generate the CI's. I know this is very elementary question. Russell " Skip" Barbour Ph.D. Yale School of Medicine 135 College St. Suit 200 New Haven , CT. 06510 Tel: 203 764 4332 Fax: 203 764 4353 Email: russell.barbour-LrD5EImo2rg&lt; at &gt;public.gmane.org&lt;mailto:russell.barbour-LrD5EImo2rg&lt; at &gt;public.gmane.org&gt; Doubt grows with knowledge. Johann Wolfgang von Goethe&lt;http://www.brainyquote.com/quotes/quotes/j/johannwolf380192.html&gt; [[alternative HTML version deleted]] </pre> Barbour, Russell 2011-03-30T21:37:18 http://comments.gmane.org/gmane.comp.lang.r.epi/110 <pre>Hi, I am trying to access object which is variable of a dataset using string.  For that I am trying to convert a string to the object name in the following code. But unfortunately it looks something is going wrong in the following code. data1&lt;- data.frame(x=c(1:4), y=rep(1:4)) gvars&lt;- c("x", "y") z&lt;- paste("data1", gvars[1], sep="$") get(z) The above code should return following output: [1] 1 2 3 4 but instead it is printing following error "Error in get(z) : object 'data1$x' not found" Please help me to fix the problem. Regards Madan </pre> Madan Gopal Kundu 2011-03-18T05:18:55 http://comments.gmane.org/gmane.comp.lang.r.epi/108 <pre>Dear Friends,   I have the following question on comparing calibration (using Hosmer-Lemeshow test) of two logistic regression models based on reported p-value.   Can we use p-values for comparing calibration for two different logistic regression models? If one model has p value .2 and another model has .8, is it justified to conclude that second model is better calibrated compare to the first model ? [My intuition says NO, because p- value only measure of consistency between the observed data and the null hypothesis. And, here the null hypothesis is that the model is well calibrated. p-value does not tell about how strong the calibration is.]   Thank you for your response.   Best regards, Suman Kundu PhD student. Erasmus MC, Rotterdam   [[alternative HTML version deleted]] </pre> Suman Kundu 2011-02-16T14:54:26 http://comments.gmane.org/gmane.comp.lang.r.epi/105 <pre>Dear Group, I am just a beginner in Epidemiology, epi-statistics and this group hence, in advance I apologize if I ask trivial question. I would like to ask how is it possible to calculate from GAM (general additive model) Relative Risk the model has following formula: log(E[O_i]) ~ log(N_i) + someCovariates_i, where Oi is number of cases of some disease in i-th area, N is population at risk in i-th area, other covariates are i-th region specific. I one paper authors calculated RR for each i-th region out of this model, but I have no idea how can I do it myself. Kindest regards, pj </pre> Przemysław Jura 2011-02-09T13:37:45 http://comments.gmane.org/gmane.comp.lang.r.epi/102 <pre>Dear all, I am planning to analyse linkage between two loci( first - diallelic, second - multiallelic (HLA-DRB1)). What function should i use? Usually I use LD(data) function but it doesn't work if number of locus' alleles more then 2. Thanks for your time. Best wishes, Ekaterina Kudryavtseva PhD student, Department of Molecular Biology Novosibirsk University Tel: +7-923-185-82-47 [[alternative HTML version deleted]] </pre> Екатерина Кудрявцева 2011-01-25T13:13:44 http://comments.gmane.org/gmane.comp.lang.r.epi/100 <pre>Dear all, I am planning to run a cox model with time-dependent covariates. I would like to use the 'fold' function (Fox, 2002: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf) to create the dataset, with one observation per subject &amp; time-window, and then use the 'coxph' function of the survival package to run the model. However, I had a couple of questions, and I can't find the answer on the internet: 1. In the document writtent by Fox, the new dataset created for the purpose of the time-dependent covariates model (Rossi.2) numbers the observations 1.1, 1.2, 1.3, 1.4, 1.5, etc. which corresponds to the measurements in the different time-windows for subject 1. My question is the following: if I run a cox model on such a dataset, is the model accounting for the fact that 1.1, 1.2, 1.3, ... belongs to the same subject? 