Biomedical subjects

Dean A Follmann

Publications and source records attributed to Dean A Follmann.

4 recordsLinked to original sources

PubMed2003

Comparing HLA antigen frequencies between two groups of patients.

For diseases that involve the immune system, the alleles of the human leukocyte antigen (HLA) complex can play a major role. For example, if responsiveness to therapy is immunologically mediated, one would think that responders and non-responders might tend to have different HLA alleles. However, comparing the frequencies between the two groups of patients at each allele can introduce a substantial multiple comparisons problem as the number of alleles is large. This paper proposes an efficient two-stage procedure for identifying alleles that may mediate response. In the first-stage, the distribution of all alleles for the patients are compared to a reference population and a few alleles are selected. These candidate alleles are then compared between the two groups of patients using a modest Bonferroni correction. The two-stage procedure strongly controls the type I error rate as the first-stage selection is statistically independent of the second-stage tests. We analyse a cohort of patients with bone marrow failure who are classified as responders or non-responders to immunosuppressive therapy. Published in 2003 by John Wiley & Sons, Ltd.

Alleles↗

PubMed2002

Regression analysis based on pairwise ordering of patients' clinical histories.

When a medical treatment influences a variety of outcomes, describing the global effect of treatment can be difficult. Traditional approaches specify how treatment affects each separate outcome. This can be done with separate models for each outcome, or by using a combined multivariate model. Describing the overall effect of a treatment thus requires combining these separate effects in some fashion and can be difficult to explain. In this paper, I specify a regression model for use with multiple outcomes where the outcome histories for each pair of patients are ranked. Pairs of patients with different lengths of follow-up are evaluated solely over the common follow-up interval. The logit of the probability that the outcome for patient i is better than that of patient j is assumed to depend on a linear function of the difference of the covariate vectors (for example, treatment indicators) for persons i and j. Thus covariates directly affect the entire clinical history, rather than directly affecting specific outcomes that comprise the history. The idea is that ranking outcomes is more relevant and interpretable than statistically combining separate effects. An estimating equations approach for estimation is described and an example of a clinical trial involving patients with heart failure is provided.

Angiotensin-Converting Enzyme Inhibitors↗

PubMed2002

Parametric and semiparametric approaches to testing for seasonal trend in serial count data.

We present two tests for seasonal trend in monthly incidence data. The first approach uses a penalized likelihood to choose the number of harmonic terms to include in a parametric harmonic model (which includes time trends and autogression as well as seasonal harmonic terms) and then tests for seasonality using a parametric bootstrap test. The second approach uses a semiparametric regression model to test for seasonal trend. In the semiparametric model, the seasonal pattern is modeled nonparametrically, parametric terms are included for autoregressive effects and a linear time trend, and a parametric bootstrap test is used to test for seasonality. For both procedures, a null distribution is generated under a null Poisson model with time trends and autoregression parameters. We apply the methods to skin melanoma incidence rates collected by the surveillance, epidemiology, and end results (SEER) program of the National Cancer Institute, and perform simulation studies to evaluate the type I error rate and power for the two procedures. These simulations suggest that both procedures are alpha-level procedures. In addition, the harmonic model/bootstrap test had similar or larger power than the semiparametric model/bootstrap test for a wide range of alternatives, and the harmonic model/bootstrap test is much easier to implement. Thus, we recommend the harmonic model/bootstrap test for the analysis of seasonal incidence data.

Journal Article↗

PubMed2002

A latent autoregressive model for longitudinal binary data subject to informative missingness.

Longitudinal clinical trials often collect long sequences of binary data. Our application is a recent clinical trial in opiate addicts that examined the effect of a new treatment on repeated binary urine tests to assess opiate use over an extended follow-up. The dataset had two sources of missingness: dropout and intermittent missing observations. The primary endpoint of the study was comparing the marginal probability of a positive urine test over follow-up across treatment arms. We present a latent autoregressive model for longitudinal binary data subject to informative missingness. In this model, a Gaussian autoregressive process is shared between the binary response and missing-data processes, thereby inducing informative missingness. Our approach extends the work of others who have developed models that link the various processes through a shared random effect but do not allow for autocorrelation. We discuss parameter estimation using Monte Carlo EM and demonstrate through simulations that incorporating within-subject autocorrelation through a latent autoregressive process can be very important when longitudinal binary data is subject to informative missingness. We illustrate our new methodology using the opiate clinical trial data.

Algorithms↗