[Drug-induced acute hepatitis. Imputability and clinical aspects].
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Acute Disease↗
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[Chronic drug-induced hepatopathies. Imputability and clinical aspects].
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Chemical and Drug Induced Liver Injury↗
[Imputability criteria for drug-induced incidents].
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Drug-Related Side Effects and Adverse Reactions↗
[Hepatitis imputable to dimethylformamide with reintroduction].
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Adult↗
Attrition in longitudinal studies. How to deal with missing data.
The purpose of this paper was to illustrate the influence of missing data on the results of longitudinal statistical analyses [i.e., MANOVA for repeated measurements and Generalised Estimating Equations (GEE)] and to illustrate the influence of using different imputation methods to replace missing data. Besides a complete dataset, four incomplete datasets were considered: two datasets with 10% missing data and two datasets with 25% missing data. In both situations missingness was considered independent and dependent on observed data. Imputation methods were divided into cross-sectional methods (i.e., mean of series, hot deck, and cross-sectional regression) and longitudinal methods (i.e., last value carried forward, longitudinal interpolation, and longitudinal regression). Besides these, also the multiple imputation method was applied and discussed. The analyses were performed on a particular (observational) longitudinal dataset, with particular missing data patterns and imputation methods. The results of this illustration shows that when MANOVA for repeated measurements is used, imputation methods are highly recommendable (because MANOVA as implemented in the software used, uses listwise deletion of cases with a missing value). Applying GEE analysis, imputation methods were not necessary. When imputation methods were used, longitudinal imputation methods were often preferable above cross-sectional imputation methods, in a way that the point estimates and standard errors were closer to the estimates derived from the complete dataset. Furthermore, this study showed that the theoretically more valid multiple imputation method did not lead to different point estimates than the more simple (longitudinal) imputation methods. However, the estimated standard errors appeared to be theoretically more adequate, because they reflect the uncertainty in estimation caused by missing values.
Analysis of Variance↗
Tools for statistical analysis with missing data: application to a large medical database.
Missing data is a common feature of large data sets in general and medical data sets in particular. Depending on the goal of statistical analysis, various techniques can be used to tackle this problem. Imputation methods consist in substituting the missing values with plausible or predicted values so that the completed data can then be analysed with any chosen data mining procedure. In this work, we study imputation in the context of multivariate data and we evaluate a number of methods which can be used by today's standard statistical software packages. Imputation using multivariate classification, multiple imputation and imputation by factorial analysis are compared using simulated data and a large medical database (from the diabetes field) with numerous missing values. Our main result is to provide a control chart for assessing data quality after the imputation process. To this end, we developed an algorithm for which the input is a set of parameters describing the underlying data (e.g., covariance matrix, distribution) and the output is a chart which plots the change in the prediction error with respect to the proportion of missing values. The chart is built by means of an iterative algorithm involving four steps: (1) a sample of simulated data is drawn by using the input parameters; (2) missing values are randomly generated; (3) an imputation method is used to fill in the missing data and (4) the prediction error is computed. Steps 1 to 4 are repeated in order to estimate the distribution of the prediction error. The control chart was established for the 3 imputation methods studied here, assuming a multivariate normal distribution of data. The use of this tool on a large medical database was then investigated. We show how the control chart can be used to assess the quality of the imputation process in the pre-processing step upstream of data mining procedures.
Algorithms↗
GCRP: Integrated Global Chicken Reference Panel from 11,951 Chicken Genomes.
Chickens are a crucial source of protein for humans and a popular model animal for bird research. Despite the emergence of imputation as a reliable genotyping strategy for large populations, the lack of a high-quality chicken reference panel has hindered progress in chicken genome research. To address this, here we introduce the first phase of the 100K Global Chicken Reference Panel (100K GCRP). Currently, two panels are available: a comprehensive mix panel (CMP) for domestication diversity research and a commercial breed panel (CBP) for breeding broilers specifically. Evaluation of genotype imputation quality showed that CMP had the highest imputation accuracy compared to imputation using existing chicken panels in Animal-SNPAtlas and Animal Genotype Imputation Database (AGIDB), whereas CBP performed stably in the imputation of commercial populations. Additionally, we found that genome-wide association studies using GCRP-imputed data, whether on simulated or real phenotypes, exhibited greater statistical power. In conclusion, our study indicates that the GCRP effectively fills the gap in high-quality reference panels for chickens, providing an effective imputation platform for future genetic and breeding research. The project includes 11,951 samples and provides services for various applications on its website at http://farmrefpanel.com/GCRP/#/.
Animals↗
[Methods for handling incomplete data in health research: a critical look].
