Ingrid A. Amara

Long-Term Prognosis of Mitral-Valve Prolapse

We examined the natural history of mitral-valve prolapse in 53 patients who had had a midsystolic click or late systolic murmur (or both) documented phonocardiographically a mean of 13.7 years earlier. Thirty-eight patients were alive without serious complications, and seven had died of unrelated causes. In two patients prolapse was implicated in the cause of death. Other complications were ventricular fibrillation in one patient and bacterial endocarditis in three. Progressive mitral regurgitation developed in five patients, requiring valve replacement in two. These complications occurred in a total of eight patients (15 per cent), and were significantly (P = 0.15) associated with a late systolic murmur rather than an isolated midsystolic click. Thus it appears that the diagnosis of mitral-valve prolapse should not be regarded as ominous; however, patients in whom this diagnosis is associated with a late systolic murmur should be followed carefully.

Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them

Analysis of covariance is an effective method for addressing two considerations for randomized clinical trials. One is reduction of variance for estimates of treatment effects and thereby the production of narrower confidence intervals and more powerful statistical tests. The other is the clarification of the magnitude of treatment effects through adjustment of corresponding estimates for any random imbalances between the treatment groups with respect to the covariables. The statistical basis of covariance analysis can be either non-parametric, with reliance only on the randomization in the study design, or parametric through a statistical model for a postulated sampling process. For non-parametric methods, there are no formal assumptions for how a response variable is related to the covariables, but strong correlation between response and covariables is necessary for variance reduction. Computations for these methods are straightforward through the application of weighted least squares to fit linear models to the differences between treatment groups for the means of the response variable and the covariables jointly with a specification that has null values for the differences that correspond to the covariables. Moreover, such analysis is similarly applicable to dichotomous indicators, ranks or integers for ordered categories, and continuous measurements. Since non-parametric covariance analysis can have many forms, the ones which are planned for a clinical trial need careful specification in its protocol. A limitation of non-parametric analysis is that it does not directly address the magnitude of treatment effects within subgroups based on the covariables or the homogeneity of such effects. For this purpose, a statistical model is needed. When the response criterion is dichotomous or has ordered categories, such a model may have a non-linear nature which determines how covariance adjustment modifies results for treatment effects. Insight concerning such modifications can be gained through their evaluation relative to non-parametric counterparts. Such evaluation usually indicates that alternative ways to compare treatments for a response criterion with adjustment for a set of covariables mutually support the same conclusion about the strength of treatment effects. This robustness is noteworthy since the alternative methods for covariance analysis have substantially different rationales and assumptions. Since findings can differ in important ways across alternative choices for covariables (as opposed to methods for covariance adjustment), the critical consideration for studies with covariance analyses planned as the primary method for comparing treatments is the specification of the covariables in the protocol (or in an amendment or formal plan prior to any unmasking of the study.

A review of some statistical methods for covariance analysis of categorical data.

Three general methods for covariance analysis of categorical data are reviewed and applied to an example from a clinical trial in rheumatoid arthritis. The three methods considered are randomization-model nonparametric procedures, maximum likelihood logistic regression, and weighted least squares analysis of correlated marginal functions. A fourth heuristic approach, the unweighted linear model analysis, is an approximate procedure but it is easy to implement. The assumptions and statistical issues for each method are discussed so as to emphasize philosophical differences between their rationales. Attention is given to computational differences, but it is shown that the methods lead to similar results for analogous problems. It is argued that the essential differences between the methods lie in their underlying assumptions and the generality of the conclusions which may be drawn.

