https://ctep.cancer.gov/protocolDevelopment/docs/recist_guideline.pdf

New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)

Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8

This article is categorized under: 

Technologies > Classification
Technologies > Machine Learning
Technologies > Prediction
Technologies > Statistical Fundamentals

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Classification and regression trees

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

BACKGROUND: Somatoform disorders are characterised by chronic, medically unexplained physical symptoms (MUPS). Although different medications are part of treatment routines for people with somatoform disorders in clinics and private practices, there exists no systematic review or meta-analysis on the efficacy and tolerability of these medications. We aimed to synthesise to improve optimal treatment decisions.OBJECTIVES: To assess the effects of pharmacological interventions for somatoform disorders (specifically somatisation disorder, undifferentiated somatoform disorder, somatoform autonomic dysfunction, and pain disorder) in adults.SEARCH METHODS: We searched the Cochrane Depression, Anxiety and Neurosis Review Group's Specialised Register (CCDANCTR) (to 17 January 2014). This register includes relevant randomised controlled trials (RCTs) from The Cochrane Library (all years), MEDLINE (1950 to date), EMBASE (1974 to date), and PsycINFO (1967 to date). To identify ongoing trials, we searched ClinicalTrials.gov, Current Controlled Trials metaRegister, the World Health Organization International Clinical Trials Registry Platform, and the Chinese Clinical Trials Registry. For grey literature, we searched ProQuest Dissertation {\&} Theses Database, OpenGrey, and BIOSIS Previews. We handsearched conference proceedings and reference lists of potentially relevant papers and systematic reviews and contacted experts in the field.SELECTION CRITERIA: We selected RCTs or cluster RCTs of pharmacological interventions versus placebo, treatment as usual, another medication, or a combination of different medications for somatoform disorders in adults. We included people fulfilling standardised diagnostic criteria for somatisation disorder, undifferentiated somatoform disorder, somatoform autonomic dysfunction, or somatoform pain disorder.DATA COLLECTION AND ANALYSIS: One review author and one research assistant independently extracted data and assessed risk of bias. Primary outcomes included the severity of MUPS on a continuous measure, and acceptability of treatment.MAIN RESULTS: We included 26 RCTs (33 reports), with 2159 participants, in the review. They examined the efficacy of different types of antidepressants, the combination of an antidepressant and an antipsychotic, antipsychotics alone, or natural products (NPs). The duration of the studies ranged between two and 12 weeks.One meta-analysis of placebo-controlled studies showed no clear evidence of a significant difference between tricyclic antidepressants (TCAs) and placebo for the outcome severity of MUPS (SMD -0.13; 95{\%} CI -0.39 to 0.13; 2 studies, 239 participants; I(2) = 2{\%}; low-quality evidence). For new-generation antidepressants (NGAs), there was very low-quality evidence showing they were effective in reducing the severity of MUPS (SMD -0.91; 95{\%} CI -1.36 to -0.46; 3 studies, 243 participants; I(2) = 63{\%}). For NPs there was low-quality evidence that they were effective in reducing the severity of MUPS (SMD -0.74; 95{\%} CI -0.97 to -0.51; 2 studies, 322 participants; I(2) = 0{\%}).One meta-analysis showed no clear evidence of a difference between TCAs and NGAs for severity of MUPS (SMD -0.16; 95{\%} CI -0.55 to 0.23; 3 studies, 177 participants; I(2) = 42{\%}; low-quality evidence). There was also no difference between NGAs and other NGAs for severity of MUPS (SMD -0.16; 95{\%} CI -0.45 to 0.14; 4 studies, 182 participants; I(2) = 0{\%}).Finally, one meta-analysis comparing selective serotonin reuptake inhibitors (SSRIs) with a combination of SSRIs and antipsychotics showed low-quality evidence in favour of combined treatment for severity of MUPS (SMD 0.77; 95{\%} CI 0.32 to 1.22; 2 studies, 107 participants; I(2) = 23{\%}).Differences regarding the acceptability of the treatment (rate of all-cause drop-outs) were neither found between NGAs and placebo (RR 1.01, 95{\%} CI 0.64 to 1.61; 2 studies, 163 participants; I(2) = 0{\%}; low-quality evidence) or NPs and placebo (RR 0.85, 95{\%} CI 0.40 to 1.78; 3 studies, 506 participants; I(2) = 0{\%}; low-quality evidence); nor between TCAs and other medication (RR 1.48, 95{\%} CI 0.59 to 3.72; 8 studies, 556 participants; I(2) =14{\%}; low-quality evidence); nor between antidepressants and the combination of an antidepressant and an antipsychotic (RR 0.80, 95{\%} CI 0.25 to 2.52; 2 studies, 118 participants; I(2) = 0{\%}; low-quality evidence). Percental attrition rates due to adverse effects were high in all antidepressant treatments (0{\%} to 32{\%}), but low for NPs (0{\%} to 1.7{\%}).The risk of bias was high in many domains across studies. Seventeen trials (65.4{\%}) gave no information about random sequence generation and only two (7.7{\%}) provided information about allocation concealment. Eighteen studies (69.2{\%}) revealed a high or unclear risk in blinding participants and study personnel; 23 studies had high risk of bias relating to blinding assessors. For the comparison NGA versus placebo, there was relatively high imprecision and heterogeneity due to one outlier study. Although we identified 26 studies, each comparison only contained a few studies and small numbers of participants so the results were imprecise.AUTHORS' CONCLUSIONS: The current review found very low-quality evidence for NGAs and low-quality evidence for NPs being effective in treating somatoform symptoms in adults when compared with placebo. There was some evidence that different classes of antidepressants did not differ in efficacy; however, this was limited and of low to very low quality. These results had serious shortcomings such as the high risk of bias, strong heterogeneity in the data, and small sample sizes. Furthermore, the significant effects of antidepressant treatment have to be balanced against the relatively high rates of adverse effects. Adverse effects produced by medication can have amplifying effects on symptom perceptions, particularly in people focusing on somatic symptoms without medical causes. We can only draw conclusions about short-term efficacy of the pharmacological interventions because no trial included follow-up assessments. For each of the comparisons where there were available data on acceptability rates (NGAs versus placebo, NPs versus placebo, TCAs versus other medication, and antidepressants versus a combination of an antidepressant and an antipsychotic), no clear differences between the intervention and comparator were found.Future high-quality research should be carried out to determine the effectiveness of medications other than antidepressants, to compare antidepressants more thoroughly, and to follow-up participants over longer periods (the longest follow up was just 12 weeks). Another idea for future research would be to include other outcomes such as functional impairment or dysfunctional behaviours and cognitions as well as the classical outcomes such as symptom severity, depression, or anxiety.

