Search PubMed⌕ Search

Biomedical subjects

N M Laird

Publications and source records attributed to N M Laird.

At least 55 records · Page 3Linked to original sources
PubMed1996

Intention-to-treat analyses for incomplete repeated measures data.

In a randomized longitudinal clinical trial designed to evaluate two or more rival treatments, an intent-to-treat analysis requires inclusion of all randomized patients, regardless of whether they remain on protocol for the duration of the study. We propose a piecewise linear random effects model for analyzing longitudinal data where the multivariate outcome can depend upon time spent on treatment. The model assumes that data are available on a random sample of subjects after treatment is terminated, and allows either a pragmatic or explanatory analysis (as defined by Schwartz and Lellouch, 1967, Journal of Chronic Diseases 20, 637-648). Full maximum likelihood estimation of the model parameters is carried out using widely available statistical software for repeated measures with missing data and for nonparametric survival curve estimation. Data from a national, multicenter pediatric AIDS clinical trial are analyzed to illustrate implementation and interpretation of the model.

Acquired Immunodeficiency Syndrome↗
PubMed1995

Bivariate logistic regression analysis of childhood psychopathology ratings using multiple informants.

A central issue in studies of risk factors for childhood psychopathology is utilization of the information obtained about the child's mental health status from multiple informants. In this paper, the authors propose a new approach to the analysis of risk factor data when the outcomes are binary ratings (presence/absence of symptoms). This new approach has several attractive features in this setting. The strategy taken is to perform a single analysis using multivariate modeling, in which simultaneous logistic regressions are conducted for the outcomes given by each of several informants. The advantages of this approach include the following: 1) it retains the complete information about case status for each informant; 2) it permits assessment of informant-risk factor interactions as well as "overall" risk factor effects; 3) it provides measures of association between the multiple informants and adjusts for the association between responses in the analysis; and 4) missing data on a subset of respondents can be incorporated in a straightforward way, permitting all subjects with at least one informant to be used in the analysis. To illustrate the methods, the authors present findings on risk factors for measures of "Internalizing" and "Externalizing" behaviors from two surveys using parent and teacher ratings of 6- to 11-year-old children in Connecticut between 1986 and 1989.

Child↗
PubMed1995

A simulation study of estimators for rates of change in longitudinal studies with attrition.

Many longitudinal studies and clinical trials are designed to compare rates of change over time in one or more outcome variables in several groups. Most such studies have incomplete data because some patients drop out before completing the study. The missing data may induce bias and inefficiency in naive estimates of important parameters. This paper uses Monte Carlo methods to compare the bias and efficiency of several two-stage estimators of the effect of treatment on the mean rate of change when the missing data arise from one of four processes. We also study the validity of confidence intervals and the power of hypothesis tests based on these estimates and their standard errors. In general, the weighted least squares estimator does relatively well, as does an analysis of covariance type estimator proposed by Wu et al. The best estimates of variance components are based on complete cases or maximum likelihood.

Analysis of Variance↗
PubMed1994

Analysing incomplete longitudinal binary responses: a likelihood-based approach.

In this paper, we describe a likelihood-based method for analysing balanced but incomplete longitudinal binary responses that are assumed to be missing at random. Following the approach outlined in Zhao and Prentice (1990, Biometrika 77, 642-648), we focus on "marginal models" in which the marginal expectation of the response variable is related to a set of covariates. The association between binary responses is modelled in terms of conditional log odds-ratios. We describe a set of scoring equations for jointly estimating both the marginal parameters and the conditional association parameters. An outline of the EM algorithm used to obtain the maximum likelihood estimates is presented. This approach yields valid and efficient estimates when the responses are missing at random, but not necessarily missing completely at random. An example, using data from the Muscatine Coronary Risk Factor Study, is presented to illustrate this methodology.

Age Factors↗
PubMed1994

Weighted least squares analysis of repeated categorical measurements with outcomes subject to nonresponse.

