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Measures Data (measure + data)

Distribution by Scientific Domains

Medical Sciences	50%
Mathematics and Statistics	50%

Selected Abstracts

Quality-of-Life and Behavioral Outcome Measures in Randomized Controlled Trials of Antiepileptic Drugs: A Systematic Review of Methodology and Reporting Standards

EPILEPSIA, Issue 11 2000
Gus A. Baker
Summary: Purpose: To review the methodology and use of quality-of-life and behavioral measures used in randomized controlled trials (RCTs) of antiepileptic drugs in patients with epilepsy. Methods: Trial reports were found by searching a previously developed comprehensive database of epilepsy RCTs and searching through journals by hand. Inclusion and exclusion criteria were applied, and methodological and quality-of-life and behavioral measure data were extracted. Results: There were 52 different measures used in 46 trials, with the Profile of Mood States, the Minnesota Multiphasic Personality Inventory, and the Washington Psychosocial Seizure Inventory being applied the most frequently. Overall, evidence of the reliability, validity, and sensitivity of measures used in populations of people with epilepsy was sparse. There was also little information on the clinical interpretation of the results. Conclusion: Our results highlight a consistent failure to apply quality-of-life and behavioral measures in RCTs in a systematic way. We found repeated evidence of researchers' failure to review the use of previous measures and selection of measures without evidence of their appropriateness for use in a population with epilepsy. We recommend the use of quality-of-life and behavioral measures in RCTs with proven psychometric properties in a population with epilepsy. [source]

A pilot study for a randomised controlled trial of waterbirth versus land birth

BJOG : AN INTERNATIONAL JOURNAL OF OBSTETRICS & GYNAECOLOGY, Issue 6 2004
Joanne Woodward
Objectives To assess the feasibility of undertaking an adequately powered multicentre study comparing waterbirth with land birth. To assess whether women are willing to participate in such a trial and whether participation has a negative effect on their birthing experience. Design A randomised controlled trial (RCT) with ,preference arm'. Setting District general hospital with 3600 deliveries annually. Population Women with no pregnancy complications and no anticipated problems for labour/delivery. Methods Women were recruited and randomised between 36 and 40 weeks of gestation. Comparison of randomised and ,preference arm' to assess any impact of randomisation on women's birthing experience. Main outcome measures Data were collected at delivery concerning the labour, the pool water and baby's condition at birth and six weeks of age. The main outcome measures are means and standard deviation of cord O2, CO2, haemoglobin, haematocrit and base excess; medians and ranges of time to first breathe and cord pH; bacterial growth from pool water samples and neonatal swabs; and maternal satisfaction. Results Eighty women participated,60 women were randomised. Twenty women participated in a non-randomised ,preference arm'. The babies randomised to a waterbirth demonstrated a significantly lower umbilical artery pCO2 (P= 0.003); however, it is recognised that this study is underpowered. Women were willing to participate and randomisation did not appear to alter satisfaction. Conclusion This small study has shown that a RCT is feasible and demonstrated outcome measures, which can be successfully collected in an average delivery suite. [source]

Multilevel models for longitudinal data

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2008
Fiona Steele
Summary., Repeated measures and repeated events data have a hierarchical structure which can be analysed by using multilevel models. A growth curve model is an example of a multilevel random-coefficients model, whereas a discrete time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle auto-correlated residuals, multiple-indicator latent variables and correlated growth processes, and event history models for correlated event processes. The multilevel approach to the analysis of repeated measures data is contrasted with structural equation modelling. The methods are illustrated in analyses of children's growth, changes in social and political attitudes, and the interrelationship between partnership transitions and childbearing. [source]

A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros

BIOMETRICS, Issue 2 2008
Dimitris Rizopoulos
Summary Many longitudinal studies generate both the time to some event of interest and repeated measures data. This article is motivated by a study on patients with a renal allograft, in which interest lies in the association between longitudinal proteinuria (a dichotomous variable) measurements and the time to renal graft failure. An interesting feature of the sample at hand is that nearly half of the patients were never tested positive for proteinuria (,1g/day) during follow-up, which introduces a degenerate part in the random-effects density for the longitudinal process. In this article we propose a two-part shared parameter model framework that effectively takes this feature into account, and we investigate sensitivity to the various dependence structures used to describe the association between the longitudinal measurements of proteinuria and the time to renal graft failure. [source]

Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout

BIOMETRICS, Issue 4 2004
Joseph W. Hogan
Summary The analysis of longitudinal repeated measures data is frequently complicated by missing data due to informative dropout. We describe a mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric. Specifically, we assume that responses follow a varying coefficient random effects model conditional on dropout time, where the regression coefficients depend on dropout time through unspecified nonparametric functions that are estimated using step functions when dropout time is discrete (e.g., for panel data) and using smoothing splines when dropout time is continuous. Inference under the proposed semiparametric model is hence more robust than the parametric conditional linear model. The unconditional distribution of the repeated measures is a mixture over the dropout distribution. We show that estimation in the semiparametric varying coefficient mixture model can proceed by fitting a parametric mixed effects model and can be carried out on standard software platforms such as SAS. The model is used to analyze data from a recent AIDS clinical trial and its performance is evaluated using simulations. [source]

Statistical methods for longitudinal research on bipolar disorders

BIPOLAR DISORDERS, Issue 3 2003
John Hennen
Objectives: Outcomes research in bipolar disorders, because of complex clinical variation over-time, offers demanding research design and statistical challenges. Longitudinal studies involving relatively large samples, with outcome measures obtained repeatedly over-time, are required. In this report, statistical methods appropriate for such research are reviewed. Methods: Analytic methods appropriate for repeated measures data include: (i) endpoint analysis; (ii) endpoint analysis with last observation carried forward; (iii) summary statistic methods yielding one summary measure per subject; (iv) random effects and generalized estimating equation (GEE) regression modeling methods; and (v) time-to-event survival analyses. Results: Use and limitations of these several methods are illustrated within a randomly selected (33%) subset of data obtained in two recently completed randomized, double blind studies on acute mania. Outcome measures obtained repeatedly over 3 or 4 weeks of blinded treatment in active drug and placebo sub-groups included change-from-baseline Young Mania Rating Scale (YMRS) scores (continuous measure) and achievement of a clinical response criterion (50% YMRS reduction). Four of the methods reviewed are especially suitable for use with these repeated measures data: (i) the summary statistic method; (ii) random/mixed effects modeling; (iii) GEE regression modeling; and (iv) survival analysis. Conclusions: Outcome studies in bipolar illness ideally should be longitudinal in orientation, obtain outcomes data frequently over extended times, and employ large study samples. Missing data problems can be expected, and data analytic methods must accommodate missingness. [source]