OBJECTIVE: To illustrate methods for handling incomplete data in health research. METHODS: Two strategies for handling missing data are presented: complete-case analysis and imputations. The imputations used were mean imputations, regression imputations, and multiple imputations. These strategies are illustrated in the context of logistic regression through an example using data from the "Second Cuban national survey on risk factors and non communicable disease", carried out in 2001. RESULTS: The results obtained via mean and regression imputation were similar. The odds ratios were overestimated by 10%. The results of complete-case analysis showed the greatest difference from the reference odds ratios, with a variation of between 2 and 65%. The three methods distorted the relationship between age and hypertension. Multiple imputations produced estimates closest to those of the reference estimates with a variation of less than 16%. This was the only procedure preserving the relationship between age and hypertension. CONCLUSIONS: Selecting methods for handling missing data is difficult, since the same procedure can give precise estimations in certain circumstances and not in others. Complete-case analysis should be used with caution due to the substantial loss of information it produces. Mean and regression imputations produce unreliable estimates under missing at random (MAR) mechanisms.
Cuba↗
Methods for estimating the AIDS incubation time distribution when date of seroconversion is censored.
In most cohort studies on HIV infection and AIDS, data on time from seroconversion to AIDS or death are doubly censored, both at the time origin and at the endpoint of interest. In epidemiological research, the most frequently adopted approach is to restrict the analysis to persons with narrow seroconversion intervals and to impute the midpoint of this interval as date of seroconversion. For many cohort studies, the consequence is that a substantial proportion of the data is not used. We consider four methods that are expected to be less biased when all cohort data are used: two imputation methods, conditional mean and multiple imputation, and two likelihood maximization methods. We derive the likelihood structure of the cohort data and clarify its dependence on study design. All methods are applied to data from the Amsterdam cohort study among injection drug users. In a simulation study the data generation process of this cohort study is imitated. The performance of midpoint, conditional mean and multiple imputation are compared. With midpoint imputation, both an analysis using the full data set, as well as one restricted to the cases with small seroconversion intervals, is performed. Conditional mean imputation comes out as the preferred method. It gives best results with respect to mean squared error. Moreover, when confidence intervals are computed through standard methods that ignore the uncertainty in the imputed date of seroconversion, coverage probabilities are almost correct.
Acquired Immunodeficiency Syndrome↗
A comparison of anlaytic methods for non-random missingness of outcome data.
Missing outcome values occur frequently in survey data and are rarely missing randomly. Depending on the pattern of missingness, the choice of analytic method has implications for accuracy of the estimated outcome distribution as well as multivariate models. Data from a study of patterns of care in disabled elders were used to evaluate several common methods when missingness of the outcome was nonrandom. Results from single and multiple model-based imputation were compared with results from complete-case analysis and mean imputation. By ignoring nonrespondents' covariate information, the latter two methods yielded biased estimates of population means. Mean imputation and single model-based imputation underestimated standard errors by treating imputed values as if they were observed. Mean imputation also distorted the relationship between the outcome and predictors. Multiple model-based imputation provided an easily implemented method of adjustment for non-random non-response in both univariate and multivariate analyses.
Activities of Daily Living↗
Residential exposure to magnetic fields: an empirical examination of alternative measurement strategies.
OBJECTIVES: To investigate the impact of measuring a single home then imputing information from another home among subjects who lived in two homes in a subset of the National Cancer Institute/Children's Cancer Group (NCI/CCG) investigation of residential exposure to magnetic fields and risk of childhood leukaemia. METHODS: Each subject's summary time weighted average (TWA) exposure was derived from measurements of two homes, weighted by the fraction of the reference period lived in the residence. The three cost efficient field work strategies examined were measuring: (a) the longer lived in home; (b) the currently lived in home; and (c) the former lived in home. Two different methods were used for imputing the missing values: (a) control mean imputation, (b) status specific mean imputation. The subject's summary exposure to magnetic fields estimated with each approach was compared with the subject's TWA calculated from measurements in both homes. The association between estimated exposure to magnetic fields and the risk of leukaemia under different approaches was examined with unconditional logistic regression analysis. RESULTS: The Pearson correlation coefficient between the two measurements within subjects was 0.31 (p < 10(-4), indicating a lack of independence of measurements. Differences were found between mean exposures in current and former homes of cases, and between longer and shorter lived in homes of controls. All methods with measurements from one of the homes in conjunction with imputation of measurements for the second home led to marked attenuation of risk estimates at the highest exposure category, particularly when measurements from current homes were used and those from former homes were imputed. CONCLUSION: Results argue against attempting to estimate lifetime magnetic field exposure from imputed values derived from current residences to fill in gaps caused by unmeasured residences previously lived in.
