Search PubMed⌕ Search

Biomedical subjects

Helena Chmura Kraemer

Publications and source records attributed to Helena Chmura Kraemer.

16 recordsLinked to original sources
PubMed2006

Caution regarding the use of pilot studies to guide power calculations for study proposals.

Clinical researchers often propose (or review committees demand) pilot studies to determine whether a study is worth performing and to guide power calculations. The most likely outcomes are that (1) studies worth performing are aborted and (2) studies that are not aborted are underpowered. There are many excellent reasons for performing pilot studies. The argument herein is not meant to discourage clinical researchers from performing pilot studies (or review committees from requiring them) but simply to caution against their use for the objective of guiding power calculations.

Biomedical Research↗
PubMed2006

Lifetime prevalence and pseudocomorbidity in psychiatric research.

CONTEXT: Comorbidity is the rule rather than the exception with psychiatric disorders and is consequently of great interest to both researchers and clinicians. However, many studies of psychiatric comorbidity have been based on lifetime prevalence with mixed-age samples, a practice that (1) biases the assessment of epidemiologic comorbidity and (2) creates the appearance of comorbidity even when disorders are randomly associated. This bias is what we refer to as pseudocomorbidity. OBJECTIVES: To clarify the source of the problem and to discuss strategies that might be adopted to deal hereafter with lifetime prevalence data. METHODS: A simulated example is presented to show that even when there is only random association between disorders, there will appear to be nonrandom comorbidity when lifetime prevalence is used with mixed-age samples. An actual example relating psychosis to phobia is presented to show the bias that can result and to illustrate one way of dealing with lifetime prevalence data. CONCLUSIONS: Use of lifetime prevalence with mixed-age samples, used almost exclusively in psychiatric research, generates problematic results, especially when used for assessment of comorbidity, and should be viewed with some skepticism. Hereafter, we recommend that any future use of lifetime prevalence should require determination of the age of onset, even if only by retrospective report. Comorbidity then should be reported by age.

Adolescent↗
PubMed2006

Factors associated with psychiatric hospitalization of individuals diagnosed with dementia and comorbid bipolar disorder.

The objective was to determine risk factors of psychiatric hospitalization among a Veterans Administration database of patients with dementia and comorbid bipolar disorder (D+BD). Patients with D+BD had a greater prevalence of psychiatric hospitalization (28% vs 4%). The strongest predictor of psychiatric hospitalization was the presence of an alcohol use disorder (51% risk); patients without alcohol use disorders but under the age of 70 had the next highest risk (33% risk). However, patients with an alcohol use disorder had shorter psychiatric hospitalizations than those without. Compared with patients without BD, D+BD patients were more likely to have alcohol use disorders (15% vs 3%) and any other substance use problem (10% vs 1%). In patients diagnosed with dementia and bipolar disorder, the strongest risk factor for psychiatric hospitalization was an alcohol abuse disorder. These findings suggest that disorders with increased frequency in BD affect the course of dementia.

Age Factors↗
PubMed2005

Size of treatment effects and their importance to clinical research and practice.

In randomized clinical trails (RCTs), effect sizes seen in earlier studies guide both the choice of the effect size that sets the appropriate threshold of clinical significance and the rationale to believe that the true effect size is above that threshold worth pursuing in an RCT. That threshold is used to determine the necessary sample size for the proposed RCT. Once the RCT is done, the data generated are used to estimate the true effect size and its confidence interval. Clinical significance is assessed by comparing the true effect size to the threshold effect size. In subsequent meta-analysis, this effect size is combined with others, ultimately to determine whether treatment (T) is clinically significantly better than control (C). Thus, effect sizes play an important role both in designing RCTs and in interpreting their results; but specifically which effect size? We review the principles of statistical significance, power, and meta-analysis, and commonly used effect sizes. The commonly used effect sizes are limited in conveying clinical significance. We recommend three equivalent effect sizes: number needed to treat, area under the receiver operating characteristic curve comparing T and C responses, and success rate difference, chosen specifically to convey clinical significance.

Area Under Curve↗
PubMed2005

Are certain multicenter randomized clinical trial structures misleading clinical and policy decisions?

