Biomedical subjects

S R Lipsitz

Publications and source records attributed to S R Lipsitz.

At least 19 recordsLinked to original sources

PubMed2001

Bias in estimating association parameters for longitudinal binary responses with drop-outs.

This paper considers the impact of bias in the estimation of the association parameters for longitudinal binary responses when there are drop-outs. A number of different estimating equation approaches are considered for the case where drop-out cannot be assumed to be a completely random process. In particular, standard generalized estimating equations (GEE), GEE based on conditional residuals, GEE based on multivariate normal estimating equations for the covariance matrix, and second-order estimating equations (GEE2) are examined. These different GEE estimators are compared in terms of finite sample and asymptotic bias under a variety of drop-out processes. Finally, the relationship between bias in the estimation of the association parameters and bias in the estimation of the mean parameters is explored.

Algorithms↗

PubMed2000

Non-invasive assessment of rejection in pediatric transplant patients: serologic and echocardiographic prediction of biopsy-proven myocardial rejection.

BACKGROUND: Cardiac allograft rejection is a multifocal immune process that is currently assessed using biopsy-guided histologic classification systems (International Society for Heart and Lung Transplantation). Cardiac troponin T and I are established serologic markers of global myocyte damage. The use of load-independent measures of contractility have also been shown to accurately assess the presence of ventricular dysfunction. Little is known about their utility in accurately predicting rejection in the pediatric age group. We undertook the present study to compare rejection grade with echocardiographic and serologic estimates of transplant rejection-related myocardial damage. METHODS: We compared histologic rejection grades (0 to 4) with patient characteristics, echocardiographic measurements, catheterization measurements, and biochemical markers for 86 evaluations in 37 transplant recipients at Children's Hospital. RESULTS: In univariate analyses, biopsy scores correlated (p < 0.05) inversely with left ventricular systolic function (shortening fraction) and contractility (stress velocity index, SVI), and directly with mitral E-wave amplitude. In multivariate analyses, lower contractility and higher mitral E-wave amplitude remained significantly (p < or = 0.01) associated with rejection (SVI, p = 0.002, odds ratio = 0.393; E wave, p = 0.0002, odds ratio = 228). Most rejection episodes were associated with elevation of biochemical markers of myocardial injury. Although troponin I was weakly associated with differences between rejection grades (p = 0.034), troponin T, creatine kinase-MB fraction, and C-reactive protein did not differ with biopsy-rejection scores. Serum markers had a poor predictive capacity for biopsy-detected rejection. Troponin T and I did correlate with increased left ventricular wall thickness and mass. CONCLUSION: Progressively depressed left ventricular contractility and diastolic function are found with worsening pediatric heart transplant rejection-biopsy score; however, sensitive and specific serum markers do not correspond to the degree of active myocardial injury. The use of echocardiographic measures of contractility is associated with a specificity of 91.8% but low sensitivity of 66.7%. Overall we found poor concordance between serum markers and grade of rejection. It is unclear whether myocardial injury as assessed by serum markers, echocardiography, or histologic scoring is more important for assessment of acute rejection or long-term outcome, but it does not appear that serum and tissue markers of rejection can be used interchangeably.

Adolescent↗

PubMed2000

Modeling kappa for measuring dependent categorical agreement data.

A method for analysing dependent agreement data with categorical responses is proposed. A generalized estimating equation approach is developed with two sets of equations. The first set models the marginal distribution of categorical ratings, and the second set models the pairwise association of ratings with the kappa coefficient (kappa) as a metric. Covariates can be incorporated into both sets of equations. This approach is compared with a latent variable model that assumes an underlying multivariate normal distribution in which the intraclass correlation coefficient is used as a measure of association. Examples are from a cervical ectopy study and the National Heart, Lung, and Blood Institute Veteran Twin Study.

Journal Article↗

PubMed2000

Estimation with correlated censored survival data with missing covariates.

Incomplete covariate data are a common occurrence in studies in which the outcome is survival time. Further, studies in the health sciences often give rise to correlated, possibly censored, survival data. With no missing covariate data, if the marginal distributions of the correlated survival times follow a given parametric model, then the estimates using the maximum likelihood estimating equations, naively treating the correlated survival times as independent, give consistent estimates of the relative risk parameters Lipsitz et al. 1994 50, 842-846. Now, suppose that some observations within a cluster have some missing covariates. We show in this paper that if one naively treats observations within a cluster as independent, that one can still use the maximum likelihood estimating equations to obtain consistent estimates of the relative risk parameters. This method requires the estimation of the parameters of the distribution of the covariates. We present results from a clinical trial Lipsitz and Ibrahim (1996b) 2, 5-14 with five covariates, four of which have some missing values. In the trial, the clusters are the hospitals in which the patients were treated.