2. I will have a big dataset, i.e. 40,000 subjects and 36 time-windows, i.e. 1,440,000 lines, and about 20 covariates. I was wondering if I should expect some big troubles to run my analysis, or can the software handle so many rows? Many thanks for your help and your time, Regards, Raphaelle </pre> raphaëlle métras 2011-01-17T12:22:47 http://comments.gmane.org/gmane.comp.lang.r.epi/99 <pre>Hello R friends, This last year I became awere of some modern strategies of varible selection for regression analisys, such as bootstrap, bayesian approaches and others. Nevertheless, most analysts (that Im awere of) find it usefull to compare crude measures with adjusted ones to have an idea of direction and magnitude of confounding. In a recent report, I had some work to make a table with several crude risk ratios, then I made a function to invoke riskratio function and display the riskratio of several exposures for the same outcome. It was inspired in epicalc::tableStack. Unfortunately, I could not avoid the many for(), but I guess it would not be much a problem with small datasets. It assumes that both the exposures and the outcome are binary variables. But I guess that with little improvementes could work with more categories too. Similar to tableStack, its output may be easily be exported to a spreadsheet with write.table or other export function. I hope it is usefull to others too. rrStack &lt;- function(outcome, exposure, data=.data, method = "wald", conf.level = 0.95, rev = "neither", correction = FALSE, verbose = FALSE, replicates = 5000, digits=2){ ret &lt;- lapply(1:length(exposure),function(i)riskratio(table(data[,exposure[i]],data[,outcome]), method = method, conf.level = conf.level, rev = rev, correction = correction, verbose = verbose, replicates = replicates)) variables &lt;- c(exposure[1],NA) for(i in 2:length(exposure)){temp &lt;- c(exposure[i],NA); variables &lt;- c(variables,temp)} categories &lt;- rownames(ret[[1]]$measure) for(i in 2:length(exposure)){temp &lt;- rownames(ret[[i]]$measure); categories &lt;- c(categories,temp)} N &lt;- unname(ret[[1]]$data[1:2,3]) for(i in 2:length(exposure)){temp &lt;- unname(ret[[i]]$data[1:2,3]); N &lt;- c(N,temp)} risk &lt;- unname(ret[[1]]$data[1:2,2]) / unname(ret[[1]]$data[1:2,3]) for(i in 2:length(exposure)){temp &lt;- unname(ret[[i]]$data[1:2,2]) / unname(ret[[i]]$data[1:2,3]); risk &lt;- c(risk,temp)} lower &lt;- binom.exact(ret[[1]]$data[1:2,2], ret[[1]]$data[1:2,3], conf.level = conf.level)$lower for(i in 2:length(exposure)){temp &lt;- binom.exact(ret[[i]]$data[1:2,2], ret[[i]]$data[1:2,3], conf.level = conf.level)$lower; lower &lt;- c(lower,temp)} upper &lt;- binom.exact(ret[[1]]$data[1:2,2], ret[[1]]$data[1:2,3], conf.level = conf.level)$upper for(i in 2:length(exposure)){temp &lt;- binom.exact(ret[[i]]$data[1:2,2], ret[[i]]$data[1:2,3], conf.level = conf.level)$upper; upper &lt;- c(upper,temp)} RR &lt;- unname(ret[[1]]$measure) colnames(RR) &lt;- c('RR','lower','upper') for(i in 2:length(exposure)){temp &lt;- unname(ret[[i]]$measure); RR &lt;- rbind(RR,temp)} pvalue &lt;- unname(ret[[1]]$p.value) colnames(pvalue) &lt;- c('midp.exact','fisher.exact','chi.square') for(i in 2:length(exposure)){temp &lt;- unname(ret[[i]]$p.value); pvalue &lt;- rbind(pvalue,temp)} table &lt;- as.data.frame(cbind(cbind(variables,categories),round(cbind(N,risk,lower,upper,RR,pvalue),digits=digits))) print(table,quote = FALSE,na.print='') invisible(table) } variables categories N risk lower upper RR lower upper midp.exact fisher.exact chi.square 1 SEXO Male 99 0.22 0.14 0.32 1 2 Female 55 0.15 0.06 0.27 0.65 0.31 1.37 0.26 0.29 0.25 3 HIV Positive 60 0.32 0.2 0.45 1 4 Negative 94 0.12 0.06 0.2 0.37 0.19 0.72 0 0 0 5 HCV Positive 9 0.11 0 0.48 1 6 Negative 145 0.2 0.14 0.27 1.8 0.28 11.75 0.58 1 0.51 Warning message: In chisq.test(xx, correct = correction) : Chi-squared approximation may be incorrect Kind regards, Abraço forte e que a força esteja com você, Dr. Pedro Emmanuel A. A. do Brasil Instituto de Pesquisa Clínica Evandro Chagas Fundação Oswaldo Cruz Rio de Janeiro - Brasil Av. Brasil 4365 Tel 55 21 3865-9648 email: pedro.brasil-am19qAsr2QmNrImTOV1V8w&lt; at &gt;public.gmane.org email: emmanuel.brasil-Re5JQEeQqe8AvxtiuMwx3w&lt; at &gt;public.gmane.org ---Apoio aos softwares livres www.zotero.org - gerenciamento de referências bibliográficas. www.broffice.org ou www.openoffice.org - textos, planilhas ou apresentações. www.epidata.dk - entrada de dados. www.r-project.org - análise de dados. www.ubuntu.com - sistema operacional [[alternative HTML version deleted]] </pre> Pedro Emmanuel Alvarenga Americano do Brasil 2011-01-08T02:55:04 