In this paper, we describe a two-step weighted least squares method for analyzing repeated categorical outcomes when some individuals are not observed at all times of follow-up. Other weighted least squares methods for analyzing repeated measures data with missing responses have previously been proposed by Koch, Imrey, and Reinfurt (1972, Biometrics 28, 663-692) and Woolson and Clarke (1984, Journal of the Royal Statistical Society, Series A 147, 87-99). These methods give consistent estimators if the responses are missing completely at random, as discussed in Rubin (1976, Biometrika 63, 581-592). We propose a two-step method that will give consistent results under the weaker condition of missing at random, and compare it with the other two methods.

Adolescent↗
PubMed1994

Performance of generalized estimating equations in practical situations.

Moment methods for analyzing repeated binary responses have been proposed by Liang and Zeger (1986, Biometrika 73, 13-22), and extended by Prentice (1988, Biometrics 44, 1033-1048). In their generalized estimating equations (GEE), both Liang and Zeger (1986) and Prentice (1988) estimate the parameters associated with the expected value of an individual's vector of binary responses as well as the correlations between pairs of binary responses. In this paper, we discuss one-step estimators, i.e., estimators obtained from one step of the generalized estimating equations, and compare their performance to that of the fully iterated estimators in small samples. In simulations, we find the performance of the one-step estimator to be qualitatively similar to that of the fully iterated estimator. When the sample size is small and the association between binary responses is high, we recommend using the one-step estimator to circumvent convergence problems associated with the fully iterated GEE algorithm. Furthermore, we find the GEE methods to be more efficient than ordinary logistic regression with variance correction for estimating the effect of a time-varying covariate.

Air Pollution↗
PubMed1992

Longitudinal studies with continuous responses.

The analysis of serial measurements obtained in longitudinal studies plays an increasingly prominent role in applied research. The last few years have seen the development of many new techniques for carrying out analyses, including computer software. These methods can be used in a variety of standard problems, including repeated measures and cross-over designs, as well as growth curve analyses. We review these new methods, their application, and available computer packages. Data from a longitudinal study of lung function is used to illustrate the methods.

Adolescent↗
PubMed1992

Physicians' perceptions of the risk of being sued.

We explore the deterrent effect of the tort system by assessing physician perceptions of the risk of being sued and the impact of those perceptions on their own practice. The data are from a mailed survey conducted in 1989 of a random sample of physicians who were practicing in New York State in 1984. The survey results were compared to the actual risk of suit using the between-group (Wald) test and logistic regression methods. We also surveyed physicians about practice changes undertaken in the last ten years, factors influencing practice standards, and the costs of being sued and included these in the analysis. On average, physicians estimate that 19.5 out of one hundred of their colleagues will be sued in a given year, approximately three times the actual rate, with significant differences by specialty, location, and suit history. Perceived risk is associated with self-reported changes in test-ordering frequency and reduction in practice scope. The median number of days lost from practice to defend a malpractice suit was three to five, and 6 percent of the physicians surveyed incurred some out-of-pocket expenses. These findings suggest that physicians respond to the messages sent by litigation in a manner consistent with the deterrent theory of tort litigation.

Age Factors↗
PubMed1991

Relation between malpractice claims and adverse events due to negligence. Results of the Harvard Medical Practice Study III.

BACKGROUND AND METHODS: By matching the medical records of a random sample of 31,429 patients hospitalized in New York State in 1984 with statewide data on medical-malpractice claims, we identified patients who had filed claims against physicians and hospitals. These results were then compared with our findings, based on a review of the same medical records, regarding the incidence of injuries to patients caused by medical management (adverse events). RESULTS: We identified 47 malpractice claims among 30,195 patients' records located on our initial visits to the hospitals, and 4 claims among 580 additional records located during follow-up visits. The overall rate of claims per discharge (weighted) was 0.13 percent (95 percent confidence interval, 0.076 to 0.18 percent). Of the 280 patients who had adverse events caused by medical negligence as defined by the study protocol, 8 filed malpractice claims (weighted rate, 1.53 percent; 95 percent confidence interval, 0 to 3.2 percent). By contrast, our estimate of the statewide ratio of adverse events caused by negligence (27,179) to malpractice claims (3570) is 7.6 to 1. This relative frequency overstates the chances that a negligent adverse event will produce a claim, however, because most of the events for which claims were made in the sample did not meet our definition of adverse events due to negligence. CONCLUSIONS: Medical-malpractice litigation infrequently compensates patients injured by medical negligence and rarely identifies, and holds providers accountable for, substandard care.

