DOCSLIB.ORG

Analysis of Epidemiological Data Using R and Epicalc

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Analysis of Epidemiological Data Using R and Epicalc > help.start() > help.start() > exp(-5) > exp(-5) [1] 0.006738 [1] 0.006738 9 9 > log(3.8 > log(3.8 + ) + ) 50 [1] 1.335001 [1] 1.335001 40 30 Virasakdi Chongsuvivatwong 20 10 Analysis of epidemiological0 data using R and Epicalc Epidemiology Unit Prince of Songkla University THAILAND EPICALC–OK.indd 1 20.2.2008 15:24:54 Analysis of Epidemiological Data Using R and Epicalc Author: Virasakdi Chongsuvivatwong [email protected] Epidemiology Unit Prince of Songkla University THAILAND Preface Data analysis is very important in epidemiological research. The capacity of computing facilities has been steadily increasing, moving state of the art epidemiological studies along the same direction of computer advancement. Currently, there are many commercial statistical software packages widely used by epidemiologists around the world. For developed countries, the cost of software is not a major problem. For developing countries however, the real cost is often too high. Several researchers in developing countries thus eventually rely on a pirated copy of the software. Freely available software packages are limited in number and readiness of use. EpiInfo, for example, is free and useful for data entry and simple data analysis. Advanced data analysts however find it too limited in many aspects. For example, it is not suitable for data manipulation for longitudinal studies. Its regression analysis facilities cannot cope with repeated measures and multi-level modelling. The graphing facilities are also limited. A relatively new and freely available software called R is promising. Supported by leading statistical experts worldwide, it has almost everything that an epidemiological data analyst needs. However, it is difficult to learn and to use compared with similar statistical packages for epidemiological data analysis such as Stata. The purpose of this book is therefore to bridge this gap by making R easy to learn for researchers from developing countries and also to promote its use. My experience in epidemiological studies spans over twenty years with a special fondness of teaching data analysis. Inspired by the spirit of the open-source software philosophy, I have spent a tremendous effort exploring the potential and use of R. For four years, I have been developing an add-on package for R that allows new researchers to use the software with enjoyment. More than twenty chapters of lecture notes and exercises have been prepared with datasets ready for self-study. Supported by WHO, TDR and the Thailand Research Fund, I have also run a number of workshops for this software in developing countries including Thailand, Myanmar, North Korea, Maldives and Bhutan, where R and Epicalc was very much welcomed. With this experience, I hereby propose that the use of this software should be encouraged among epidemiological researchers, especially for those who cannot afford to buy expensive commercial software packages. i R is an environment that can handle several datasets simultaneously. Users get access to variables within each dataset either by copying it to the search path or by including the dataset name as a prefix. The power of R in this aspect is a drawback in data manipulation. When creating a variable or modifying an existing one, without prefixing the dataset name, the new variable is isolated from its parental dataset. If prefixing is the choice, the original data is changed but not the copy in the search path. Careful users need to remove the copy in the search path and recopy the new dataset into it. The procedure in this aspect is clumsy. Not being tidy will eventually end up with too many copies in the search path overloading the system or confusing the analyst on where the variable is actually located. Epicalc presents a concept solution for common types of work where the data analyst works on one dataset at a time using only a few commands. In Epicalc the user can virtually eliminate the necessity of specifying the dataset and can avoid overloading of the search path very effectively and efficiently. In addition to make tidying of memory easy to accomplished, Epicalc makes it easy to recognize the variables by adopting variable labels or descriptions which have been prepared from other software such as SPSS or Stata or locally prepared by Epicalc itself. R has very powerful graphing functions that the user has to spend time learning. Epicalc exploits this power by producing a nice plot of the distribution automatically whenever a single variable is summarised. A breakdown of the first variable by a second categorical variable is also simple and graphical results are automatically displayed. This automatic graphing strategy is also applied to one- way tabulation and two-way tabulation. Description of the variables and the value or category labels are fully exploited with these descriptive graphs. Additional epidemiological functions added in by Epicalc include calculation of sample