Patterns of missing data and mechanisms of missing data rubin, 1976 are described along with details on historical and current methods of dealing with missing data. Expectationmaximization, imputation, missing data, sensitivity analysis. How to do statistical analysis when data are missing. Orchard and woodbury 1972 sooner or later usually sooner, anyone who does statistical analysis runs into problems with missing data allison, 2002. A simple approach for dealing with missing data is to throw out all the data for any sample missing one or more data elements.
An introduction to modern missing data analyses wiki for statistical. These methods include multiple imputation rubin, 1978 and the expectation maximisation. The treatment of missing data has been an issue in statistics for some time, but it has come to the fore in recent years. Mit critical data, secondary analysis of electronic health records. Handling missing data in r with mice stef van buuren.
Graham pennsylvania state university statistical procedures for missing data have vastly improved, yet miscon ception and unsound practice still abound. Data analysis process data collection and preparation collect data prepare codebook set up structure of data enter data screen data for errors exploration of data descriptive statistics graphs. Missing data are ubiquitous in ecological and evolutionary data sets as in any other branch of science. During the course of study, the outcomes of interest can be missing due to subjects nonresponse, missed visits, dropout, death and other reasons. Particularly if the missing data is limited to a small number of observations, you may just opt to eliminate those cases from the analysis. Missing data examples mplus some output omitted to save. Each category calls for a different approach to identification and handling. May 24, 20 missing data or missing values is defined as the data value that is not stored for a variable in the observation of interest. Minimize bias maximize use of available information get good estimates of uncertainty. The common methods used to deal with missing data are to delete cases containing missing data, and to use the mean to fill in missing values.
Missing value analysis helps address several concerns caused by incomplete data. Practical missing data analysis issues are discussed, most notably the inclusion of auxiliary variables for improving power and reducing bias. Hereinafter we will consider a dataset in which every row is a pattern or observation and every column is a feature or attribute and lets say we want to fix a given pattern which has a missing value in its j th feature position. Missing values are imputed, forming a complete data set. We use as a running example the social indicators survey, a telephone survey of new york city families. As an alternative to completecase analysis, researchers may fill in a plausible value for the missing observations, such as using the mean of the observed cases. Sensitivity analysis in multiple imputation for missing data yang yuan, sas institute inc. Missing data imputation missing data arise in almost all serious statistical analyses. As its name implies, regression imputation replaces missing values with predicted scores from a regression equation. But that work would be a lot less onerous if one had con. The question with the highest rate of missing data is income. Many researchers use ad hoc methods such as complete case analysis, available case analysis pairwise deletion, or singlevalue imputation. While there is a wealth of information contained in remotely sensed time series, the analysis of such time series is.
The patterns of missing data are displayed, tabulated, and found to be random. Graham, phd, is professor of biobehavioral health at the pennsylvania state university. In mplus, when measured exogenous variables but not indicators for exogenous latent variables have missing values, the cases with missing dataare excluded from the analysis. Such analysis is confounded by cloud cover and other imagespeci. Some of the models and topics for longitudinal data analysis that will be covered include the following. Statistical analysis with missing data, third edition wiley. I will briefly explain 5 commonly used missing data imputation techniques. The treatment of missing data university of vermont.
Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and bayesian perspectives. Principal component analysis with missing data and outliers. However, these traditional methods will result in biased estimation of parameters and uncertainty, and reduction in statistical power. Pdf statistical analysis with missing data download full. Work is required to become a practiced user of the acceptable i. In addition to specific aspects of trial design, many components of clinicaltrial conduct can limit the extent of missing data. Analysis and design and its web site work together to enable beginners to gain confidence in their ability to conduct missing data analysis, and more advanced readers to expand their skill set. If cases with missing values are systematically different from cases without missing values, the results can be misleading. That is, for a variable y, the probability that an observation is missing depends. One of the most common problems in the longitudinal data analysis is the missing data problem.
Pdf in this chapter, the reader will learn about common sources for missing data, how. The completecase analysis, in which individuals with any missing data are omitted, is also investigated theoretically and is found to be free of bias in many situations, though often wasteful of. Statistical methods for handling missing data jaekwang kim department of statistics, iowa state university july 5th, 2014. Explore missing data the missing data analysis begins by investigating the extent of missing data. Good missing data analysis solutions are available, but practical information. Addressing missing outcome data in metaanalysis evidence. Missingdata imputation missing data arise in almost all serious statistical analyses. Missing values are then replaced by imputed values and saved into a new data file for further analysis. Missing data analysis examine missing data by variable by respondent by analysis if no problem found, go directly to your analysis if a problem is found. Listwise listwise deletion completecase analysis removes all data for an observation that has one or more missing values.
