Biomedical subjects

J K Lindsey

Publications and source records attributed to J K Lindsey.

At least 19 recordsLinked to original sources

PubMed2001

A general family of distributions for longitudinal dependence with special reference to event histories.

Event histories play an increasingly important role in medical studies. Examples include times between recurrences of tumours, as with bladder cancer, and between repeated infections, as with chronic granulotomous disease. A general method for generating new distributions is proposed by introducing an intensity function into a density. This procedure yields, as special cases, several distributions already proposed in the literature. The families of distributions based on the Pareto distribution are of particular interest for event history analysis because of their relationship to the Laplace transform of a gamma distribution. They can yield multivariate distributions, with longitudinal (serial) dependence by a procedure similar to updating in the Kalman filter and with uniform dependence in a similar way to copulas. For longitudinal dependence, several such updating procedures are proposed.

Antineoplastic Agents, Alkylating↗

PubMed2000

Obtaining marginal estimates from conditional categorical repeated measurements models with missing data.

The most commonly used models for categorical repeated measurement data are log-linear models. Not only are they easy to fit with standard software but they include such useful models as Markov chains and graphical models. However, these are conditional models and one often also requires the marginal probabilities of responses, for example, at each time point in a longitudinal study. Here a simple method of matrix manipulation is used to derive the maximum likelihood estimates of the marginal probabilities from any such conditional categorical repeated measures model. The technique is applied to the classical Muscatine data set, taking into account the dependence of missingness on previous observed values, as well as serial dependence and a random effect.

Adolescent↗

PubMed2000

Directly modelling matched case-control data.

Matching in case-control studies is a situation in which one wishes to make inferences about a parameter of interest in the presence of nuisance parameters. The usual approach is to apply a conditional likelihood. A bivariate latent class log-linear model for binomial responses is shown to yield a standard likelihood identical to the usual conditional one. This extension of the Rasch model for binary responses gives consistent estimates and a suitable likelihood function for cases matched with any fixed number of controls.

Age Factors↗

PubMed2000

Dropouts in longitudinal studies: definitions and models.

The widely used distinction of Little and Rubin (1) about types of randomness for missing data presents difficulties in its application to dropouts in longitudinal repeated measurement studies. In its place, a new typology of randomness for dropouts is proposed that relies on using a survival model for the dropout process. In terms of a stochastic process, dropping out is a change of state. Then, the longitudinal measures and dropout processes can be modeled simultaneously, each conditional on the complete previous history of both repeated measures and states. In this context, Poisson regression is used to fit various proportional hazards models, some of which are new, to the dropout process using the longitudinal measurements responses as time-varying covariates. As examples of longitudinal measurement studies displaying nonrandom dropout processes, a dental study of testosterone production in rats and clinical trials for treatment of gallstones and of depression are analyzed.

Algorithms↗

PubMed2000

Modeling pharmacokinetic data using heavy-tailed multivariate distributions.

Pharmacokinetic studies of drug and metabolite concentrations in the blood are usually conducted as crossover trials, especially in Phases I and II. A longitudinal series of measurements is collected on each subject within each period. Dependence among such observations, within and between periods, will generally be fairly complex, requiring two levels of variance components, for the subjects and for the periods within subjects, and an autocorrelation within periods as well as a time-varying variance. Until now, the standard way in which this has been modeled is using a multivariate normal distribution. Here, we introduce procedures for simultaneously handling these various types of dependence in a wider class of distributions called the multivariate power exponential and Student t families. They can have the heavy tails required for handling the extreme observations that may occur in such contexts. We also consider various forms of serial dependence among the observations and find that they provide more improvement to our models than do the variance components. An integrated Ornstein-Uhlenbeck (IOU) stochastic process fits much better to our data set than the conventional continuous first-order autoregression, CAR(1). We apply these models to a Phase I study of the drug, flosequinan, and its metabolite.

Clinical Trials, Phase I as Topic↗

PubMed2000

Generalized nonlinear models for pharmacokinetic data.

Phase I trials to study the pharmacokinetic properties of a new drug generally involve a restricted number of healthy volunteers. Because of the nature of the group involved in such studies, the appropriate distributional assumptions are not always obvious. These model assumptions include the actual distribution but also the ways in which the dispersion of responses is allowed to vary over time and the fact that small concentrations of a substance are not easily detectable and hence are left censored. We propose that a reasonably wide class of generalized nonlinear models allowing for left censoring be considered now that this is feasible with current computer power and sophisticated statistical packages. These modelling strategies are applied to a Phase I study of the drug flosequinan and its metabolite. This drug was developed for the treatment of heart failure. Because the metabolite also exhibits an active pharmacologic effect, study of both the parent drug and the metabolite is of interest.

