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Results for “Effect Modifier, Epidemiologic”

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At least 145 records · Page 8Linked to original sources
PubMed1996

Meta-analysis of the sensitivity and specificity of platform posturography.

OBJECTIVE: To compare the sensitivity and specificity of platform posturography with other vestibular tests for patients with peripheral vestibular deficits (PVD), Meniere's disease, benign paroxysmal positional vertigo (BPPV), and central nervous system-vestibular impairment (CNS). DATA SOURCES: A computed search was conducted using the Index Medicus database (1966-1994) and Current Contents Science Editions. STUDY SELECTION: Studies were selected for analysis if the article addressed the sensitivity and/or specificity of platform posturography, compared posturography with another objective test of vestibular function, identified the basis for abnormal test results, and reported the data with sufficient detail to calculate an effect size from a 2 x 2 contingency table. DATA EXTRACTION: A count of the normal and abnormal test results for posturography and the criterion standard were retrieved from each article, analyzed using a chi 2 statistic, and converted to an effect size. A positive effect size indicated that posturography identified abnormalities in patients who had normal tests on the criterion standard. DATA SYNTHESIS: Sensitivity and specificity of posturography were about 50%. The overall effect size was small (0.13) but positive. The diagnostic category had a significant influence on the predictive value of abnormal results (73% for Meniere's disease and BPPV, compared with 41% for PVD, and 44% for mixed CNS and PVD (F2,12 = 5.26, P = .02) and on the magnitude of the effect size (0.41 for mixed CNS and PVD compared with 0.22 for Meniere's disease and BPPV, and -0.10 for PVD (F2,12 = 13.95, P = .001). CONCLUSIONS: Platform posturography provides a measurable supplement to the standard vestibular examination. The enhancement was most notable when the target population included patients with CNS deficits.

Abstracting and Indexing↗
PubMed1992

How much 'better' is good enough? The magnitude of treatment effect in clinical trials.

OBJECTIVES: Among the various factors required to calculate sample size for clinical trials, the magnitude of treatment effect anticipated is an important component. The objective of this report is to present some of the complexities involved in selection of treatment effect size in clinical trials. As a framework for discussion, an analysis of published reports related to surfactant therapy was carried out. DESIGN: Twenty-one consecutive exogenous surfactant trials for neonatal respiratory distress syndrome were analyzed. The "Methods" sections were reviewed for evaluating various components of sample size calculation, including the anticipated treatment effect size. RESULTS: Sixteen (76%) of the 21 reports provided a description of sample size calculations, and 12 of these gave some reasons for the choice of the anticipated treatment effect size. Expressed as percent change, the median treatment effect from intervention anticipated by the investigators was 50% (range, 15% to 90%), with a positively skewed distribution. The actual median percent reduction in adverse events from treatment (as compared with baseline) was 36% (range, 75% reduction to 5% excess). When the treatment effect was expressed as difference in adverse event rate, in the 14 (of 16) trials that could be analyzed, the median observed reduction in adverse events (death, bronchopulmonary dysplasia, or occurrence of respiratory distress syndrome) was 14.5% (range, 52% reduction to 2% excess). All trials except one concluded, however, that the intervention was effective, mostly based on additional subgroup calculations. CONCLUSIONS: Researchers often select sample sizes capable of detecting only large treatment effects, thus risking type II error, although sometimes a much smaller effect could be clinically important. While pragmatic considerations must be considered during the design of randomized clinical trials, researchers ought to present a rationale for anticipating a given magnitude of treatment effect in their sample size calculations. It may be possible to consider innovative trial designs that help determine the most appropriate treatment choice with the least possible sample size.

Confidence Intervals↗
PubMed2002

Mediators and moderators of treatment effects in randomized clinical trials.

Randomized clinical trials (RCTs) not only are the gold standard for evaluating the efficacy and effectiveness of psychiatric treatments but also can be valuable in revealing moderators and mediators of therapeutic change. Conceptually, moderators identify on whom and under what circumstances treatments have different effects. Mediators identify why and how treatments have effects. We describe an analytic framework to identify and distinguish between moderators and mediators in RCTs when outcomes are measured dimensionally. Rapid progress in identifying the most effective treatments and understanding on whom treatments work and do not work and why treatments work or do not work depends on efforts to identify moderators and mediators of treatment outcome. We recommend that RCTs routinely include and report such analyses.

Clinical Protocols↗
PubMed1996

Practical p-value adjustment for optimally selected cutpoints.

This paper concerns a series of simulations undertaken to examine the effects of two data features--number of cutpoints and true marker prognostic effect size--on three methods of p-value adjustment (asymptotic, P(acor); improved Bonferroni, P(bon); and empirical permutation, P(emp)). H(o) rejection rates for P(emp) and P(bon) are almost indistinguishable from those for an independent validation sample (P(vld)), while those of P(acor) are somewhat conservative, especially when the number of cutpoints is small. Analysis of a new breast cancer prognostic marker, heat shock protein 70, illustrates the methods. These results underscore many of the problems associated with data-derived cutpoints in general, and the need for p-value adjustment.