Multicenter studies involving randomized clinical trials (RCTs) may have different structures. We discuss four general types. The first two, an "ideal" multicenter RCT and decentralized multicenter collaborative RCTs, we feel are, in different circumstances, highly recommended approaches. The other two, the multicenter RCT that ignores site differences and centralized multicenter collaborative RCTs, we argue, are not only not cost-effective but may also produce misleading results, thus impeding scientific progress and possibly putting patients at unnecessary risk.

Data Interpretation, Statistical↗
PubMed2005

A simple effect size indicator for two-group comparisons? A comment on r equivalent.

R. Rosenthal and D. B. Rubin (2003) proposed an effect size, r equivalent, to be used when (a) only sample size and p values are known for a study, (b) there are no generally accepted effect size indicators, or (c) sample sizes are so small or the data so non-normal that the directly computed effect sizes would be more misleading than the simple effect size. The limitations of their proposal, however, are many, and much more serious than the authors suggested, and should be carefully considered before this effect size is applied, as well as in developing other effect sizes using similar methods.

Data Interpretation, Statistical↗
PubMed2004

Care management for low-risk patients with heart failure: a randomized, controlled trial.

BACKGROUND: Nurse care management programs for patients with chronic illness have been shown to be safe and effective. OBJECTIVE: To determine whether a telephone-mediated nurse care management program for heart failure reduced the rate of rehospitalization for heart failure and for all causes over a 1-year period. DESIGN: Randomized, controlled trial of usual care with nurse management versus usual care alone in patients hospitalized for heart failure from May 1998 through October 2001. SETTING: 5 northern California hospitals in a large health maintenance organization. PATIENTS: Of 2786 patients screened, 462 met clinical criteria for heart failure and were randomly assigned (228 to intervention and 234 to usual care). INTERVENTION: Nurse care management provided structured telephone surveillance and treatment for heart failure and coordination of patients' care with primary care physicians. MEASUREMENTS: Time to first rehospitalization for heart failure or for any cause and time to a combined end point of first rehospitalization, emergency department visit, or death. RESULTS: At 1 year, half of the patients had been rehospitalized at least once and 11% had died. Only one third of rehospitalizations were for heart failure. The rate of first rehospitalization for heart failure was similar in both groups (proportional hazard, 0.85 [95% CI, 0.46 to 1.57]). The rate of all-cause rehospitalization was similar (proportional hazard, 0.98 [CI, 0.76 to 1.27]). LIMITATIONS: The findings of this study, conducted in a single health care system, may not be generalizable to other health care systems. The overall effect of the intervention was minor. CONCLUSIONS: Among patients with heart failure at low risk on the basis of sociodemographic and medical attributes, nurse care management did not statistically significantly reduce rehospitalizations for heart failure or for any cause. Such programs may be less effective for patients at low risk than those at high risk.

Aged↗
PubMed2004

Reconsidering the odds ratio as a measure of 2x2 association in a population.

The odds ratio (OR) is probably the most widely used measure of 2x2 association in epidemiology, but it often produces results that are puzzling or misleading. Receiver operating characteristic (ROC) methods are used to take a fresh look at the OR and show where and why such puzzling results arise. When researchers choose to report a summary measure of association, the OR is one of many measures of association that might be considered, not one that should be considered the 'gold standard' of 2x2 measures of association. In a randomized clinical trial with binary outcome for success, either the success or failure rates in treatment and control groups might be reported separately or the number needed to treat to achieve one extra success, to emphasize the cost of unnecessary treatment needed to achieve a success. In studies assessing reliability or heritability, we recommend the intraclass kappa. In studies in which one binary variable is assessed against a binary criterion, we recommend the weighted kappa.

Female↗
PubMed2004

Categorical versus dimensional approaches to diagnosis: methodological challenges.

The arguments pitting categorical versus dimensional approaches to psychiatric diagnosis have been long ongoing with little sign of imminent resolution. We argue that categorical and dimensional approaches are fundamentally equivalent, but that one or other approach is more appropriate depending on the clinical circumstances and research questions being addressed. This paper aims to demonstrate (a) how these two approaches necessarily interdigitate, (b) to clarify the conditions under which one should utilize one approach over the other, and (c) to alert psychiatric clinicians and researchers to issues in the methodology literature that might facilitate their considerations. Using an example from the Infant Health and Development Program (IHDP), we illustrate the importance of using dimensional approaches for hypothesis testing, identify the problems with power and with interpretation that arise from employing a categorical approach, and underscore the importance of identifying the appropriate cutpoints when a categorical approach is necessitated. We argue that failure to utilize the correct approach under the appropriate circumstances can result in impaired clinical and research decision-making.

