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Full-Text Articles in Biostatistics
Estimation Of Dose-Response Functions For Longitudinal Data, Erica E M Moodie, David A. Stephens
Estimation Of Dose-Response Functions For Longitudinal Data, Erica E M Moodie, David A. Stephens
COBRA Preprint Series
In a longitudinal study of dose-response, the presence of confounding or non-compliance compromises the estimation of the true effect of a treatment. Standard regression methods cannot remove the bias introduced by patient-selected treatment level, that is, they do not permit the estimation of the causal effect of dose. Using an approach based on the Generalized Propensity Score (GPS), a generalization of the classical, binary treatment propensity score, it is possible to construct a balancing score that provides a more meaningful estimation procedure for the true (unconfounded) effect of dose. Previously, the GPS has been applied only in a single interval …
An Example Of How To Write The Statistical Section Of A Bioequivalence Study Protocol For Fda Review, William F. Mccarthy
An Example Of How To Write The Statistical Section Of A Bioequivalence Study Protocol For Fda Review, William F. Mccarthy
COBRA Preprint Series
This paper provides a detailed example of how one should write the statistical section of a bioequivalence study protocol for FDA review. Three forms of bioequivalence are covered: average bioequivalence (ABE), population bioequivalence (PBE) and individual bioequivalence (IBE). The method of analysis is based on Jones and Kenward (2003) and a modification of their SAS Macro is provided.
Adjustment To The Mcnemar’S Test For The Analysis Of Clustered Matched-Pair Data, William F. Mccarthy
Adjustment To The Mcnemar’S Test For The Analysis Of Clustered Matched-Pair Data, William F. Mccarthy
COBRA Preprint Series
This paper presents how one can adjust the McNemar’s test for the analysis of clustered matched-pair data. A McNemar’s-like table for K clusters of matched-pair data is used.
Assessment Of Sample Size And Power For The Analysis Of Clustered Matched-Pair Data, William F. Mccarthy
Assessment Of Sample Size And Power For The Analysis Of Clustered Matched-Pair Data, William F. Mccarthy
COBRA Preprint Series
This paper outlines how one can determined the sample size or power of a study design that is based on clustered matched-pair data. Detailed examples are provided.
The Existence Of Maximum Likelihood Estimates For The Binary Response Logistic Regression Model, William F. Mccarthy
The Existence Of Maximum Likelihood Estimates For The Binary Response Logistic Regression Model, William F. Mccarthy
COBRA Preprint Series
The existence of maximum likelihood estimates for the binary response logistic regression model depends on the configuration of the data points in your data set. There are three mutually exclusive and exhaustive categories for the configuration of data points in a data set: Complete Separation, Quasi-Complete Separation, and Overlap. For this paper, a binary response logistic regression model is considered. A 2 x 2 tabular presentation of the data set to be modeled is provided for each of the three categories mentioned above. In addition, the paper will present an example of a data set whose data points have a …
Lachenbruch’S Method For Determining The Sample Size Required For Testing Interactions: How It Compares To Nquery Advisor And O’Brien’S Sas Unifypow., William F. Mccarthy
Lachenbruch’S Method For Determining The Sample Size Required For Testing Interactions: How It Compares To Nquery Advisor And O’Brien’S Sas Unifypow., William F. Mccarthy
COBRA Preprint Series
Lachenbruch (1988) proposed a simple method based on the use of orthogonal contrasts to determine the sample size or power for testing main effects and interactions, and uses the normal distribution instead of the non-central F distribution. This method can be used for factorial designs of various size. The example illustrated in this paper considers a 2 x 2 factorial design. This paper will determine both sample size and power of a particular study design with anticipated (assumed) means for each cell of the 2 x 2 factorial design. Lachenbruch’s method will be compared to nQuery Advisor 6.0 (2005) and …
The Assessment Of The Degree Of Concordance Between The Observed Values And The Predicted Values Of A Mixed-Effect Model Using “Method Of Comparison” Techniques, William F. Mccarthy, Nan Guo
The Assessment Of The Degree Of Concordance Between The Observed Values And The Predicted Values Of A Mixed-Effect Model Using “Method Of Comparison” Techniques, William F. Mccarthy, Nan Guo
COBRA Preprint Series
In this paper, we present a methodology for determining the degree of concordance between observed and model-based predicted values of a mixed-effect model. In particular, we will compare the degree to which observed and model-based predicted values agree by using ‘method of comparison’ techniques. We will also present the results of the concordance correlation coefficient (CCC).
The Analysis Of Pixel Intensity (Myocardial Signal Density) Data: The Quantification Of Myocardial Perfusion By Imaging Methods., William F. Mccarthy, Douglas R. Thompson
The Analysis Of Pixel Intensity (Myocardial Signal Density) Data: The Quantification Of Myocardial Perfusion By Imaging Methods., William F. Mccarthy, Douglas R. Thompson
COBRA Preprint Series
This paper described a number of important issues in the analysis of pixel intensity data, as well as approaches for dealing with these. We particularly emphasized the issue of clustering, which may be ubiquitous in studies of pixel intensity data. Clustering can take many forms, e.g., measurements of different sections of a heart or repeated measurements of the same research participant. Clustering typically has the effect of increasing variance estimates. When one fails to account for clustering, variance estimates may be unrealistically small, resulting in spurious significance. We illustrated several possible approaches to account for clustering, including adjusting standard errors …
A Flexible Semi-Parametric Approach To Estimating A Dose-Response Relationship: The Treatment Of Childhood Amblyopia. , David A. Stephens, Erica E M Moodie
A Flexible Semi-Parametric Approach To Estimating A Dose-Response Relationship: The Treatment Of Childhood Amblyopia. , David A. Stephens, Erica E M Moodie
COBRA Preprint Series
In a study of a dose-response relationship, flexibility in modelling is essential to capturing the treatment effect when the mean effect of other covariates is not fully understood, so that observed treatment effect is not due to the imposition of a rigid model for the relationship between response, treatment, and other variables. A semiparametric additive linear mixed (SPALM) model (Ruppert et al. 2003) provides a tractable and flexible approach to modelling the influence of potentially confounding variables. In this paper, we present pure likelihood and Bayesian versions of the SPALM model. Both methods of inference are readily implementable, but the …