Pharmacological interventions for somatoform disorders in adults.

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

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Missing data: Our view of the state of the art.

In medical science, studies are often designed to investigate changes in a specific parameter which is measured repeatedly over time in the participating subjects. This allows one to model the process of change within individuals. Although this process occurs in every individual, the inter subject variability can be high. For example, using data of 955 men, Brant et al showed that the average rates of increase of systolic blood pressure (SBP) are smallest in the younger age groups, and greatest in the older age groups, that obese individuals tend to have a higher SBP than non-obese individuals, and that individuals in more recent birth cohorts have lower SBP’s than those born before 1910. However, these factors are not sufficient to explain all the heterogeneity between individuals since, after correction for age, obesity and birth cohort, individuals with SBP’s above (below) average at initial examination, still have slower (faster) rates of longitudinal change in SBP.

Linear Mixed Models for Longitudinal Data

Missing data in clinical trials can have a major effect on the validity of the inferences that can be drawn from the trial. This article reviews methods for preventing missing data and, failing that, dealing with data that are missing.

/pdf/the-prevention-and-treatment-of-missing-data-in-clinical-gvpp8p81b1.pdf

The Prevention and Treatment of Missing Data in Clinical Trials

Introduction.- Motivating Studies.- Generalized Linear Models.- Linear Mixed Models for Gaussian Longitudinal Data.- Model Families.- The Strength of Marginal Models.- Likelihood-based Models.- Generalized Estimating Equations.- Pseudo-likelihood.- Fitting Marginal Models with SAS.- Conditional Models.- Pseudo-likehood.- From Subject-Specific to Random-Effects Models.- Generalized Linear Mixed Models (GLMM).- Fitting Generalized Linear Mixed Models with SAS.- Marginal Versus Random-Effects Models.- Ordinal Data.- The Epilepsy Data.- Non-linear Models.- Psuedo-likelihood for a Hierarchical Model.- Random-effects Models with Serial Correlation.- Non-Gaussian Random Effects.- Joint Continuous and Discrete Responses.- High-dimensional Multivariate Repeated Measurements.- Missing Data Concepts.- Simple Methods, Direct Likelikhood and WGEE.- Multiple Imputation and the Expectation-Maximization Algorithm.- Selection Models.- Pattern-mixture Models.- Sensitivity Analysis.- Incomplete Data and SAS.