Journal Article↗

PubMed2000

GEE with Gaussian estimation of the correlations when data are incomplete.

This paper considers a modification of generalized estimating equations (GEE) for handling missing binary response data. The proposed method uses Gaussian estimation of the correlation parameters, i.e., the estimating function that yields an estimate of the correlation parameters is obtained from the multivariate normal likelihood. The proposed method yields consistent estimates of the regression parameters when data are missing completely at random (MCAR). However, when data are missing at random (MAR), consistency may not hold. In a simulation study with repeated binary outcomes that are missing at random, the magnitude of the potential bias that can arise is examined. The results of the simulation study indicate that, when the working correlation matrix is correctly specified, the bias is almost negligible for the modified GEE. In the simulation study, the proposed modification of GEE is also compared to the standard GEE, multiple imputation, and weighted estimating equations approaches. Finally, the proposed method is illustrated using data from a longitudinal clinical trial comparing two therapeutic treatments, zidovudine (AZT) and didanosine (ddI), in patients with HIV.

Anti-HIV Agents↗

PubMed1999

Using missing data methods in genetic studies with missing mutation status.

Because of current techniques of determining gene mutation, investigators are now interested in estimating the odds ratio between genetic status (mutation, no mutation) and an outcome variable such as disease cell type (A, B). In this paper we consider the mutation of the RAS genetic family. To determine if the genes have mutated, investigators look at five specific locations on the RAS gene. RAS mutated is a mutation in at least one of the five gene locations and RAS non-mutated is no mutation in any of the five locations. Owing to limited time and financial resources, one cannot obtain a complete genetic evaluation of all five locations on the gene for all patients. We propose the use of maximum likelihood (ML) with a 2(6) multinomial distribution formed by cross-classifying the binary mutation status at five locations by binary disease cell type. This ML method includes all patients regardless of completeness of data, treats the locations not evaluated as missing data, and uses the EM algorithm to estimate the odds ratio between genetic mutation status and the disease type. We compare the ML method to complete case estimates, and a method used by clinical investigators, which excludes patients with data on less than five locations who have no mutations on these sites.

Biometry↗

PubMed1999

Goodness-of-fit for GEE: an example with mental health service utilization.

Suppose we use generalized estimating equations to estimate a marginal regression model for repeated binary observations. There are no established summary statistics available for assessing the adequacy of the fitted model. In this paper we propose a goodness-of-fit test statistic which has an approximate chi-squared distribution when we have specified the model correctly. The proposed statistic can be viewed as an extension of the Hosmer and Lemeshow goodness-of-fit statistic for ordinary logistic regression to marginal regression models for repeated binary responses. We illustrate the methods using data from a study of mental health service utilization by children. The repeated responses are a set of binary measures of service use. We fit a marginal logistic regression model to the data using generalized estimating equations, and we apply the proposed goodness-of-fit statistic to assess the adequacy of the fitted model.

Age Factors↗

PubMed1999

GEECAT and GEEGOR: computer programs for the analysis of correlated categorical response data.

GEECAT and GEEGOR are two user-friendly SAS macros for the analysis of clustered, correlated categorical response data. Both programs implement methodology which extend the generalized estimating equation (GEE) approach of Liang and Zeger (Biometrika 73 (1986) 13-22). GEECAT and GEEGOR both use a first set of estimating equations to model the marginal response. With GEECAT, either correlated nominal or ordered categorical response data can be analyzed. The program GEEGOR employs a second set of estimating equations to model the association of ordered categorical responses within a cluster using the global odds ratio as a measure of association. The programs run on both mainframe computers and microcomputers. Examples are provided to illustrate the features of both programs.

Antirheumatic Agents↗

PubMed1999

Likelihood methods for incomplete longitudinal binary responses with incomplete categorical covariates.

We consider longitudinal studies in which the outcome observed over time is binary and the covariates of interest are categorical. With no missing responses or covariates, one specifies a multinomial model for the responses given the covariates and uses maximum likelihood to estimate the parameters. Unfortunately, incomplete data in the responses and covariates are a common occurrence in longitudinal studies. Here we assume the missing data are missing at random (Rubin, 1976, Biometrika 63, 581-592). Since all of the missing data (responses and covariates) are categorical, a useful technique for obtaining maximum likelihood parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). In using the EM algorithm with missing responses and covariates, one specifies the joint distribution of the responses and covariates. Here we consider the parameters of the covariate distribution as a nuisance. In data sets where the percentage of missing data is high, the estimates of the nuisance parameters can lead to highly unstable estimates of the parameters of interest. We propose a conditional model for the covariate distribution that has several modeling advantages for the EM algorithm and provides a reduction in the number of nuisance parameters, thus providing more stable estimates in finite samples.