http://comments.gmane.org/gmane.comp.lang.r.epi/96 <pre> I am trying to make a function that works like the command Frequencies of Epiinfo. I am a beginner user and I make something like this: freq&lt;-function(x){ Frecuencia&lt;-table(x,exclude=NULL); Porcentaje&lt;-as.vector(round(prop.table(table(x,exclude=NULL)),2)); Porc&lt;-as.vector(round(prop.table(table(x,exclude=NULL))*100,1)); Acumulado&lt;-cumsum(Porc); IC95.Sup&lt;-round(as.vector(Porc+196*sqrt(Porcentaje*(1-Porcentaje)/sum(Frecue ncia))),1); IC95.Inf&lt;-round(as.vector(Porc-196*sqrt(Porcentaje*(1-Porcentaje)/sum(Frecue ncia))),1); matrix( c(Frecuencia,sum(Frecuencia),Porc,round(sum(Porc),1), Acumulado,NA,IC95.Inf,NA,IC95.Sup,NA),length(Frecuencia)+1,5, dimnames=list(c(labels(Frecuencia)[[1]],"Total"),c("Frecuencia","Porcentaje" ,"Acumulado","[IC--","--95%]")) ); } There is a way more efficient to create a function like this? there is a way to make it more visually - friendly adding something like -------------- that stat.table does? How can I add an argument like missing=T that show NA's and missing=F that exclude Na's?? Thanks in advance. Diego. [[alternative HTML version deleted]] </pre> Diego Garcilazo 2010-11-29T21:36:21 http://comments.gmane.org/gmane.comp.lang.r.epi/93 <pre>Sir, Its wonderful to start using this software R. I am making full efforts to learn it on my own with the help of the free online manuals available. I am using epicalc package in R. I have a dataset where i want to apply conditions like... if var1 is yes or 1, assign 1 to it or else 0. Can you help me with this problem? Thanking you, </pre> Dr Khan Amir Maroof 2010-11-27T05:30:44 http://comments.gmane.org/gmane.comp.lang.r.epi/90 <pre>To someone who could help me: I am a PhD student, working in the Novosibirsk University, Russia. I am now using glm() function to do logistic regression analysis. I cannot understand how the chosen reference group. For example, for the three genotypes of GG, GC, CC I want to be risk assessed in relation to the genotype CC, but the program selects the genotype GG. How do I change the reference group? Thanks for your time. Best wishes, Ekaterina Kudryavtseva PhD student, Department of Molecular Biology Novosibirsk University Tel: +7-923-185-82-47 </pre> Екатерина Кудрявцева 2010-10-28T07:56:53 http://comments.gmane.org/gmane.comp.lang.r.epi/88 <pre>Dear friends, I am a PhD student at Erasmus Medical Centre, Rotterdam, The Netherlands. I found the R package "pls", which is very handy and useful. This package descibes how to use Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR). Would you mind to kindly answer the following? Logistic regression (LR) is a commonly used method in medical science to make a prediction model for binary outcome variable. This is fine, and I would like to apply (if applicable) PLSR and PCR to construct a prediction model for binary outcome variable, and will check whether PLSR or PCR method helps to make better model compare to LR method, in the sence of discrimination and goodness-of-fit (calibration). However, I am not sure whether the R package "pls" allows to construct such a model. I will be pleased if you kindly let me know whether PLSR ( or PCR) method is applicable for binary outcome variable. Thank you very much for your suggestions. Kind regards, Suman Kundu [[alternative HTML version deleted]] </pre> Suman Kundu 2010-10-27T08:48:52 http://comments.gmane.org/gmane.comp.lang.r.epi/87 <pre>Dear list, This is a somewhat off-topic question as I don't have a specific issue with an R-function, but rather seek advice on how to proceed with my analyses. I have survival data on approximately 2000 subjects followed for about 35 years. The subjects had their first medical examination at the age of 50 and were examined almost every 10th year after that, so the data is in long form with multiple records per subject. The event of interest now is cardiovascular mortality with mortality from other causes being a competing event. I consider myself a novice when it comes to analysis of these kinds of data, so as far as I understand estimating the survival function is not of much use as I have time-varying covariates. I therefore turned my attention to the Poisson representation of the Cox model. I have created a dataset with yearly increments of time (with age as the time-scale) using the Lexis and splitLexis functions in the Epi package v. 1.1.17. Additionaly the dataset has been duplicated and the status variable changed according to type of event. The main issue for me now is the interpretation of the effects. My model looks like this: mymodel &lt;- glm(status ~ type*( bs(age, 3)*( ... covariates ...))