Adult↗
PubMed2003

"Rules" of evidence in assessing the efficacy and effectiveness of treatments.

A brief review of the "rules" of evidence in assessing the efficacy and effectiveness of treatments leads to a discussion of what the future might hold in the development of the rules of randomized clinical trials (RCTs) in order to enhance the impact of such studies. The primary requisite for enhancing the impact of studies appears to be discussions and interactions among researchers, clinicians, policy makers, and patient advocates, on the one hand, and among the basic scientists and clinical researchers, on the other.

Clinical Protocols↗
PubMed2002

Kappa coefficients in medical research.

Kappa coefficients are measures of correlation between categorical variables often used as reliability or validity coefficients. We recapitulate development and definitions of the K (categories) by M (ratings) kappas (K x M), discuss what they are well- or ill-designed to do, and summarize where kappas now stand with regard to their application in medical research. The 2 x M(M>/=2) intraclass kappa seems the ideal measure of binary reliability; a 2 x 2 weighted kappa is an excellent choice, though not a unique one, as a validity measure. For both the intraclass and weighted kappas, we address continuing problems with kappas. There are serious problems with using the K x M intraclass (K>2) or the various K x M weighted kappas for K>2 or M>2 in any context, either because they convey incomplete and possibly misleading information, or because other approaches are preferable to their use. We illustrate the use of the recommended kappas with applications in medical research.

Data Interpretation, Statistical↗
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↗
PubMed2002

Interactive influences on BVRT performance level: geriatric considerations.

We examined the interactive influence of specific demographic and diagnostic variables on Benton Visual Retention Test (BVRT) performance in three commonly presenting groups of older adults. Cross-sectional data from three archival samples were utilized: "normals" (n=156), "normals with memory concerns" (n=435), and a "mixed neurologic" group (n=196). In both normal groups, as well as in a "no/low deficit" neurologic subgroup, we confirmed that the higher one's age, the lower their BVRT accuracy, while the higher one's level of education, the greater their BVRT accuracy (at least through age 84). For normal subjects, gender had no impact on BVRT performance. Variability in BVRT performance increased consistently, but not significantly, through age 85.

Journal Article↗
PubMedDate not supplied

On disentangling states versus traits: demonstration of a new technique using the Alzheimer's disease assessment scale.

Part of the challenge in research on degenerative neurologic disease relates to distinguishing those measurements that essentially describe patient characteristics stable across the course of illness (traits) from those that vary systematically within subjects (states), particularly those specifically related to stage or duration of illness. A components-of-variance approach was used to examine the state versus trait aspects of the Alzheimer's Disease Assessment Scale (ADAS) Cognitive and Noncognitive subscales, a clinical instrument frequently used in research on Alzheimer disease. Subjects were 190 patients with probable AD followed longitudinally. Stage of illness was indexed by mental status scores. Analysis of variance was used to partition total variance into that associated with subjects (trait), stages (state: stage), subjects x stages (state: other), and error. ADAS Cognitive scores were strongly related to stage of illness (83% of true variance). ADAS Noncognitive scores were modestly related to stage (approximately 21% of true variance) and moderately related to state: other (47%). We discuss how state-trait analyses can be helpful in focusing attention on those areas of assessment most likely to accomplish specific objectives.

Aged↗
PubMedDate not supplied

Biomarkers in psychiatry: methodological issues.

Particularly in psychiatry, biological measures are increasingly sought to detect exposure to toxic agents, to assist in early identification of illness, and to enhance diagnostic certainty, provide prognostic information, and permit the mapping of outcome in a variety of disorders. The authors explore the fundamental criteria necessary to designate a biological measure as a "biomarker" and discuss the potential applications, limitations, and hazards of such markers. Authors discuss methods for establishing the validity of a biomarker. Finally, they convey a word of caution about overinterpreting the clinical or scientific value of any biological measure.

Biomarkers↗