Thresholds, refractory periods, and conduction times of the normal and diseased human atrium

In order to better understand the electrophysiology of the diseased human atrium, we measured high right atrial refractory periods, threshold, and conduction times of 61 patients undergoing routine electrophysiologic study. Refractory periods and conduction times of patients with apparently normal atria were compared to those of patients with a history of persistent sinus bradycardia, atrial fibrillation, or other forms of primary atrial tachyarrhythmia. Refractory periods and thresholds were derived from strength-interval curves. Conduction times were measured for all premature beats induced. Threshold, refractory periods, and conduction times of premature beats induced late in the cardiac cycle did not distinguish patients with normal atria from patients with bradycardia or tachycardia. In contrast, increases in conduction time of early cycle premature beats separated patients with these abnormalities from patients with normal atria. The increases in interatrial and intraatrial conduction time of early cycle premature beats were the strongest correlates of primary atrial tachyarrhythmia (r = 0.52, p = 0.0065 and r = 0.274, p = 0.041, respectively) and induction of repetitive atrial firing (r = 0.65, p = 0.002, and r = 0.59, p = 0.0001, respectively). This increase in conduction time of early cycle premature beats may predispose these patients to primary atrial tachyarrhythmias.

Some Views on Parametric and Non-Parametric Analysis for Repeated Measurements and Selected Bibliography

A common feature of many statistical investigations is the collection of data from groups of experimental units each of which is observed under two or more conditions. Such studies are generally called either split-plot experiments or repeated measurements experiments. This paper is concerned with reviewing general statistical strategies for the analysis of data from these types of research designs. For this purpose, primary attention is directed at two basic dimensions. One of these is the nature of the randomization processes for the data as obtained among and within the experimental units. The other is the level of the measurement scale as either nominal, ordinal, or interval. This framework is then used as the basis of discussion of alternative statistical methods such as repeated measurements analysis of variance, multivariate analysis of variance, and their non-parametric rank and categorical data counterparts in both a general sense and for some specific classes of examples. Finally, a selected bibliography of references for these and related methods is given.

Methylphenidate and memory: Dissociated effects in hyperactive children

Fourteen children with Attention Deficit Disorder with Hyperactivity (ADD + H) were administered the psychostimulant methylphenidate in a double-blind, placebo-controlled, crossover study. Subjects were evaluated on a well-validated measure of verbal memory and learning with an experimental design comprised of four conditions: placebo and active drug at three doses. Positive memory effects were found in the drug conditions. Significant dose-response relationships were found, indicating enhanced learning from placebo to low to medium to high dose. However, there was a differential drug effect on the memory task; methylphenidate selectively enhanced storage and retrieval mechanisms without affecting immediate acquisition.

Noninvasive assessment of pulmonary hypertension from right ventricular isovolumic contraction time

In order to assess a noninvasive method of predicting pulmonary arterial pressure in adults, right ventricular systolic time intervals were determined with echocardiography simultaneously with pulmonary arterial end-diastolic pressure measurements. Right ventricular isovolumic contraction time was measured from echographic recordings of the tricuspid and pulmonary valves. Although this interval was found to increase as pulmonary arterial pressure increased, the method cannot be used to predict quantitatively the level of pulmonary arterial pressure in adults. However, an echocardiographically determined right ventricular contraction time of less than 25 ms suggests a normal pulmonary arterial pressure. In patients with pulmonary parenchymal diseases, echograms of the tricuspid and pulmonary valves are only rarely of such quality as to permit accurate delineation of the valvular events required for these measurements.

Statistical Issues in the Design and Analysis of Ulcer Healing and Recurrence Studies

Statistical considerations are discussed for a randomized parallel groups study to compare treatments for the healing of ulcers during a specified time period of dosing and for the avoidance of subsequent ulcer recurrence (in patients with healing) during a follow-up period with no medication. For this study, randomization enables valid comparisons of treatments for the rates of healing during the dosing period and for the cumulative rates of being ulcer-free (ie, healing and no recurrence subsequent to healing) during the combined dosing and follow-up periods. Appropriate methods of analysis include Mantel-Haenszel tests and logistic regression for dichotomous outcomes and their life table counterparts for time to event outcomes. For recurrence rates among patients with healing, the basis for comparisons among treatments is unclear because of potential lack of similarity of treatment groups for risk factors for recurrence at the beginning of the follow-up period. This difficulty can be addressed by interpreting recurrence rates within treatment groups as descriptive for corresponding populations with healing. Moreover, such descriptions can involve statistical models which account for the effects of risk factors. Consideration is additionally given to sample size determination and other aspects of the design for a healing and recurrence study.