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At least 199 records · Page 11Linked to original sources
PubMed1999

A mixed approach and a distribution-free multiple imputation technique for the estimation of a multivariate probit model with missing values.

In the present paper a mixed generalized estimating/pseudo-score equations (GEPSE) approach together with a distribution-free multiple imputation technique is proposed for the estimation of regression and correlation structure parameters of multivariate probit models with missing values for an ordered categorical time-invariant variable. Furthermore, a generalization of the squared trace correlation (RT2) for multivariate probit models, denoted by pseudo-RT2, is proposed. A simulation study was conducted, simulating a probit model with an equicorrelation structure in the errors of an underlying regression model and using two different missing mechanisms. For a low 'true' correlation the difference between the GEPSE, a generalized estimating equations (GEE) and a maximum likelihood (ML) estimator were negligible. For a high 'true' correlation the GEPSE estimator turned out to be more efficient than the GEE and very efficient relative to the ML estimator. Furthermore, the pseudo-RT2 was close to RT2 of the underlying linear model. The mixed approach is illustrated using a psychiatric data set of depressive in-patients. The results of this analysis suggest that the depression score at discharge from a psychiatric hospital and the occurrence of stressful life events seem to increase the probability of having an episode of major depression within a one-year interval after discharge. Furthermore, the correlation structure points to short-time effects on having or not having a depressive episode, not accounted for in the systematic part of the regression model.

Depressive Disorder↗
PubMed1976

The imputation of motives for marijuana behavior.

The motives imputed to marijuana use and nonuse by both users and nonusers were investigated by means of open-ended questions. Verbal responses were categorized to symbolize typal vocabularies of motive, and these categories were tabulated against behavioral, social, experiential, and attitudinal variables. The results indicated that frequency of marijuana use and favorableness in drug attitude are the key factors that influence the degree to which different vocabular explanations are offered. Current users most often say that personal experience and personal reward are the motives behind use of the drug, and that legal sanctions and psychomedical effects largely account for its nonuse. Nonusers with unfavorable attitudes tend to verbalize emotional relief and psychological dependence as the motivations for use, and emotional independence for nonuse. Some of the vocabularies of both of these groups are emphasized by former users and nonusers with favorable attitudes. Interpretations of the data in terms of sociological theories of motives are presented.

Attitude↗
PubMed1986

Is long-term care protected from imputed interest rules?

In October 1985, an amendment to the imputed interest rules was signed into law. This amendment exempted continuing care facilities, that qualified, from these rules. Unfortunately, the exemption was aimed at the traditional form of life care center that is no longer widely developed. Therefore, it appears this amendment may not provide the protection necessary to the continuing care industry.

Aged↗
PubMedDate not supplied

[Psychoanalytic studies and examination of imputability].

The Author points out that the recent contributions to the study of the crime require an improvement of the traditional principles followed for the investigation and qualification of the crime, as regards both its psychological dynamics and any juridical implications. The Author also shows that psychoanalysis is able to determine a decisive evolution in the eitology as well in the therapy of criminality. After a few preliminary considerations on the ambiguity of the idea of insanity, in accordance with the ordinary nosographic principles formulated in psychiatry, the Author emphasizes the uncertainties and discrepancies that, in the legislative systems from different countries, result from a limited view of the psychological phenomena. Then, he examines the utility of considering again the whole intra-psychic process which is involved in the crime dynamics through a psychoanalytical methodology. Particularly, he highlights the clarification which psycho-analysis may bring to the understanding of certain forms of aggressiveness which cannot be properly diagnosed according to the conventional medical and juridical methods. The Author finally considers the opening of the psycho-analytical application in the delicate examination of imputability. In this regard, he suggests to avoid any strict qualification, even in the evaluation of the most abnormal processes of psyche and he recommends--in conformity with a few juridical trends appeared in some countries--not to limit the investigation on the ability of understanding and will to the moment when a crime is committed, but to extend it to a single evaluation of the whole personality of the criminal.

Criminal Psychology↗
PubMed2005

Multiple imputation of the Glasgow Coma Score.