Adult↗

PubMed1999

Modeling the covariance structure in pharmacokinetic crossover trials.

Pharmacokinetic studies of drug and metabolite concentrations in the blood are usually conducted as crossover trials, especially in phases I and II. A longitudinal series of measurements is collected on each subject within each period. However, much of the dependence among such observations, within and between periods, is generally ignored in analyzing this type of data. Usually, only a random coefficient model is fitted for the parameters in the nonlinear mean function, along with allowing the variance to depend on the mean so that it changes over time. Here, we develop models to allow more fully for the structure of the crossover study. We introduce two levels of variance components, for the subjects and for the periods within subjects, and also an autocorrelation within periods. We also retain the time-varying variance, using a separate variance function for this, different from that for the mean. We apply this model to a phase I study of the drug flosequinan and its metabolite. This drug was developed for the treatment of heart failure. Because the metabolite also exhibits an active pharmacologic effect, study of both the parent drug and the metabolite is of interest. We find that the autocorrelation is the element in the covariance structure that most improves the fit of the model but that two levels of variance components can also be necessary.

Body Fluid Compartments↗

PubMed1999

Response surfaces for overdispersion in the study of the conditions for fish eggs hatching.

Response surface methodology, originally developed for determining optimal conditions in industrial experiments, was early adapted to experiments in marine ecology. However, these involved studying the shape of the complete response surface, not only detecting the optimum, and often had counts or durations as the response variable. Thus, nonlinear, nonnormal response models were required. For counts, binomial and beta-binomial models have been used, the latter because of substantial overdispersion. In closely controlled experiments, overdispersion among units held under the same conditions might indicate that some mishap has occurred in conducting the study. One possible check is to model the dispersion as a second response surface. This procedure is used to show that overdispersion in fish egg hatching experiments has a biological explanation in that it occurs only under suboptimal hatching conditions.

Animals↗

PubMed1999

Multivariate elliptically contoured distributions for repeated measurements.

The multivariate power exponential distribution, a member of the multivariate elliptically contoured family, provides a useful generalization of the multivariate normal distribution for the modeling of repeated measurements. Both light and heavy tailed distributions are included. The covariance matrix retains its interpretation so that it can easily be structured for serial dependence and several levels of variance components. A crossover trial on insulin applied to rabbits, with a series of repeated measurements within each period, is analyzed by means of this distribution using autocorrelation and two levels of variance components.

Animals↗

PubMed1998

On the appropriateness of marginal models for repeated measurements in clinical trials.

Although models developed directly to describe marginal distributions have become widespread in the analysis of repeated measurements, some of their disadvantages are not well enough known. These include producing profile curves that correspond to no possible individual, possibly showing that a treatment is superior on average when it is poorer for each individual subject, implicitly generating complex and implausible physiological explanations, including underdispersion in subgroups, and sometimes corresponding to no possible probabilistic data generating mechanism. We conclude that such marginal models may sometimes be appropriate for descriptive observational studies, such as sample surveys in epidemiology, but should only be used with great care in causal experimental settings, such as clinical trials.

Clinical Trials as Topic↗

PubMed1998

Choosing among generalized linear models applied to medical data.

When testing for a treatment effect or a difference among groups, the distributional assumptions made about the response variable can have a critical impact on the conclusions drawn. For example, controversy has arisen over transformations of the response (Keene). An alternative approach is to use some member of the family of generalized linear models. However, this raises the issue of selecting the appropriate member, a problem of testing non-nested hypotheses. Standard model selection criteria, such as the Akaike information criterion (AIC), can be used to resolve problems. These procedures for comparing generalized linear models are applied to checking for difference in T4 cell counts between two disease groups. We conclude that appropriate model selection criteria should be specified in the protocol for any study, including clinical trials, in order that optimal inferences can be drawn about treatment differences.

Clinical Trials as Topic↗

PubMed1998

A study of interval censoring in parametric regression models.

Parametric models for interval censored data can now easily be fitted with minimal programming in certain standard statistical software packages. Regression equations can be introduced, both for the location and for the dispersion parameters. Finite mixture models can also be fitted, with a point mass on right (or left) censored observations, to allow for individuals who cannot have the event (or already have it). This mixing probability can also be allowed to follow a regression equation. Here, models based on nine different distributions are compared for three examples of heavily censored data as well as a set of simulated data. We find that, for parametric models, interval censoring can often be ignored and that the density, at centres of intervals, can be used instead in the likelihood function, although the approximation is not always reliable. In the context of heavily interval censored data, the conclusions from parametric models are remarkably robust with changing distributional assumptions and generally more informative than the corresponding non-parametric models.