size, matched 1:n (n can vary) tabulation, kappa statistics, drawing of ROC curve from a table or from a logistic regression results, population pyramid plots from age and sex and follow-up plots. R has several advanced regression modelling functions such as multinomial logistic regression, ordinal logistic regression, survival analysis and multi-level modelling. By using Epicalc nice tables of odds ratios and 95% CI are produced, ready for simple transferal into a manuscript document with minimal further modification required. Although use of Epicalc implies a different way of working with R from conventional use, installation of Epicalc has no effect on any existing or new functions of R. Epicalc functions only increase efficiency of data analysis and makes R easier to use. ii This book is essentially about learning R with an emphasis on Epicalc. Readers should have some background in basic computer usage. With R, Epicalc and the supplied datasets, the users should be able to go through each lesson learning the concepts of data management, related statistical theories and the practice of data analysis and powerful graphing. The first four chapters introduce R concepts and simple handling of important basic elements such as scalars, vectors, matrices, arrays and data frames. Chapter 5 deals with simple data exploration. Date and time variables are defined and dealt with in Chapter 6 and fully exploited in a real dataset in Chapter 7. Descriptive statistics and one-way tabulations are automatically accompanied by corresponding graphs making it rather unlikely that important information is overlooked. Finally, time plots of exposure and disease onsets are plotted with a series of demonstrating commands. Chapter 8 continues to investigate the outbreak by two-way tabulation. Various kinds of risk assessment, such as the risk ratio and protective efficacy, are analysed with numeric and graphic results. Chapter 9 extends the analysis of the dataset to deal with levels of association or odds ratios. Stratified tabulation, the Mantel-Haenzsel odds ratio, and test of homogeneity of odds ratios are explained in detail. All results are complemented by simultaneous plots. With these graphs, the concept of confounding is made more understandable. Before proceeding further, the reader has a thorough exercise of data cleaning and standard data manipulation in Chapter 10. Simple looping commands are introduced to increase the efficiency of data management. Subsequently, and from time to time in the book, readers will learn how to develop these loops to create powerful graphs. Scatter plots, simple linear regression and analysis of variance are presented in Chapter 11. Stratified scatter plots to enhance the concept of confounding and interaction for continuous outcome variables are given in Chapter 12. Curvilinear models are discussed in Chapter 13. Linear modelling is extended to generalized linear modelling in Chapter 14. For binary outcome variables, Chapter 15 introduces logistic regression with additional comparison with stratified cross-tabulation learned in Chapter 9. The concept of a matched case control study is discussed in Chapter 16 with matched tabulation for 1:1 and 1:n matching. Finally, conditional logistic regression is applied. Chapter 17 introduces polytomous logistic regression using a case-control study in which one type of case series is compared with two types of control groups. Ordinal logistic regression is applied for ordered outcomes in Chapter 18. iii For a cohort study, with grouped exposure datasets, Poisson regression is used in Chapter 19. Extra-Poisson regression for overdispersion is also discussed. This includes modeling the outcome using the negative binomial error distribution. Multi-level modelling and longitudinal data analysis are discussed in Chapter 20. For cohort studies with individual follow-up times, survival analysis is discussed in Chapter 21 and the Cox proportional hazard model is introduced in Chapter 22. In chapter 23 the focus is on analyzing datasets involving attitudes, such as those encountered in the social sciences. Chapter 24 deals with day-to-day work in calculation of sample sizes and the technique of documentation that all professional data analysts must master is explained in Chapter 25. Some suggested strategies for handling large datasets are given in chapter 26. The book ends with a demonstration of the tableStack command, which dramatically shortens the preparation of a tidy stack of tables with a special technique of copy and paste into a manuscript. At the end of each chapter some references are given for further reading. Most chapters also end with some exercises to practice on. Solutions to these are given at the end of the book. Colour It is assumed that the readers of this book will simultaneously practice the commands and see the results on the screen. The explanations in the text sometimes describe the colour of graphs that appear in black and white in this book (the reason for this is purely for reducing the printing costs). The electronic copy of the book, however, does include colour.
