The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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mice: Multivariate Imputation by Chained Equations in R

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It is important that the medical profession play a significant role in critically evaluating the use of diagnostic procedures and therapies as they are introduced in the detection, management,

/pdf/acc-aha-2006-guidelines-for-the-management-of-patients-with-27qfh9fc7i.pdf

ACC/AHA 2006 Guidelines for the Management of Patients With Valvular Heart Disease

Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

/pdf/robins-i-a-tool-for-assessing-risk-of-bias-in-non-randomised-2037fcklle.pdf

ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions.

Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. Copyright © 2010 John Wiley & Sons, Ltd.

Multiple imputation using chained equations: Issues and guidance for practice

Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them

Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.

Personalized model to predict keratoconus progression from demographic, topographic and genetic data.

Prescriptions for working statisticians. Albert Madansky, Springer‐Verlag, New York, 1988. No. of pages: xviii + 295. Price: DM 74

MFPIGEN: Stata module for modelling and displaying interactions between continuous predictors

gam fits a generalized or proportional hazards additive model (GAM) by mazimizing a penalized log likelihood function. Each component of the resulting estimated function of the covariates is a cubic smoothing spline. The smoothness of each component function is determined by the 'equivalent degrees of freedom' of the corresponding covariate. Models supported: Normal (Gaussian) errors, binomial, Poisson, gamma, Cox (now with Stata's stcox), and link functions among identity, log, logit and inverse. This package is an update to accommodate the latest versions of the Windows operating system (specifically, Win 2000 and XP) and supersedes the version published in the STB (Royston P, Ambler G (1998) Generalized additive models. Stata Technical Bulletin 42: 38-43.)

GAM: Stata module for generalised additive models

The logrank test is probably the most commonly used tool for analysing clinical trials with a survival time outcome. The planning of such a trial must take into account not only the suggested method of analysis but also circumstances not encountered in other types of experiments. Patient accrual into a trial is sequential. Also, when complete this is usually followed by a period during which patients are under observation but no new patients are entered. Hence, administrative censoring occurs, implying that some patients will not have experienced an event by the time the trial ends. Schoenfeld and Freedman were among the first to propose sample size formulae for comparing two survival distributions under the logrank test while taking into account administrative censoring.

Patrick Royston

Papers

Personalized model to predict keratoconus progression from demographic, topographic and genetic data.

Prescriptions for working statisticians. Albert Madansky, Springer‐Verlag, New York, 1988. No. of pages: xviii + 295. Price: DM 74

MFPIGEN: Stata module for modelling and displaying interactions between continuous predictors

GAM: Stata module for generalised additive models

Evaluation of sample size and power for multi-arm survival trials allowing for non-proportional hazards, loss to follow-up and cross-over