Bias↗
[Determination of the beginning of follow-up in longitudinal studies applied to HIV infections].
BACKGROUND: In cohort or longitudinal studies, subjects are recruited some time after the beginning of the problem, as in HIV infection. The aim of this paper is to show several imputation techniques of the beginning of follow up and evaluate its use in the framework of a study of HIV progression. METHODS: Three subcohorts of HIV+ subjects recruited in Valencia and Castellón CIPS up to 1996 are available. Seroconversion date was estimated for 244 Seroincidents, 887 seroprevalents with CD4 measurements and 337 without CD4 measurements. For seroincidents midpoint between last HIV- and first HIV+ visits was considered. For prevalent with CD4 serocon version date was imputed from 5 random samples of a progression model of infection to a CD4 level. For prevalent without CD4 seroconversion date was imputed from 5 random samples of HIV incidence density obtained from the other subcohorts. The imputation was repeated 500 times, assigning the seroconversion date as the median of imputations and obtaining confidence limits from 5 and 95 percentiles. Imputation validity was tested comparing time to AIDS and death for each one of the 3 groups. RESULTS: 443 and 405 deaths were observed among the 1468 subjects. Median of seroconversion was January 1993 for incidents, January 1991 for prevalents with CD4 and November 1988 for prevalents without CD4. The latest group showed a worse survival and AIDS free time compared to the other two cohorts. CONCLUSIONS: The imputation tools used showed their usefulness to reduce the survival bias in observational studies. Their generalization depends on the viability of incident cohorts, the availability of a progression marker or a origin on time.
Adult↗
Intent-to-treat analysis for longitudinal studies with drop-outs.
We consider intent-to-treat (IT) analysis of clinical trials involving longitudinal data subject to drop-out. Common methods, such as Last Observation Carried Forward imputation or incomplete-data methods based on models that assume random dropout, have serious drawbacks in the IT setting. We propose a method that involves multiple imputation of the missing values following drop-out based on an "as treated" model, using actual dose after drop-out if this is known, or imputed doses that incorporate a variety of plausible alternative assumptions if unknown. The multiply-imputed data sets are then analyzed using IT methods, were subjects are classified by randomization group rather than by the dose actually received. Results from the multiply-imputed data sets are combined using the methods of Rubin (1987, Multiple Imputation for Nonresponse in Surveys). A novel feature of the proposed method is that the models for imputation differ from the model used for the analysis of the filled-in data. The method is applied to data on a clinical trial for Tacrine in the treatment of Alzheimer's disease.
Alzheimer Disease↗
Estimation of time since exposure for a prevalent cohort.
In natural history studies of human immunodeficiency virus type 1 (HIV-1) infection a substantial proportion of participants are seropositive at time of enrollment in the study. These participants form a prevalent subcohort. Estimation of the unknown times since exposure to HIV-1 in the prevalent subcohort is of primary importance for estimation of the incubation time of AIDS. The subset of the cohort that tested negative for antibody to HIV-1 at study entry and was observed to seroconvert forms the incident subcohort that provides longitudinal data on markers of maturity (that is, duration) of infection. We use parametric life table regression models incorporating truncation to describe the conditional distribution (imputing model) of the times since seroconversion given a vector of the markers of maturity. Using the fitted model and the values of the markers of maturity of infection provided by the seroprevalent subcohort at entry into the study, we can impute the unknown times since seroconversion for the prevalent subcohort. We implement multiple imputation based on a model-robust estimate of the covariance matrix of parameters of the imputing model to provide confidence intervals for the geometric mean of the time since seroconversion in the prevalent subcohort, and to compare maturity of infection of cohorts recruited in different cities. The accuracy of imputation is further validated by comparisons of imputation-based estimates of AIDS incubation distribution in the seroprevalent subcohort with more direct estimates obtained from the seroincident subcohort.
Acquired Immunodeficiency Syndrome↗
A systematic review identifies a lack of standardization in methods for handling missing variance data.