Load more

Recommended publications
  • Summarize — Summary Statistics
    Title stata.com summarize — Summary statistics Description Quick start Menu Syntax Options Remarks and examples Stored results Methods and formulas References Also see Description summarize calculates and displays a variety of univariate summary statistics. If no varlist is specified, summary statistics are calculated for all the variables in the dataset. Quick start Basic summary statistics for continuous variable v1 summarize v1 Same as above, and include v2 and v3 summarize v1-v3 Same as above, and provide additional detail about the distribution summarize v1-v3, detail Summary statistics reported separately for each level of catvar by catvar: summarize v1 With frequency weight wvar summarize v1 [fweight=wvar] Menu Statistics > Summaries, tables, and tests > Summary and descriptive statistics > Summary statistics 1 2 summarize — Summary statistics Syntax summarize varlist if in weight , options options Description Main detail display additional statistics meanonly suppress the display; calculate only the mean; programmer’s option format use variable’s display format separator(#) draw separator line after every # variables; default is separator(5) display options control spacing, line width, and base and empty cells varlist may contain factor variables; see [U] 11.4.3 Factor variables. varlist may contain time-series operators; see [U] 11.4.4 Time-series varlists. by, collect, rolling, and statsby are allowed; see [U] 11.1.10 Prefix commands. aweights, fweights, and iweights are allowed. However, iweights may not be used with the detail option; see [U] 11.1.6 weight. Options Main £ £detail produces additional statistics, including skewness, kurtosis, the four smallest and four largest values, and various percentiles. meanonly, which is allowed only when detail is not specified, suppresses the display of results and calculation of the variance.
    [Show full text]
  • U3 Introduction to Summary Statistics
    Presentation Name Course Name Unit # – Lesson #.# – Lesson Name Statistics • The collection, evaluation, and interpretation of data Introduction to Summary Statistics • Statistical analysis of measurements can help verify the quality of a design or process Summary Statistics Mean Central Tendency Central Tendency • The mean is the sum of the values of a set • “Center” of a distribution of data divided by the number of values in – Mean, median, mode that data set. Variation • Spread of values around the center – Range, standard deviation, interquartile range x μ = i Distribution N • Summary of the frequency of values – Frequency tables, histograms, normal distribution Project Lead The Way, Inc. Copyright 2010 1 Presentation Name Course Name Unit # – Lesson #.# – Lesson Name Mean Central Tendency Mean Central Tendency x • Data Set μ = i 3 7 12 17 21 21 23 27 32 36 44 N • Sum of the values = 243 • Number of values = 11 μ = mean value x 243 x = individual data value Mean = μ = i = = 22.09 i N 11 xi = summation of all data values N = # of data values in the data set A Note about Rounding in Statistics Mean – Rounding • General Rule: Don’t round until the final • Data Set answer 3 7 12 17 21 21 23 27 32 36 44 – If you are writing intermediate results you may • Sum of the values = 243 round values, but keep unrounded number in memory • Number of values = 11 • Mean – round to one more decimal place xi 243 Mean = μ = = = 22.09 than the original data N 11 • Standard Deviation: Round to one more decimal place than the original data • Reported: Mean = 22.1 Project Lead The Way, Inc.
    [Show full text]
  • Descriptive Statistics
    Descriptive Statistics Fall 2001 Professor Paul Glasserman B6014: Managerial Statistics 403 Uris Hall Histograms 1. A histogram is a graphical display of data showing the frequency of occurrence of particular values or ranges of values. In a histogram, the horizontal axis is divided into bins, representing possible data values or ranges. The vertical axis represents the number (or proportion) of observations falling in each bin. A bar is drawn in each bin to indicate the number (or proportion) of observations corresponding to that bin. You have probably seen histograms used, e.g., to illustrate the distribution of scores on an exam. 2. All histograms are bar graphs, but not all bar graphs are histograms. For example, we might display average starting salaries by functional area in a bar graph, but such a figure would not be a histogram. Why not? Because the Y-axis values do not represent relative frequencies or proportions, and the X-axis values do not represent value ranges (in particular, the order of the bins is irrelevant). Measures of Central Tendency 1. Let X1,...,Xn be data points, such as the result of n measurements or observations. What one number best characterizes or summarizes these values? The answer depends on the context. Some familiar summary statistics are these: • The mean is given by the arithemetic average X =(X1 + ···+ Xn)/n.(No- tation: We will often write n Xi for X1 + ···+ Xn. i=1 n The symbol i=1 Xi is read “the sum from i equals 1 upto n of Xi.”) 1 • The median is larger than one half of the observations and smaller than the other half.