Clinical Competence↗
PubMed1991

Hospital characteristics associated with adverse events and substandard care.

To explore the epidemiology of adverse events (AEs), which were defined as injuries due to medical treatment, and that subset of AEs caused by negligence, we studied interhospital variation in these outcomes in a sample of 31,000 medical records drawn from a random selection of 51 hospitals in New York in 1984. We found a substantial variation in both AE rates (0.2% to 7.9%; mean, 3.2%) and the percentage of AEs due to negligence (1% to 60%; mean, 24.9%) among hospitals. Univariate analyses of AEs revealed that primary teaching institutions had significantly higher rates (4.1%) and rural hospitals had significantly lower ones (1.0%). The percentage of AEs due to negligence was lower in primary teaching (10.7%) and for-profit (9.5%) hospitals and was significantly higher in hospitals with predominantly (greater than 80%) minority patients who had been discharged (37%). These findings were corroborated by multivariate analysis. Our results suggest that AEs and negligence are not randomly distributed and that certain types of hospitals have significantly higher rates of injuries due to substandard care. These observations may represent an important improvement on existing measures of quality because they take into account the fact that some hospitals' populations may be at risk of suffering a poor outcome.

Hospital Records↗
PubMed1991

Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I.

BACKGROUND: As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care. METHODS: We reviewed 30,121 randomly selected records from 51 randomly selected acute care, nonpsychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians. RESULTS: Adverse events occurred in 3.7 percent of the hospitalizations (95 percent confidence interval, 3.2 to 4.2), and 27.6 percent of the adverse events were due to negligence (95 percent confidence interval, 22.5 to 32.6). Although 70.5 percent of the adverse events gave rise to disability lasting less than six months, 2.6 percent caused permanently disabling injuries and 13.6 percent led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi 2 = 21.04, P less than 0.0001). Using weighted totals, we estimated that among the 2,671,863 patients discharged from New York hospitals in 1984 there were 98,609 adverse events and 27,179 adverse events involving negligence. Rates of adverse events rose with age (P less than 0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (P less than 0.01). There were significant differences in rates of adverse events among categories of clinical specialties (P less than 0.0001), but no differences in the percentage due to negligence. CONCLUSIONS: There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.

Adolescent↗
PubMed1991

Modelling adolescent blood pressure patterns and their prediction of adult pressures.

Tracking of blood pressure in adolescent boys is investigated using a mathematical model that corresponds to progression along a constant percentile. A more general analysis, based on the method of principal components, is also proposed that determines various alternative tracks or patterns that are most prevalent in the longitudinal blood pressure data. The degree of tracking along a constant percentile curve for systolic pressure was moderately high, as evidenced by a tracking index of .78 explaining 81% of the variance, but less strong for diastolic (tracking index of .60) where tracking along a percentile explained 66% of the variance. The value of the more general analysis of blood pressure patterns may lie in the assessment of adolescent risk factors for elevated adult blood pressure. Using adolescent patterns determined by either statistical model, adult systolic at age 38 was predicted (R2 = .22) by the concept of a systolic fixed percentile curve in adolescence, and similarly for diastolic (R2 = .21). However, the more general analysis based on longitudinal principal components further suggests that boys who have a larger than usual systolic peak at age 14 years, which is near the time of the adolescent physical growth spurt in these boys, may be more likely to have higher systolic pressures at age 38. Because the adult data were incomplete and highly unbalanced, these findings were obtained using random-effects models for longitudinal data.