Affect↗

PubMed1999

A global goodness-of-fit statistic for Cox regression models.

In this paper, a global goodness-of-fit test statistic for a Cox regression model, which has an approximate chi-squared distribution when the model has been correctly specified, is proposed. Our goodness-of-fit statistic is global and has power to detect if interactions or higher order powers of covariates in the model are needed. The proposed statistic is similar to the Hosmer and Lemeshow (1980, Communications in Statistics A10, 1043-1069) goodness-of-fit statistic for binary data as well as Schoenfeld's (1980, Biometrika 67, 145-153) statistic for the Cox model. The methods are illustrated using data from a Mayo Clinic trial in primary billiary cirrhosis of the liver (Fleming and Harrington, 1991, Counting Processes and Survival Analysis), in which the outcome is the time until liver transplantation or death. The are 17 possible covariates. Two Cox proportional hazards models are fit to the data, and the proposed goodness-of-fit statistic is applied to the fitted models.

Biometry↗

PubMed1999

Monte Carlo EM for missing covariates in parametric regression models.

We propose a method for estimating parameters for general parametric regression models with an arbitrary number of missing covariates. We allow any pattern of missing data and assume that the missing data mechanism is ignorable throughout. When the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). We extend this method to continuous or mixed categorical and continuous covariates, and for arbitrary parametric regression models, by adapting a Monte Carlo version of the EM algorithm as discussed by Wei and Tanner (1990, Journal of the American Statistical Association 85, 699-704). In addition, we discuss the Gibbs sampler for sampling from the conditional distribution of the missing covariates given the observed data and show that the appropriate complete conditionals are log-concave. The log-concavity property of the conditional distributions will facilitate a straightforward implementation of the Gibbs sampler via the adaptive rejection algorithm of Gilks and Wild (1992, Applied Statistics 41, 337-348). We assume the model for the response given the covariates is an arbitrary parametric regression model, such as a generalized linear model, a parametric survival model, or a nonlinear model. We model the marginal distribution of the covariates as a product of one-dimensional conditional distributions. This allows us a great deal of flexibility in modeling the distribution of the covariates and reduces the number of nuisance parameters that are introduced in the E-step. We present examples involving both simulated and real data.

Algorithms↗

PubMed1999

Selection models and pattern-mixture models for incomplete data with covariates.

Most models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern-mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study.

Biometry↗

PubMed1998

Using ecological data to estimate a regression model for individual data: the association between arsenic in drinking water and incidence of skin cancer.

In ecologic studies, participants are studied by groups, and the exposure status of each group is usually represented by a single indicator, mostly the mean exposure. In this paper, we propose using multiple variables derived from dummy variables at the individual level to describe the exposure. An analysis of the association between arsenic in drinking water and skin cancer was used as an example. Well water arsenic levels and skin cancer incidence from 1980 to 1987 were assessed for 243 townships in Taiwan. We first analyzed the data using the mean arsenic concentration in each township as the only exposure variable. The second analysis used multiple variables to describe arsenic exposure; each variable denoted the percentage of wells with arsenic levels within a specific range in each township. Although the first approach did not identify associations between arsenic levels and skin cancer, the multiple-variable approach identifies a positive association at the highest arsenic exposure category (>0.64 mg/L) in both men and women. Therefore, using multiple variables to describe an exposure in ecologic studies may facilitate a better description of the exposure status and thereby lead to more accurate risk assessment, especially when the dose-response relationship is not linear.

Arsenic↗

PubMed1998

Relationship between cumulative anthracycline dose and late cardiotoxicity in childhood acute lymphoblastic leukemia.