    [Show full text]
  • Measures of Dispersion for Multidimensional Data
    European Journal of Operational Research 251 (2016) 930–937 Contents lists available at ScienceDirect European Journal of Operational Research journal homepage: www.elsevier.com/locate/ejor Computational Intelligence and Information Management Measures of dispersion for multidimensional data Adam Kołacz a, Przemysław Grzegorzewski a,b,∗ a Faculty of Mathematics and Computer Science, Warsaw University of Technology, Koszykowa 75, Warsaw 00–662, Poland b Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw 01–447, Poland article info abstract Article history: We propose an axiomatic definition of a dispersion measure that could be applied for any finite sample of Received 22 February 2015 k-dimensional real observations. Next we introduce a taxonomy of the dispersion measures based on the Accepted 4 January 2016 possible behavior of these measures with respect to new upcoming observations. This way we get two Available online 11 January 2016 classes of unstable and absorptive dispersion measures. We examine their properties and illustrate them Keywords: by examples. We also consider a relationship between multidimensional dispersion measures and mul- Descriptive statistics tidistances. Moreover, we examine new interesting properties of some well-known dispersion measures Dispersion for one-dimensional data like the interquartile range and a sample variance. Interquartile range © 2016 Elsevier B.V. All rights reserved. Multidistance Spread 1. Introduction are intended for use. It is also worth mentioning that several terms are used in the literature as regards dispersion measures like mea- Various summary statistics are always applied wherever deci- sures of variability, scatter, spread or scale. Some authors reserve sions are based on sample data. The main goal of those characteris- the notion of the dispersion measure only to those cases when tics is to deliver a synthetic information on basic features of a data variability is considered relative to a given fixed point (like a sam- set under study.
    [Show full text]
  • Summary Statistics, Distributions of Sums and Means
    Summary statistics, distributions of sums and means Joe Felsenstein Department of Genome Sciences and Department of Biology Summary statistics, distributions of sums and means – p.1/18 Quantiles In both empirical distributions and in the underlying distribution, it may help us to know the points where a given fraction of the distribution lies below (or above) that point. In particular: The 2.5% point The 5% point The 25% point (the first quartile) The 50% point (the median) The 75% point (the third quartile) The 95% point (or upper 5% point) The 97.5% point (or upper 2.5% point) Note that if a distribution has a small fraction of very big values far out in one tail (such as the distributions of wealth of individuals or families), the may not be a good “typical” value; the median will do much better. (For a symmetric distribution the median is the mean). Summary statistics, distributions of sums and means – p.2/18 The mean The mean is the average of points. If the distribution is the theoretical one, it is called the expectation, it’s the theoretical mean we would be expected to get if we drew infinitely many points from that distribution. For a sample of points x1, x2,..., x100 the mean is simply their average ¯x = (x1 + x2 + x3 + ... + x100) / 100 For a distribution with possible values 0, 1, 2, 3,... where value k has occurred a fraction fk of the time, the mean weights each of these by the fraction of times it has occurred (then in effect divides by the sum of these fractions, which however is actually 1): ¯x = 0 f0 + 1 f1 + 2 f2 + ..
    [Show full text]
  • Numerical Summary Values for Quantitative Data 35
    3.1 Numerical summary values for quantitative data 35 Chapter 3 Descriptive Statistics II: Numerical Summary Values 3.1 Numerical summary values for quantitative data For many purposes a few well–chosen numerical summary values (statistics) will suffice as a description of the distribution of a quantitative variable. A statistic is a numerical characteristic of a sample. More formally, a statistic is a numerical quantity computed from the values of a variable, or variables, corresponding to the units in a sample. Thus a statistic serves to quantify some interesting aspect of the distribution of a variable in a sample. Summary statistics are particularly useful for comparing and contrasting the distribution of a variable for two different samples. If we plan to use a small number of summary statistics to characterize a distribution or to compare two distributions, then we first need to decide which aspects of the distribution are of primary interest. If the distributions of interest are essentially mound shaped with a single peak (unimodal), then there are three aspects of the distribution which are often of primary interest. The first aspect of the distribution is its location on the number line. Generally, when speaking of the location of a distribution we are referring to the location of the “center” of the distribution. The location of the center of a symmetric, mound shaped distribution is clearly the point of symmetry. There is some ambiguity in specifying the location of the center of an asymmetric, mound shaped distribution and we shall see that there are at least two standard ways to quantify location in this context.