Bias↗
PubMed1996

Polynomials with asymptotes for longitudinal data.

I use Laguerre polynomials to model growth curves or time--response curves known to approach an asymptote as time approaches infinity. An example is with measurements on a variable or variables from subjects recovering from surgery. These variables can often vary in a non-monotonic fashion for which a functional form of the curve is unknown. Using a longitudinal data mixed model, one can include in the model random subject effects, within-subject serial correlation and fixed or time varying covariates. I present two examples that involve groups of subjects recovering from surgery.

Anterior Cruciate Ligament↗
PubMed1996

Statistical analysis of zidovudine (AZT) effect on CD4 cell counts in HIV disease.

We fit a class of random effects linear growth curve models for the square root of CD4 count to serial marker data from 164 HIV-positive individuals with known (or accurately estimated) dates of seroconversion and at least 10 CD4 measurements each (median 16). We do so by adopting a Bayesian viewpoint and using the Markov chain Monte Carlo technique Gibbs sampling. In particular, we examine the effect of the antiretroviral treatment zidovudine on the square root of CD4 series for the 136 patients who took the drug. Treatment effects are modelled by positing recoveries in square root of CD4 level proportional to current immuno-competence and changes in slope proportional to current rate of square root of CD4 loss. Both fixed and random treatment effects are considered and models are criticized and compared using Bayesian predictive methodology and checking data which comprise 424 new observations. Results indicate re-elevation of square root of CD4 level is associated with treatment but the effect, though significant, is mostly of small magnitude and is possibly transient; models neglecting consideration of treatment fit the checking data almost as well. Best overall model estimates mean rate of square root of CD4 loss per annum to be 2.1 (standard error 0.12); mean seroconversion value of square root of CD4 is 28.4 (SE 0.65). The estimated variance of individual slopes is 1.9 (SE 0.28), there being considerable individual variation in rate of CD4 loss, and a recovery in level of 0.047 (SE 0.014) times current square root of CD4 level is estimated at treatment uptake.

Antiviral Agents↗
PubMed1996

The analysis of incomplete data in the three-period two-treatment cross-over design for clinical trials.

The additional time to complete a three-period two-treatment (3P2T) cross-over trial may cause a greater number of patient dropouts than with a two-period trial. This paper develops maximum likelihood (ML), single imputation and multiple imputation missing data analysis methods for the 3P2T cross-over designs. We use a simulation study to compare and contrast these methods with one another and with the benchmark method of missing data analysis for cross-over trials, the complete case (CC) method. Data patterns examined include those where the missingness differs between the drug types and depends on the unobserved data. Depending on the missing data mechanism and the rate of missingness of the data, one can realize substantial improvements in information recovery by using data from the partially completed patients. We recommend these approaches for the 3P2T cross-over designs.

Analysis of Variance↗
PubMed1996

Meta-analysis: current issues in research synthesis.

Recent concern about the effectiveness of alternative treatments in medicine and health, in education, in psychology, and in the social sciences has led to a consideration of how to combine or synthesize the results of independent studies. Historically, integration of independent results focused on how to combine p-values. More recently the emphasis has been on estimating effect sizes, which in turn has motivated a variety of alternative models depending on the experimental conditions. We here review the development of the field with an emphasis of diagnostics and future research.

Bias↗
PubMed1996

The semi-proportional hazards model revisited: practical reparametrizations.

Reformulations of the semi-proportional hazards model are outlined making estimation and testing of stratum-covariate interaction effects easily accessible within the framework of the stratified Cox proportional hazards model. The method is illustrated by a practical analysis of variables influencing bronchial responsiveness with data from the Hordaland study of obstructive lung disease.

Adult↗
PubMed1997

Power of logrank test and Cox regression model in clinical trials with heterogeneous samples.

This paper evaluates the loss of power of the simple and stratified logrank tests due to heterogeneity of patients in clinical trials and proposes a flexible and efficient method of estimating treatment effects adjusting for prognostic factors. The results of the paper are based on the analyses of survival data from a large clinical trial which includes more than 6000 cancer patients. Major findings from the simulation study on power are: (i) for a heterogeneous sample, such as advanced cancer patients, a simple logrank test can yield misleading results and should not be used; (ii) the stratified logrank test may suffer some power loss when many prognostic factors need to be considered and the number of patients within stratum is small. To address the problems due to heterogeneity, the Cox regression method with a special hazard model is recommended. We illustrate the method using data from a gastric cancer clinical trial.

Bias↗
PubMed1997

Incidence of HIV-related deaths in the United States: seasonality and trend.