PURPOSE: Late anthrocycline cardiotoxicity after treatment for childhood cancer is common and often progressive. A safe anthracycline dose that will not result in late cardiac abnormalities has not been established due to the limited dose ranges used in existing studies. PATIENTS AND METHODS: To determine the relationship between cumulative anthracycline dose and late cardiotoxicity, we performed echocardiograms on 189 survivors of childhood acute lymphoblastic leukemia a median of 8.1 years (range, 2.0 to 23.4) after completion of anthracycline therapy. Patients were treated according to protocols that used widely varying cumulative anthracycline doses, but comparable nonanthracycline chemotherapy. Patients were divided into four groups based on the city of treatment and cumulative anthracycline dose: Copenhagen, 0 to 23 mg/m2 (n = 32); Boston, 45 mg/m2 (n = 17); Copenhagen, 73 to 301 mg/m2 (n = 53); and Boston, 244 to 550 mg/m2 (n = 87). Left ventricular dimension and fractional shortening were adjusted for sex and age or body-surface area through use of a control population (n = 296), and then compared among the four groups. RESULTS: Mean left ventricular dimension was significantly increased in the high-dose Boston group (observed:predicted value, 4.57 cm:4.45 cm; P = .002) and significantly higher than in the two Copenhagen groups. In the three lower-dose groups, there was no significant increase in mean left ventricular dimension, and the groups were not significantly different from each other. Similarly, the mean left ventricular fractional shortening was significantly depressed in the high-dose Boston group (observed:predicted value, 29.0%:33.8%; P = .0001) and significantly lower than in the three lower-dose groups. CONCLUSION: Depressed left ventricular fractional shortening and left ventricular dilatation were uncommon years after treatment of childhood leukemia when cumulative anthracycline doses were < or = 300 mg/m2.

Adolescent↗

PubMed1998

Tests for homogeneity of the risk difference when data are sparse.

Test statistics for the homogeneity of the risk difference for a series of 2 x 2 tables when the data are sparse is proposed. A weighted least squares statistic is commonly used to test for equality of the risk difference over the tables; however, when the data are sparse, this statistic can have anticonservative Type I error rates. Simulation is used to compare the proposed test statistics to the weighted least squares statistic. The weighted least squares statistic has the most anticonservative Type I error rates of all the statistics compared. We suggest the use of one of our proposed test statistics instead of the weighted least squares statistic.

Antineoplastic Agents↗

PubMed1998

Inference using conditional logistic regression with missing covariates.

When there are many nuisance parameters in a logistic regression model, a popular method for eliminating these nuisance parameters is conditional logistic regression. Unfortunately, another common problem in a logistic regression analysis is missing covariate data. With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates and then use conditional logistic regression, often called a complete-case analysis. In this article, we derive a modified conditional logistic regression that is appropriate with covariates that are missing at random. Performing a conditional logistic regression with only the complete cases is convenient with existing statistical packages, but it may give bias if missingness is not completely at random.

Bias↗

PubMed1998

Estimating equations with incomplete categorical covariates in the Cox model.

Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. When a full likelihood is specified, a useful technique for obtaining parameter estimates is the EM algorithm. We propose a set of estimating equations to estimate the parameters of Cox's proportional hazards model when some covariate values are missing. These estimating equations can be solved by an algorithm similar to the EM algorithm. Because of the computational burden of finding a solution to these estimating equations, we propose obtaining parameter estimates via Monte Carlo methods. Asymptotic variances of the parameter estimates are also derived. We present a clinical trials example with three covariates, two of which have some missing values.

Algorithms↗

PubMed1997

Predictive value of cardiac troponin T in pediatric patients at risk for myocardial injury.

BACKGROUND: Biochemical markers have not been routinely used in children at risk for myocardial damage. Yet, because of somatic growth and the duration of survival, a low level of myocardial damage may ultimately be of more consequence in children than in adults. METHODS AND RESULTS: We investigated the utility of cardiac troponin T (cTnT) blood levels (CARDIAC T ELISA Troponin T, Boehringer Mannheim Corp) in 51 consecutively sampled patients from 1 day to 34 years of age (median=5.7 years) undergoing cardiovascular (n=19) or noncardiovascular (n=17) surgery or who received doxorubicin for acute lymphoblastic leukemia (ALL) (n=15). Minimum detectable cTnT elevations were 0.03 ng/mL. cTnT was measurable in children of all ages with myocyte damage. In patients who underwent cardiovascular surgery, a correlation was noted between a score of increasing surgical severity and the mean level of postoperative cTnT (r=.79, P<.0001). Postoperative cTnT levels were elevated in children who completed cardiovascular surgery with an open chest compared with those with a closed chest (P=.0083). In addition, cTnT levels before cardiovascular surgery predicted postoperative survival (P=.007). cTnT elevations were observed after initial doxorubicin therapy for ALL. The magnitude of elevation predicted left ventricular dilatation (r=.80 when variables were treated as continuous, P=.003) and wall thinning (r=.61, P=.044) 9 months later. CONCLUSIONS: Elevations of blood cTnT in children relate to the severity of myocardial damage and predict subsequent subclinical and clinical cardiac morbidity and mortality.

Adolescent↗