Adolescent↗
PubMed1990

Identification of adverse events occurring during hospitalization. A cross-sectional study of litigation, quality assurance, and medical records at two teaching hospitals.

STUDY OBJECTIVES: To estimate the efficacy of a medical record review for identifying adverse events and negligent case suffered by hospitalized patients. DESIGN: Cross-sectional study comparing an objective medical record review with information available from hospital quality assurance records as well as risk management and litigation records. SETTING: Two metropolitan teaching hospitals in the northeastern United States. MEASUREMENTS AND MAIN RESULTS: Using the litigation and risk management records as a criterion standard, we found that the medical record review had a sensitivity of 80% (93 of 116; 95% CI, 73% to 88%) for discovering adverse events and a sensitivity of 76% (51 of 67; 95% CI, 66% to 86%) for discovering negligent care. We estimated that record review of a random sample of hospitalizations across a geographic region would have even higher sensitivity (adverse-event sensitivity, 84%; negligence sensitivity, 80%). Moreover, we found that the adverse events we failed to discover led to less costly malpractice claims. A significant number of adverse events (20 of 172) among hospitalizations never gave rise to litigation or risk management investigation. Six of the twenty were due to negligent care. Quality assurance efforts at the level of the clinical departments in one hospital led to review of only 12 out of 82 risk management records. CONCLUSIONS: The overwhelming majority of adverse events and episodes of negligent care are discoverable with the methods we used to evaluate medical records. Quality assurance efforts using similar record review methods should be further evaluated.

Cross-Sectional Studies↗
PubMed1990

Maximum likelihood regression methods for paired binary data.

We discuss maximum likelihood methods for analysing binary responses measured at two times, such as in a cross-over design. We construct a 2 x 2 table for each individual with cell probabilities corresponding to the cross-classification of the responses at the two times; the underlying likelihood for each individual is multinomial with four cells. The three dimensional parameter space of the multinomial distribution is completely specified by the two marginal probabilities of success of the 2 x 2 table and an association parameter between the binary responses at the two times. We examine a logistic model for the marginal probabilities of the 2 x 2 table for individual i; the association parameters we consider are either the correlation coefficient, the odds ratio or the relative risk. Simulations show that the parameter estimates for the logistic regression model for the marginal probabilities are not very sensitive to the parameters used to describe the association between the binary responses at the two times. Thus, we suggest choosing the measure of association for ease of interpretation.

Humans↗
PubMed1990

Estimating rates of change in randomized clinical trials.

This article deals with the extension of the pretest-posttest clinical trial to the longitudinal data setting. We assume that a baseline (or pretest) measurement is taken on all individuals, who are then randomized, without regard to baseline values, to a treatment group. Repeated measurements are taken postrandomization at specified times. Our objective is to estimate the average rate of change (or slope) in the experimental groups and the differences in the slopes. Our focus is on the optimal use of the baseline measurements in the analysis. We contrast two different approaches:--a multivariate one that regards the entire vector of responses (including the baseline) as random outcomes and a univariate one that uses each individual's least squares slope as an outcome. Our multivariate approach is essentially a generalization of Stanek's Seemingly Unrelated Regression (SUR) estimator for the pretest-posttest design. The multivariate approach is natural to apply in this setting, and optimal if the assumed model is correct. However, the most efficient estimator requires assuming that the baseline mean parameters are the same for all experimental groups. Although this assumption is reasonable in the randomized setting, the resulting multivariate estimator uses postrandomization data as a covariate; if the assumed linear model is not correct, this can lead to distortions in the estimated treatment effect. We propose instead a reduced form multivariate estimator that may be somewhat less efficient, but protects against model misspecification.

Analysis of Variance↗
PubMed1990

Some statistical methods for combining experimental results.

Advances in science and technology are generally the product of multiple investigations. This article discusses statistical methods for combining empirical results from a series of different experiments or clinical investigations. We delineate the steps an assessor might take in combining data from different studies and provide references for topics not discussed in detail. The article reviews some of the most commonly used statistical techniques for combining results in the medical and social sciences.

Analysis of Variance↗