BACKGROUND: To investigate whether multiple imputation (MI) of missing Glasgow Coma Scale (GCS) values generates more accurate GCS frequency distributions and less biased parameter estimates in logistic regression models predicting mortality than the standard procedure of excluding observations with missing GCS values. METHODS: The study population consisted of 5,065 patients with complete GCS information from the trauma registry of a Level 1 trauma center. Missing GCS values were imposed on the data set, and the performance of MI (extrapolating missing GCS from a data prediction model) and of deleting all data observations with missing GCS (list-wise deletion) were evaluated. GCS and Trauma and Injury Severity Score (TRISS) frequency distributions and parameter estimates were compared with true values from the original data set. RESULTS: GCS and TRISS frequency values generated by MI were much more accurate than those generated by list-wise deletion. GCS and TRISS parameter estimates generated by MI all had acceptable bias and coverage rates when compared with true values. List-wise deletion provided biased parameter estimates for the GCS, the Revised Trauma Score, and the Injury Severity Score. CONCLUSION: MI is a valid solution to the problem of missing GCS data in trauma research. It allows the conservation of precious data observations and leads to unbiased estimates in consequent analyses. Analyses, which exclude observations with missing GCS data, provide biased results.

Biomedical Research↗
PubMed2006

National Survey of Family Growth, Cycle 6: sample design, weighting, imputation, and variance estimation.

OBJECTIVES: Cycle 6 of the National Survey of Family Growth (NSFG) was conducted by the National Center for Health Statistics in 2002 and early 2003. This report describes how the sample was designed, shows response rates for various subgroups of men and women, describes how the sample weights were computed to make national estimates possible, shows how missing data were imputed for a limited set of key variables, and describes the proper ways to estimate sampling errors from the NSFG. The report includes both nontechnical summaries for readers who need only general information and more technical detail for readers who need an in-depth understanding of these topics. METHODS: The NSFG Cycle 6 was based on an independent, national probability sample of men and women 15-44 years of age. It was the first NSFG ever to include a national sample of men 15-44 as well as a sample of women. Fieldwork was carried out by the University of Michigan's Institute for Social Research (ISR) under a contract with NCHS. In-person, face-to-face interviews were conducted by professional female interviewers using laptop computers. In all, 12,571 women and men-7,643 females and 4,928 males-were interviewed, the largest NSFG ever done. RESULTS: Analysis of NSFG Cycle 6 data requires the use of sampling weights and estimation of sampling errors that accounts for the complex sample design and estimation features of the survey. Examples of how to use several available software packages that incorporate complex design features in estimation, such as SAS, SUDAAN, and STATA, are presented.

Adolescent↗
PubMed1997

[Post-traumatic temporomandibular pain dysfunction syndrome and the problems raised by its imputation].

Direct, certain and total imputability of trauma as the cause of mandibular joint dysfunction is often difficult to establish. The theoretically obligatory conditions are rarely met, making the expert's opinion quite difficult to establish. The complexity of the pathophysiology involved in mandibular joint dysfunction confirms however the reality of this sequella. By determining the precise lesional or functional origin of the syndrome and the latent or patent nature of the prior status it is possible to ascertain intermediary situations.

Expert Testimony↗
PubMed1998

Multiple imputation for early stopping of a complex clinical trial.

It is desirable to have procedures available for stopping a clinical trial early if there appears to be no treatment effect. Conditional power procedures allow for early stopping in favor of the null hypothesis if the probability of rejecting H0 at the planned end of the trial given the current data and a value of the parameter of interest is below some threshold level. Lan, Simon, and Halperin (1982, Communications in Statistics C1, 207-219) proposed a stochastic curtailment procedure that calculates the conditional power under the alternative hypothesis. Alternatively, predictive power procedures incorporate information from the observed data by averaging the conditional power over the posterior distribution of the parameter. For complex problems in which explicit evaluation of conditional power is not possible, we propose treating the problem of projecting the outcome of a trial given the current data as a missing data problem. We then complete the data using multiple imputation and thus eliminate the need for explicit calculation of conditional power. We apply this method to AIDS Clinical Trials Group (ACTG) protocol 118 and to several simulated clinical trials.

Anti-HIV Agents↗
PubMed1996

Bayesian imputation of predictive values when covariate information is available and gold standard diagnosis is unavailable.

We suggest a conceptually simple Bayesian approach to inferences about the conditional probability of a specimen being infection-free given the outcome of a diagnostic test and covariate information. The approach assumes that the infection state of a specimen is not observable but uses the outcomes of a second test in conjunction with those of the first, that is, dual testing data. Dual testing procedures are often employed in clinical laboratories to assure that samples are not contaminated or to increase the likelihood of correct diagnoses. Using the CD4 count and a proxy for risk behavior as covariates, we apply the method to obtain inferences about the conditional probability of an individual being HIV-1 infection-free given the individual's covariates and a negative outcome with the standard enzyme-linked immunoad-sorbent assay/Western blotting test for HIV-1 detection. Inferences combine data from two studies where specimens were tested with the standard and with the more sensitive polymerase chain reaction test.

Algorithms↗
PubMed2004

Imputation for incomplete high-dimensional multivariate normal data using a common factor model.