+offset(log(duration)), family=poisson(), data = mydata) where status is a dummy for event / no event and type = the type of event. My intention is to model the effect of the covariates and age on the type of event (the type * rest interaction) and allow for possible time-dependent effects of the (time-varying) covariates (the age * covariates interaction). My question is whether one can estimate age-specific incidence rates for some values of the covariates this way? (for example. the incidence rates for cardiovascular mortality among males aged 50 are x% y% and z% and among males aged 60 a% b% and c% for bmi values of 25, 30 and 35 ...) Thank you for your time. /Erik </pre> Erik Lampa 2010-10-13T09:10:46 http://comments.gmane.org/gmane.comp.lang.r.epi/86 <pre>We're trying to identify articles where investigators have used mvtnorm to estimate rates of occurrence of a condition or rates of eligibility for services. In our application we used a pentavariate normal cumulative distribution function to determine how many children are likely be eligible for disability services because they fall -2 SD on any of 5 developmental domains. Thanks Steve Steven Rosenberg, Ph.D. Associate Professor Department of Psychiatry University of Colorado Denver Campus Box C268-22 4455 E. 12th Avenue Denver CO 80220 303-315-9121 [[alternative HTML version deleted]] </pre> Rosenberg, Steven 2010-10-08T18:21:18 http://comments.gmane.org/gmane.comp.lang.r.epi/82 <pre>Hello, I was wondering who to contact to see about somehow interconnecting or integrating the programs R and Epi Info. I have been involved in the development of Epi Info which is a software program developed by CDC (www.cdc.gov/epiinfo&lt;http://www.cdc.gov/epiinfo&gt;). Epi Info allows for creating data entry systems and performs certain epidemiologic and statistical analyses. Currently Epi Info is in the public domain and I believe future versions will be open source. It might be useful to pull together some of the Epi Info development team members and R team members to discuss how the programs might complement each other. Note that I send this e-mail as an effort to combine the efforts of these two groups and this is not an official request from CDC. Thanks, Kevin Kevin M. Sullivan, PhD, MPH, MHA Associate Professor Department of Epidemiology Rollins School of Public Health at Emory Univerisity Claudia Nance Rollins Building (CNR), Room 3051 1518 Clifton Road, NE Atlanta, GA 30322 USA Office Phone: 404-727-8714 Office Fax: 404-727-8737 Cell phone: 404-210-2668 ________________________________ This e-mail message (including any attachments) is for t...{{dropped:14}} </pre> Sullivan, Kevin M 2010-09-01T19:08:21 http://comments.gmane.org/gmane.comp.lang.r.epi/78 <pre>Hi all, Being new to analysing epidemiologic data, I have a question about the following data set: A friend has inspected three randomly chosen farms (random factor 'farm'). At each farm three randomly chosen series of chickens (random factor 'flock') were each inspected for the presence of a certain bacteria. The contaminated chickens were counted (response variable 'positives'). The sample sizes per flock are given by 'broilers'. We want to have a look at within-broilers, within-farm and between-farm variability. It seems to me that we have a random-effects model, in which the factor 'flock' is nested within the factor 'farm'. Am I correct so far? Now, since the response variable yields count data, fitting the model should be done using Poisson regression. Correct? Could somebody help me out with (an example of) such an analysis? Many thanks in advance, Dieter Here is the dataset used: broilers&lt;-data.frame(farm=c("FA","FA","FA","FB","FB","FB","FC","FC","FC"), flock=c("a","b","c","d","e","f","g","h","i"), broilers=c(50, rep(25,8)), positives=c(7,2,0,7,2,0,0,5,2)) -- Dieter Anseeuw Katho Campus Roeselare Wilgenstraat 32 8800 Roeselare Belgium Direct phone: +32 51 23 29 68 http://www.katho.be/hivb http://www.linkedin.com/in/dieteranseeuw [[alternative HTML version deleted]] </pre> dieter.anseeuw 2010-08-17T05:28:47 Search the mailing list at Gmane query http://search.gmane.org/?group=$group=gmane.comp.lang.r.epi