    [Show full text]
  • Measures of Dispersion
    MEASURES OF DISPERSION Measures of Dispersion • While measures of central tendency indicate what value of a variable is (in one sense or other) “average” or “central” or “typical” in a set of data, measures of dispersion (or variability or spread) indicate (in one sense or other) the extent to which the observed values are “spread out” around that center — how “far apart” observed values typically are from each other and therefore from some average value (in particular, the mean). Thus: – if all cases have identical observed values (and thereby are also identical to [any] average value), dispersion is zero; – if most cases have observed values that are quite “close together” (and thereby are also quite “close” to the average value), dispersion is low (but greater than zero); and – if many cases have observed values that are quite “far away” from many others (or from the average value), dispersion is high. • A measure of dispersion provides a summary statistic that indicates the magnitude of such dispersion and, like a measure of central tendency, is a univariate statistic. Importance of the Magnitude Dispersion Around the Average • Dispersion around the mean test score. • Baltimore and Seattle have about the same mean daily temperature (about 65 degrees) but very different dispersions around that mean. • Dispersion (Inequality) around average household income. Hypothetical Ideological Dispersion Hypothetical Ideological Dispersion (cont.) Dispersion in Percent Democratic in CDs Measures of Dispersion • Because dispersion is concerned with how “close together” or “far apart” observed values are (i.e., with the magnitude of the intervals between them), measures of dispersion are defined only for interval (or ratio) variables, – or, in any case, variables we are willing to treat as interval (like IDEOLOGY in the preceding charts).
    [Show full text]
  • Section II Descriptive Statistics for Continuous & Binary Data (Including
    Section II Descriptive Statistics for continuous & binary Data (including survival data) II - Checklist of summary statistics Do you know what all of the following are and what they are for? One variable – continuous data (variables like age, weight, serum levels, IQ, days to relapse ) _ Means (Y) Medians = 50th percentile Mode = most frequently occurring value Quartile – Q1=25th percentile, Q2= 50th percentile, Q3=75th percentile) Percentile Range (max – min) IQR – Interquartile range = Q3 – Q1 SD – standard deviation (most useful when data are Gaussian) (note, for a rough approximation, SD 0.75 IQR, IQR 1.33 SD) Survival curve = life table CDF = Cumulative dist function = 1 – survival curve Hazard rate (death rate) One variable discrete data (diseased yes or no, gender, diagnosis category, alive/dead) Risk = proportion = P = (Odds/(1+Odds)) Odds = P/(1-P) Relation between two (or more) continuous variables (Y vs X) Correlation coefficient (r) Intercept = b0 = value of Y when X is zero Slope = regression coefficient = b, in units of Y/X Multiple regression coefficient (bi) from a regression equation: Y = b0 + b1X1 + b2X2 + … + bkXk + error Relation between two (or more) discrete variables Risk ratio = relative risk = RR and log risk ratio Odds ratio (OR) and log odds ratio Logistic regression coefficient (=log odds ratio) from a logistic regression equation: ln(P/1-P)) = b0 + b1X1 + b2X2 + … + bkXk Relation between continuous outcome and discrete predictor Analysis of variance = comparing means Evaluating medical tests – where the test is positive or negative for a disease In those with disease: Sensitivity = 1 – false negative proportion In those without disease: Specificity = 1- false positive proportion ROC curve – plot of sensitivity versus false positive (1-specificity) – used to find best cutoff value of a continuously valued measurement DESCRIPTIVE STATISTICS A few definitions: Nominal data come as unordered categories such as gender and color.
    [Show full text]
  • 4. Descriptive Statistics
    4. Descriptive statistics Any time that you get a new data set to look at one of the first tasks that you have to do is find ways of summarising the data in a compact, easily understood fashion. This is what descriptive statistics (as opposed to inferential statistics) is all about. In fact, to many people the term “statistics” is synonymous with descriptive statistics. It is this topic that we’ll consider in this chapter, but before going into any details, let’s take a moment to get a sense of why we need descriptive statistics. To do this, let’s open the aflsmall_margins file and see what variables are stored in the file. In fact, there is just one variable here, afl.margins. We’ll focus a bit on this variable in this chapter, so I’d better tell you what it is. Unlike most of the data sets in this book, this is actually real data, relating to the Australian Football League (AFL).1 The afl.margins variable contains the winning margin (number of points) for all 176 home and away games played during the 2010 season. This output doesn’t make it easy to get a sense of what the data are actually saying. Just “looking at the data” isn’t a terribly effective way of understanding data. In order to get some idea about what the data are actually saying we need to calculate some descriptive statistics (this chapter) and draw some nice pictures (Chapter 5). Since the descriptive statistics are the easier of the two topics I’ll start with those, but nevertheless I’ll show you a histogram of the afl.margins data since it should help you get a sense of what the data we’re trying to describe actually look like, see Figure 4.2.