Models for Discrete Longitudinal Data

(2005). Applied Longitudinal Analysis. Journal of the American Statistical Association: Vol. 100, No. 470, pp. 709-710.

Applied Longitudinal Analysis

Preface. Acknowledgements. I Preliminaries. 1 Introduction. 1.1 From Imbalance to the Field of Missing Data Research. 1.2 Incomplete Data in Clinical Studies. 1.3 MAR, MNAR, and Sensitivity Analysis. 1.4 Outline of the Book. 2 Key Examples. 2.1 Introduction. 2.2 The Vorozole Study. 2.3 The Orthodontic Growth Data. 2.4 Mastitis in Dairy Cattle. 2.5 The Depression Trials. 2.6 The Fluvoxamine Trial. 2.7 The Toenail Data. 2.8 Age-Related Macular Degeneration Trial. 2.9 The Analgesic Trial. 2.10 The Slovenian Public Opinion Survey. 3 Terminology and Framework. 3.1 Modelling Incompleteness. 3.2 Terminology. 3.3 Missing Data Frameworks. 3.4 Missing Data Mechanisms. 3.5 Ignorability. 3.6 Pattern-Mixture Models. II Classical Techniques and the Need for Modelling. 4 A Perspective on Simple Methods. 4.1 Introduction. 4.2 Simple Methods. 4.3 Problems with Complete Case Analysis and Last Observation Carried Forward. 4.4 Using the Available Cases: a Frequentist versus a Likelihood Perspective. 4.5 Intention to Treat. 4.6 Concluding Remarks. 5 Analysis of the Orthodontic Growth Data. 5.1 Introduction and Models. 5.2 The Original, Complete Data. 5.3 Direct Likelihood. 5.4 Comparison of Analyses. 5.5 Example SAS Code for Multivariate Linear Models. 5.6 Comparative Power under Different Covariance Structures. 5.7 Concluding Remarks. 6 Analysis of the Depression Trials. 6.1 View 1: Longitudinal Analysis. 6.2 Views 2a and 2b and All versus Two Treatment Arms. III Missing at Random and Ignorability. 7 The Direct Likelihood Method. 7.1 Introduction. 7.2 Ignorable Analyses in Practice. 7.3 The Linear Mixed Model. 7.4 Analysis of the Toenail Data. 7.5 The Generalized Linear Mixed Model. 7.6 The Depression Trials. 7.7 The Analgesic Trial. 8 The Expectation-Maximization Algorithm. 8.1 Introduction. 8.2 The Algorithm. 8.3 Missing Information. 8.4 Rate of Convergence. 8.5 EM Acceleration. 8.6 Calculation of Precision Estimates. 8.7 A Simple Illustration. 8.8 Concluding Remarks. 9 Multiple Imputation. 9.1 Introduction. 9.2 The Basic Procedure. 9.3 Theoretical Justification. 9.4 Inference under Multiple Imputation. 9.5 Efficiency. 9.6 Making Proper Imputations. 9.7 Some Roles for Multiple Imputation. 9.8 Concluding Remarks. 10 Weighted Estimating Equations. 10.1 Introduction. 10.2 Inverse Probability Weighting. 10.3 Generalized Estimating Equations for Marginal Models. 10.4 Weighted Generalized Estimating Equations. 10.5 The Depression Trials. 10.6 The Analgesic Trial. 10.7 Double Robustness. 10.8 Concluding Remarks. 11 Combining GEE and MI. 11.1 Introduction. 11.2 Data Generation and Fitting. 11.3 MI-GEE and MI-Transition. 11.4 An Asymptotic Simulation Study. 11.5 Concluding Remarks. 12 Likelihood-Based Frequentist Inference. 12.1 Introduction. 12.2 Information and Sampling Distributions. 12.3 Bivariate Normal Data. 12.4 Bivariate Binary Data. 12.5 Implications for Standard Software. 12.6 Analysis of the Fluvoxamine Trial. 12.7 The Muscatine Coronary Risk Factor Study. 12.8 The Crepeau Data. 12.9 Concluding Remarks. 13 Analysis of the Age-Related Macular Degeneration Trial. 13.1 Introduction. 13.2 Direct Likelihood Analysis of the Continuous Outcome. 13.3 Weighted Generalized Estimating Equations. 13.4 Direct Likelihood Analysis of the Binary Outcome. 13.5 Multiple Imputation. 13.6 Concluding Remarks. 14 Incomplete Data and SAS. 14.1 Introduction. 