Animals↗

PubMed1997

Simple models for repeated ordinal responses with an application to a seasonal rhinitis clinical trial.

In contrast to other models for ordinal data, the continuation ratio model can be fitted with standard statistical software. This makes it particularly appropriate for large clinical trials with ordinal response variables. In addition, when the trials are longitudinal, this model can be applied to individual responses instead of frequencies in contingency tables. Dependence can be incorporated by conditioning on the previous response, yielding a form of Markov chain. This approach is applied to the analysis of a large seasonal rhinitis trial, where patients were observed over 28 days and six symptoms recorded as ordinal responses.

Humans↗

PubMed1997

Treatment-patient interactions for diagnostics of cross-over trials.

In cross-over trials, various types of responses may be recorded, not all of which can be appropriately modelled by a Normal distribution. Widening the class of models to the generalized linear model family has a number of advantages. An important one is that certain interactions, especially that between patients and treatments, can easily be fitted for frequency and count data. These can be used as diagnostics for the fit of the model used. One handicap has been the frequentist difficulty of comparing the fit of different non-nested models in this family. This can be overcome by the use of a model selection criterion such as the Akaike or Bayesian information criterion. This approach to modelling and diagnostics for cross-over trials is applied to two studies involving small counts of anginal attacks, previously analysed in the literature using classical Normal techniques.

Clinical Trials as Topic↗

PubMed1996

Deposition in the distal parts of the bovine respiratory tract: assessment of equipment suitable for drug inhalation.

The efficiency of equipment suitable for the inhalation of drugs by calves was assessed in six animals which inhaled radioisotopically labelled particles while suffering from reversible diffuse bronchoconstriction induced experimentally with 5-hydroxytryptamine and while they were breathing normally. Respiratory rates and data from pulmonary function tests and scintiscans were recorded during both investigations. After the first investigation, a mean (se) wash-out period of 9.8 (3.2) days was allowed. Under diffuse bronchoconstriction, the respiratory rate, the oscillatory resistance and the compliance of the respiratory system reached 282.1 (22.0), 161.1 (10.8) and 68.8 (2.7) per cent of their respective baseline values. When the calves were breathing normally these parameters did not change over time. The ratios (Cp/Ct) of the counts of gamma-disintegrations in the peripheral part (Cp) of the lungs and in the total lung area (Ct) were not significantly different when comparing the results from the two investigations. The ratios of Cp/Ct in the left lungs did not differ significantly from those in the right lungs.

Administration, Inhalation↗

PubMed1996

Analysis of cross-over trials for duration data.

Survival models and cross-over designs both have an established place in biomedical research. Surprisingly, there are a few examples of proper exploitation of two in combination. A number of advantages and disadvantages of such studies are discussed. Two examples are used to illustrate the application of semi-Markov models with time-varying covariates, as standard log-linear models, to such data.

Cross-Over Studies↗

PubMed1996

A model for cross-over trials evaluating therapeutic preferences.

A preference trial is a special form of cross-over trial where clinical conditions determine when patients change treatment, in a prescribed order. This can be modelled using a geometric distribution. The model can be simply fitted using standard logistic regression methodology. The procedure is applied to a trial studying the effects of bronchodilators in the treatment of chronic asthma.

Asthma↗

PubMed1995

Pulmonary function changes induced by three regimens of bronchodilating agents in calves with acute respiratory distress syndrome.

Two aerosolised bronchodilators, one sympathomimetic and one parasympatholytic, were tested either alone or in combination for their ability to improve the pulmonary function of double-muscled calves suffering from acute respiratory distress syndrome. In control animals treated with 0.9 per cent saline the parameters of pulmonary function and signs of clinical distress did not change significantly within the hour following the first treatment. Among the other animals, both at one hour and seven days after the first treatment, the most clinical improvement was observed in the animals treated with both bronchodilators and the least in the animals treated with clenbuterol hydrochloride. One hour after the first treatment the respiratory system compliance of the animals treated with ipratropium bromide and the arterial oxygen tension of the animals treated with both bronchodilators were significantly enhanced. After seven days the resistive parameters, the rectal temperature and the respiratory rate were also significantly improved in the animals treated with ipratropium bromide or both bronchodilators whereas only the respiratory rate and rectal temperature were significantly reduced in the animals treated with clenbuterol hydrochloride.

Acute Disease↗