This paper examines possible short-term patterns and distortions in the incidence of deaths with AIDS in the United States, using methods previously applied to incidence of AIDS diagnoses. The variation in death counts by calendar month models fairly well as a seasonal pattern consistent with seasonal variation in deaths from all causes. In addition, three apparently non-biological short-term effects in AIDS incidence -a workday effect, a jump between December and January, and a spike in June-are not apparent in death incidence, and analysis of death counts in subgroups does not show any strong evidence for non-biological influences on time of death. Deseasonalized death incidence shows a steady increase over time. Because death incidence is not subject to definition change and is apparently less susceptible to other non-biological influences than AIDS incidence, it may have value for monitoring the HIV epidemic.

Adolescent↗
PubMed1997

Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis.

When combining results from separate investigations in a meta-analysis, random effects methods enable the modelling of differences between studies by incorporating a heterogeneity parameter tau 2 that accounts explicitly for across-study variation. We develop a simple form for the variance of Cochran's homogeneity statistic Q, leading to interval estimation of tau 2 utilizing an approximating distribution for Q; this enables us to extend the point estimation of DerSimonian and Laird. We also develop asymptotic likelihood methods and compared them with this method. We then use these approximating distributions to give a new method of calculating the weight given to the individual studies' results when estimating the overall mean which takes into account variation in these point estimates of tau 2. Two examples illustrate the methods presented, where we show that the new weighting scheme is between the standard fixed and random effects models in down-weighting the results of large studies and up-weighting those of small studies.

Effect Modifier, Epidemiologic↗
PubMed1997

Effect size and power for clinical trials that measure years of healthy life.

Some clinical trials perform repeated measurements on patients over time, plot those measures against time, and summarize the results in terms of the area under the curve. If the measured variable is health status, the summary outcome is sometimes referred to as years of healthy life (YHL), or quality-adjusted life years (QALY). This paper investigates some theoretical and practical aspects of randomized trials designed to assess measures such as YHL. We first derived algebraic expressions for the effect size of YHL measures under several theoretical models of the treatment's effect on health. We used these expressions to examine how the length of the study, the number of measurements per person and the correlations among health measurements over time influence the effect size. We also explored the relative statistical power of analyses based on YHL versus analyses based on change-scores using the same data. We present an example. Findings suggest that: (i) the number of measurements per person need not be large; (ii) high correlation among measures over time tends to lower the power of a study using YHL; (iii) a longer study will not always provide more power than a shorter study, and (iv) analyses based on YHL may have less power than change-score analyses. Some of these findings depend on the model of change in health status caused by the treatment. Such models require further study.

Effect Modifier, Epidemiologic↗
PubMed1997

A test of missing completely at random for longitudinal data with missing observations.

Liang and Zeger proposed a generalized estimating equations approach to the analysis of longitudinal data. Their models assume that missing observations are missing completely at random in the sense of Rubin. However, when this assumption does not hold, their analysis may yield biased results. In this paper, we develop a simple and practical procedure for testing this assumption. The proposed procedure is related to that of Park and Davis.

Aged↗
PubMed1997

Hierarchical polytomous regression models with applications to health services research.

The analysis of variations is an important area of interest in health services and outcomes research and has two main goals: to identify and quantify variability across units, such as geographic regions or health care providers, in terms of procedure utilization and outcomes, and to explore the links between process, such as regional or hospital practice patterns, and outcomes, such as patient mortality and functional status. Hierarchical regression models are well suited for this type of analysis. In this paper we formulate a hierarchical polytomous regression model and apply it to the analysis of variations in the utilization of alternative cardiac procedures in a national cohort of elderly Medicare patients who had an acute myocardial infarction during 1987. The model is designed to accommodate clustered multinomial data with covariate vectors available on individual cases and on clusters. We present a Bayesian approach to fitting and checking the model using simulated values from the posterior distribution of the parameters. The simulation algorithms are based on Gibbs sampling in combination with Metropolis steps. Using the hierarchical polytomous regression model, we examine how the rates of cardiac procedures depend on patient-level characteristics, including age, gender and race, and whether there exist interstate differences and regional patterns in the use of these procedures.

Aged↗
PubMed1997

An imputation method for non-ignorable missing data in studies of blood pressure.

In studies with repeated measures of blood pressure (BP), particularly in trials of hypertension prevention, BP measurements often become censored once a participant commences antihypertensive medication. When prescribed by non-study physicians under uncontrolled conditions, the missing data mechanism is non-ignorable and may bias the BP effects of interest. I propose a method that models the distribution of BPs measured by non-study physicians and their relation to study BPs using random effects models. If treated for hypertension, I assume that BP measured outside the study is greater than a clinical cutpoint, such as diastolic BP > or = 90 mmHg. I then compute estimates for the missing study BPs conditional on previously observed study BPs and treatment for hypertension. Multiple imputation is used to model the variability of the BP values and adjust the standard error estimates of the parameters. Examples are given using simulated data and data from the weight loss intervention of phase I of the Trials of Hypertension Prevention.

Antihypertensive Agents↗