BACKGROUND AND OBJECTIVES: To describe and critically appraise available methods for handling missing variance data in meta-analysis (MA). METHODS: Systematic review. MEDLINE, EMBASE, Web of Science, MathSciNet, Current Index to Statistics, BMJ SearchAll, The Cochrane Library and Cochrance Colloquium proceedings, MA texts and references were searched. Any form of text was included: MA, method chapter, or otherwise. Descriptions of how to implement each method, the theoretic basis and/or ad hoc motivation(s), and the input and output variable(s) were extracted and assessed. Methods may be: true imputations, methods that obviate the need for a standard deviation (SD), or methods that recalculate the SD. RESULTS: Eight classes of methods were identified: algebraic recalculations, approximate algebraic recalculations, imputed study-level SDs, imputed study-level SDs from nonparametric summaries, imputed study-level correlations (e.g., for change-from-baseline SD), imputed MA-level effect sizes, MA-level tests, and no-impute methods. CONCLUSION: This work aggregates the ideas of many investigators. The abundance of methods suggests a lack of consistency within the systematic review community. Appropriate use of methods is sometimes suspect; consulting a statistician, early in the review process, is recommended. Further work is required to optimize method choice to alleviate any potential for bias and improve accuracy. Improved reporting is also encouraged.
Data Interpretation, Statistical↗
Intent-to-treat analysis for longitudinal clinical trials: coping with the challenge of missing values.
Drop-out is a common phenomenon in clinical trials of drug treatments involving longitudinal assessments for a fixed duration of follow-up. For these trials intent-to-treat (IT) analysis is usually preferred because time effects are seen in practice. The IT analysis mandates that all subjects randomized to a treatment arm should be included in the analysis. The purpose of the present paper is to acquaint both clinicians and statisticians with recent statistical methodological advances in handling drop-outs and their usage for IT analysis. We discuss a sensitivity analysis of 12-month outcome data to investigate the efficacy of drug therapy from a longitudinal double-blind placebo-controlled clinical trial in the maintenance therapy of geriatric major depressive illness. Outcome measures consist of monthly Hamilton depression scores. The sensitivity analysis includes endpoint analysis, last observation carried forward analysis, repeated measures models and imputation models. Imputation models are based on multiple imputations of missing responses deriving from an 'as-treated' model. The model used imputed doses from a plausible treatment scenario after drop-out and a 'propensity-adjusted' model where the imputations for the drop-outs were obtained from the adhering subjects with the same probability to remain on study (propensity) given the observed trajectory prior to withdrawal. Issues related to bias and efficiency of the estimates obtained by different analyses are discussed. We recommend a more widespread use of imputation models for the IT analysis.
Aged↗
Dealing with gene expression missing data.
Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA). The influence in prediction accuracy of some factors, such as methods' parameters, type of data relationships used in the estimation process (i.e. row-wise, column-wise or both), missing rate and pattern and type of experiment [time series (TS), non-time series (NTS) or mixed (MIX) experiments] is elucidated. Improvements based on the iterative use of data (iterative LLS and PLS imputation--ILLSimpute and IPLSimpute), the need to perform initial imputations (modified PLS and Helland PLS imputation--MPLSimpute and HPLSimpute) and the type of relationships employed (KNNarray, LLSarray, HPLSarray and alternating PLS--APLSimpute) are proposed. Overall, it is shown that data set properties (type of experiment, missing rate and pattern) affect the data similarity structure, therefore influencing the methods' performance. LLSimpute and ILLSimpute are preferable in the presence of data with a stronger similarity structure (TS and MIX experiments), whereas PLS-based methods (MPLSimpute, IPLSimpute and APLSimpute) are preferable when estimating NTS missing data.
Algorithms↗
The effect of gender and race on the measurement properties of the CES-D in older adults.
Having observed a three-fold difference in the prevalence of significant symptoms of depression among four race-gender groups of elderly adults attending an urban primary care practice, we investigated the extent to which these differences might be explained by variability in the measurement properties of the Centers for Epidemiologic Studies depression scale (CES-D). Although the internal consistency of the CES-D was acceptable for all groups, 5% of our patients were excluded for inability to complete the minimum required number of CES-D items, and nearly 40% of patients required response imputation for the allowable one to four items that they could not answer. Imputation was most frequently required for items tapping positive affect. Principal components factor analysis was performed separately for respondents answering all items and for respondents with imputed values. In both analyses we found important race-gender differences in factor structure. Moreover, the factor structure for those with imputed values was markedly different from that of respondents answering all items, including a dissolution of the positive affect dimension. Neither the race-gender differences in factor structure nor the differences among those with and without imputed data were resolved by eliminating respondents with poor education, cognitive impairment, or alcoholism, or by varying the assumptions for data imputation. However, the disparities in factor structure were essentially resolved by eliminating five CES-D items, suggesting the need to modify the CES-D in populations like ours. Although eliminating these five items results in a more pure factor structure, it does not resolve the differences in prevalence of depressive symptoms. These differences may, however, be partially due to differential response tendencies among the race-gender groups.
Black or African American↗