14.2 Complete Case Analysis. 14.3 Last Observation Carried Forward. 14.4 Direct Likelihood. 14.5 Weighted Estimating Equations. 14.6 Multiple Imputation. IV Missing Not at Random. 15 Selection Models. 15.1 Introduction. 15.2 The Diggle-Kenward Model for Continuous Outcomes. 15.3 Illustration and SAS Implementation. 15.4 An MNAR Dale Model. 15.5 A Model for Non-monotone Missingness. 15.6 Concluding Remarks. 16 Pattern-Mixture Models. 16.1 Introduction. 16.2 A Simple Gaussian Illustration. 16.3 A Paradox. 16.4 Strategies to Fit Pattern-Mixture Models. 16.5 Applying Identifying Restrictions. 16.6 Pattern-Mixture Analysis of the Vorozole Study. 16.7 A Clinical Trial in Alzheimer's Disease. 16.8 Analysis of the Fluvoxamine Trial. 16.9 Concluding Remarks. 17 Shared-Parameter Models. 18 Protective Estimation. 18.1 Introduction. 18.2 Brown's Protective Estimator for Gaussian Data. 18.3 A Protective Estimator for Categorical Data. 18.4 A Protective Estimator for Gaussian Data. 18.5 Concluding Remarks. V Sensitivity Analysis. 19 MNAR, MAR, and the Nature of Sensitivity. 19.1 Introduction. 19.2 Every MNAR Model Has an MAR Bodyguard. 19.3 The General Case of Incomplete Contingency Tables. 19.4 The Slovenian Public Opinion Survey. 19.5 Implications for Formal and Informal Model Selection. 19.6 Behaviour of the Likelihood Ratio Test for MAR versus MNAR. 19.7 Concluding Remarks. 20 Sensitivity Happens. 20.1 Introduction. 20.2 A Range of MNAR Models. 20.3 Identifiability Problems. 20.4 Analysis of the Fluvoxamine Trial. 20.5 Concluding Remarks. 21 Regions of Ignorance and Uncertainty. 21.1 Introduction. 21.2 Prevalence of HIV in Kenya. 21.3 Uncertainty and Sensitivity. 21.4 Models for Monotone Patterns. 21.5 Models for Non-monotone Patterns. 21.6 Formalizing Ignorance and Uncertainty. 21.7 Analysis of the Fluvoxamine Trial. 21.8 Artificial Examples. 21.9 The Slovenian Public Opinion Survey. 21.10 Some Theoretical Considerations. 21.11 Concluding Remarks. 22 Local and Global Influence Methods. 22.1 Introduction. 22.2 Gaussian Outcomes. 22.3 Mastitis in Dairy Cattle. 22.4 Alternative Local Influence Approaches. 22.5 The Milk Protein Content Trial. 22.6 Analysis of the Depression Trials. 22.7 A Local Influence Approach for Ordinal Data with Dropout. 22.8 Analysis of the Fluvoxamine Data. 22.9 A Local Influence Approach for Incomplete Binary Data. 22.10 Analysis of the Fluvoxamine Data. 22.11 Concluding Remarks. 23 The Nature of Local Influence. 23.1 Introduction. 23.2 The Rats Data. 23.3 Analysis and Sensitivity Analysis of the Rats Data. 23.4 Local Influence Methods and Their Behaviour. 23.5 Concluding Remarks. 24 A Latent-Class Mixture Model for Incomplete Longitudinal Gaussian Data. 24.1 Introduction. 24.2 Latent-Class Mixture Models. 24.3 The Likelihood Function and Estimation. 24.4 Classification. 24.5 Simulation Study. 24.6 Analysis of the Depression Trials. 24.7 Concluding Remarks. VI Case Studies. 25 The Age-Related Macular Degeneration Trial. 25.1 Selection Models and Local Influence. 25.2 Local Influence Analysis. 25.3 Pattern-Mixture Models. 25.4 Concluding Remarks. 26 The Vorozole Study. 26.1 Introduction. 26.2 Exploring the Vorozole Data. 26.3 A Selection Model for the Vorozole Study. 26.4 A Pattern-Mixture Model for the Vorozole Study. 26.5 Concluding Remarks. References. Index.

Geert Molenberghs

Papers

Linear Mixed Models for Longitudinal Data

The Prevention and Treatment of Missing Data in Clinical Trials

Models for Discrete Longitudinal Data

Applied Longitudinal Analysis

Missing Data in Clinical Studies