It is common in applied research to have large numbers of variables measured on a modest number of cases. Even with low rates of missingness on individual variables, such data sets can have a large number of incomplete cases. Here we present a new method for handling missing continuously scaled items in multivariate data, based on extracting common factors to reduce the number of covariance parameters to be estimated in a multivariate normal model. The technique is compared in several simulation settings to available-case analysis and to a multivariate normal model with a ridge prior. The method is also illustrated on a study with over 100 variables evaluating an emergency room intervention for adolescents who attempted suicide.

Humans↗
PubMed2006

Four-fold table cell frequencies imputation in meta analysis.

Meta analysis is a collection of quantitative methods devoted to combine summary information from related but independent studies. Because research reports usually present only data reductions and summary statistics rather than detailed data, the reviewer must often resort to rather crude methods for constructing summary effect estimate suitable for meta analysis pooling methods. When the studies involve a binary variable, both number of events and sample sizes are required to compute pooled estimate and its confidence interval. Sometimes, only summary statistics and related confidence intervals are provided in the publication. Although it is possible to estimate the standard error of each study's effect measure using the confidence interval from each study, this lack of detailed data compels the reviewers to use the inverse variance method to perform meta analysis, or to exclude the works with incomplete data. This paper shows three methods to reconstruct four-fold tables when summary measures for binary data and related confidence intervals and sample sizes are provided. The methods are discussed through a wider application example to assess the reconstruction precision, and the impact of using reconstructed data on meta analysis results. These methods seem to yield a correct reconstruction if original measures are reported at least with two decimal places. Meta analysis results do not seem seriously affected by the use of reconstructed data. These methods allow the reviewer to use full meta analysis statistical tools, instead of the simple inverse variance method, and can greatly contribute to the completeness of systematic reviews.

Confidence Intervals↗
PubMed2002

Complete imputation of missing repeated categorical data: one-sample applications.

Longitudinal studies with repeated measures are often subject to non-response. Methods currently employed to alleviate the difficulties caused by missing data are typically unsatisfactory, especially when the cause of the missingness is related to the outcomes. We present an approach for incomplete categorical data in the repeated measures setting that allows missing data to depend on other observed outcomes for a study subject. The proposed methodology also allows a broader examination of study findings through interpretation of results in the framework of the set of all possible test statistics that might have been observed had no data been missing. The proposed approach consists of the following general steps. First, we generate all possible sets of missing values and form a set of possible complete data sets. We then weight each data set according to clearly defined assumptions and apply an appropriate statistical test procedure to each data set, combining the results to give an overall indication of significance. We make use of the EM algorithm and a Bayesian prior in this approach. While not restricted to the one-sample case, the proposed methodology is illustrated for one-sample data and compared to the common complete-case and available-case analysis methods.

Algorithms↗
PubMed2005

Modeling the economic burden of diseases imputable to stress at work.

This study evaluated the costs of work-related stress in France. Three illnesses--cardiovascular diseases, depression, musculoskeletal diseases and back pain--that may result from exposure to stress are identified and the proportions of cases attributable to the risk factor are calculated from epidemiological studies. Two methodological hypotheses allow us to provide complementary evaluations of the social cost of occupational stress and raise the ethical questions inherent in the choice of methodology. For the year 2000 our model shows that of a working population of 23.53 million in France some 310,000-393,400 persons (1.3-1.7%) were affected by illnesses attributable to work-related stress, and that 2,300-3,600 persons died as a result of their illness. Work-related stress costs society between Euro 1,167 million and Euro 1,975 million in France, or 14.4-24.2% of the total spending of social security occupational illnesses and work injuries branch.

Adult↗
PubMed1993

Regression imputation of missing values in longitudinal data sets.

A stand-alone, menu-driven PC program, written in GAUSS, which can be used to estimate missing observations in longitudinal data sets is described and male available to interested readers. The program is limited to the situation in which we have complete data on N cases at each of the planned times of measurement t1, t2,..., tT; and we wish to use this information, together with the non-missing values for n additional cases, to estimate the missing values for those cases. The augmented data matrix may be saved in an ASCII file and subsequently imported into programs requiring complete data. The use of the program is illustrated. Ten percent of the observations in a data set consisting of mandibular ramus height measurements for N = 12 young male rhesus monkeys measured at T = 5 time points are randomly discarded. The augmented data matrix is used to determine the lowest degree polynomial adequate to fit the average growth curve (AGC); the regression coefficients are estimated and confidence intervals for them are determined; and confidence bands for the AGC are constructed. The results are compared with those obtained when the original complete data set is used.

Animals↗