    [Show full text]
  • Analysis of Variance with Summary Statistics in Microsoft Excel
    American Journal of Business Education – April 2010 Volume 3, Number 4 Analysis Of Variance With Summary Statistics In Microsoft Excel David A. Larson, University of South Alabama, USA Ko-Cheng Hsu, University of South Alabama, USA ABSTRACT Students regularly are asked to solve Single Factor Analysis of Variance problems given only the sample summary statistics (number of observations per category, category means, and corresponding category standard deviations). Most undergraduate students today use Excel for data analysis of this type. However, Excel, like all other statistical software packages, requires an input data set in order to invoke its Anova: Single Factor procedure. The purpose of this paper is therefore to provide the student with an Excel macro that, given just the sample summary statistics as input, generates an equivalent underlying data set. This data set can then be used as the required input data set in Excel for Single Factor Analysis of Variance. Keywords: Analysis of Variance, Summary Statistics, Excel Macro INTRODUCTION ost students have migrated to Excel for data analysis because of Excel‟s pervasive accessibility. This means, given just the summary statistics for Analysis of Variance, the Excel user is either limited to solving the problem by hand or to solving the problem using an Excel Add-in. Both of Mthese options have shortcomings. Solving by hand means there is no Excel counterpart solution that puts the entire answer „right there in front of the student‟. Using an Excel Add-in is often not an option because, typically, Excel Add-ins are not available. The purpose of this paper, therefore, is to explain how to accomplish analysis of variance using an equivalent input data set and also to provide the Excel user with a straight-forward macro that accomplishes this technique.
    [Show full text]
  • Descriptive Statistics
    Statistics: Descriptive Statistics When we are given a large data set, it is necessary to describe the data in some way. The raw data is just too large and meaningless on its own to make sense of. We will sue the following exam scores data throughout: x1, x2, x3, x4, x5, x6, x7, x8, x9, x10 45, 67, 87, 21, 43, 98, 28, 23, 28, 75 Summary Statistics We can use the raw data to calculate summary statistics so that we have some idea what the data looks like and how sprad out it is. Max, Min and Range The maximum value of the dataset and the minimum value of the dataset are very simple measures. The range of the data is difference between the maximum and minimum value. Range = Max Value − Min Value = 98 − 21 = 77 Mean, Median and Mode The mean, median and mode are measures of central tendency of the data (i.e. where is the center of the data). Mean (µ) The mean is sum of all values divided by how many values there are N 1 X 45 + 67 + 87 + 21 + 43 + 98 + 28 + 23 + 28 + 75 xi = = 51.5 N i=1 10 Median The median is the middle data point when the dataset is arranged in order from smallest to largest. If there are two middle values then we take the average of the two values. Using the data above check that the median is: 44 Mode The mode is the value in the dataset that appears most. Using the data above check that the mode is: 28 Standard Deviation The standard deviation (σ) measures how spread out the data is.
    [Show full text]
  • 1 - to Overcoming the Reluctance of Epidemiologists to Explicitly Tackle the Measurement Error Problem
    Statistics → Applications → Epidemiology Does adjustment for measurement error induce positive bias if there is no true association? Igor Burstyn, Ph.D. Community and Occupational Medicine Program, Department of Medicine, Faculty of Medicine and Dentistry, the University of Alberta, Edmonton, Alberta, Canada; Tel: 780-492-3240; Fax: 780-492-9677; E-mail: [email protected] Abstract This article is a response to an off-the-record discussion that I had at an international meeting of epidemiologists. It centered on a concern, perhaps widely spread, that measurement error adjustment methods can induce positive bias in results of epidemiological studies when there is no true association. I trace the possible history of this supposition and test it in a simulation study of both continuous and binary health outcomes under a classical multiplicative measurement error model. A Bayesian measurement adjustment method is used. The main conclusion is that adjustment for the presumed measurement error does not ‘induce’ positive associations, especially if the focus of the interpretation of the result is taken away from the point estimate. This is in line with properties of earlier measurement error adjustment methods introduced to epidemiologists in the 1990’s. An heuristic argument is provided to support the generalizability of this observation in the Bayesian framework. I find that when there is no true association, positive bias can only be induced by indefensible manipulation of the priors, such that they dominate the data. The misconception about bias induced by measurement error adjustment should be more clearly explained during the training of epidemiologists to ensure the appropriate (and wider) use of measurement error correction procedures.
    [Show full text]