Since all s containing missing data have been removed, there is. Missingdata imputation department of statistics columbia. All about missing data handling towards data science. Values in a data set are missing completely at random mcar if the events that lead to any particular dataitem being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random.
Chapter 2 introduces and discusses missing data analysis in some detail. Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking. But for other types of data one would look at the distribution of the data for normality, skew etc. It is also an excellent source of information for applied statisticians and practitioners in government and industry. Any general purpose method for handling missing data must assume that the missing data mechanism is ignorable.
Longitudinal data analysis with nonignorable missing data. Statistical analysis with missing data available for download and read online in other formats. For example, if a child does not attend an examination because the. An em analysis is used to estimate the means, correlations, and covariances. Missing data, inconsistent data and outliers problem data can be classified into three broad categories. Deal with missing data use what you know about why data is missing distribution of missing data decide on the best analysis strategy to yield the least biased estimates deletion methods listwise deletion, pairwise deletion single imputation methods meanmode substitution, dummy variable method, single regression. Abstract multiple imputation, a popular strategy for dealing with missing values, usually assumes that the data are missing at random mar.
Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. If the missing data mechanism is ignorable, there is no need to model it. In addition to missing values for analysis variables, sas excludes observations if the weight of any of the design va. There is often a logical hierarchy to identification and handling of bad data based on the category. Missing data analysis with the mahalanobis distance. Accordingly, some studies have focused on handling the missing data, problems. Options for missing data paul gorrell, impaq international, llc, columbia, md abstract a common situation researchers working with survey data face is the analysis of missing data, often due to nonresponse. A summary table figure 5 gives an overview of the responses for each question. Dealing with missing data real statistics using excel. It would that a missing data analysis is an essential prerequisit. The completecase analysis, in which individuals with any missing data are omitted, is also investigated theoretically and is found to be free of bias. Sensitivity analysis in multiple imputation for missing data. Sep 03, 2019 by far the most common approach to the missing data is to simply omit those cases with the missing data and analyse the remaining data.
Finally, in studies with missing data, analysis methods that are based on plausible scientific assumptions should be used. Complete case cc analysis this method deletes all cases with missing data and then performs statistical analyses on the remaining complete data set which has a smaller sample size. In this chapter we discuss avariety ofmethods to handle missing data, including some relativelysimple approaches that can often yield reasonable results. In a bivariate analysis with missing data on a single variable, the complete cases are used to estimate a regression equation where the incomplete variable serves as the outcome and the complete variable is the predictor. If cases with missing values are systematically different from cases without missing values, the. Missing data or missing values is defined as the data value that is not stored for a variable in the observation of interest. One problem with this approach is that the sample size will be reduced. Missing at random mar when we say data are missing at random, we mean that missing data on a partly missing variable y is related to some other completely observed variablesx in the analysis model but not to the values of y itself it is not specifically related to the missing information. This approach is called a completecase analysis, and we discuss some of its weaknesses below. Enders arizona state university, united states received 19 october 2009. Missing data strategies from completecase analysis to modelbased methods each carry assumptions about the nature of the mechanism that causes the missing data. An introduction to modern missing data analyses amanda n. Each of the m complete data sets is then analyzed using a statistical model e.
In bugs, missing outcomes in a regression can be handled. This is particularly relevant when the reduced sample size is too small to obtain significant results in the analysis. In the example below, there are four cases excluded because they were missing data on one or. The first edition of statistical analysis with missing data has been a standard reference on missingdata methods. There is a rich statistical literature on the analysis of missing data 1. However, in many clinical trials a complete case analysis or. Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Comparison of four methods for handing missing data in. Download pdf statistical analysis with missing data book full free.
Pdf the prevention and handling of the missing data. Delete the cases with missing data try to estimate the value of the missing data. When data are mcar, the analysis performed on the data is unbiased. Data are missing on some variables for some observations problem. The problem of missing data is relatively common in almost all research and can have a significant effect on the conclusions that can be drawn from the data. It is also used to determine that the data are missing completely at random.