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PubMed1990

Statistical methods in SUPPORT.

The analysis and interpretation of the data collected in SUPPORT provide great potential for understanding the relationships among treatment choices, patient and physician values and preferences, perceptions about the risks and benefits of treatments, institutional characteristics, and outcomes (as measured by quality of life, survival, and satisfaction). The complicated analyses required to elucidate these relationships will pose many technical challenges in dealing with longitudinal observational data collected from seriously ill patients at multiple sites. Major challenges include the handling of incomplete data, proper parameterization of treatment effects, strategies to avoid various potential biases, validating predictive models, and constructing endpoints that combine survival with quality of life. Within the structure of the SUPPORT study, mechanisms have been established to guide the analyses and to ensure their quality and validity.

Confounding Factors, Epidemiologic↗
PubMed2001

Use and barriers to use of laboratory data by clinical dietitians.

The results of this study reveal two general areas of concern for clinical dietitians in the use of laboratory data--their lack of confidence in their own interpretive skills and the perceived resistance of physicians. Although clinical dietitians receive theoretical and applied training on the use of laboratory values in their undergraduate education and dietetic internship, clinical managers should be aware that they need support and continuing education to use their laboratory assessment skills to the fullest. Also, dietitians should demonstrate more clearly to physicians their knowledge, skill, and needs in this area. They should initiate and continue dialogue about the importance of laboratory analysis to nutrition care and improved patient outcome. Evidence-based data and clear documentation of improved medical outcomes should help overcome barriers to use of laboratory data by clinical dietitians.

Adult↗
PubMed2000

Emergence of item response modeling in instrument development and data analysis.

In summary, readers are encouraged to read carefully the IRT articles in this special issue. They provide theoretical as well as practical arguments in favor of using IRT models in the health outcomes measurement field. At the same time, readers should be aware that the IRT field is complex and software is limited and, in my judgment, not very user friendly (although some packages are better than others). In addition, sample sizes will often need to be larger than in classical measurement, at least with the more general IRT models, and applications are rarely straightforward. Considerable practical experience is needed to ensure successful applications of IRT in the development and validation of instruments for health outcomes measurement.

Data Interpretation, Statistical↗
PubMedDate not supplied

Too much ado about propensity score models? Comparing methods of propensity score matching.

OBJECTIVE: A large number of possible techniques are available when conducting matching procedures, yet coherent guidelines for selecting the most appropriate application do not yet exist. In this article we evaluate several matching techniques and provide a suggested guideline for selecting the best technique. METHODS: The main purpose of a matching procedure is to reduce selection bias by increasing the balance between the treatment and control groups. The following approach, consisting of five quantifiable steps, is proposed to check for balance: 1) Using two sample t-statistics to compare the means of the treatment and control groups for each explanatory variable; 2) Comparing the mean difference as a percentage of the average standard deviations; 3) Comparing percent reduction of bias in the means of the explanatory variables before and after matching; 4) Comparing treatment and control density estimates for the explanatory variables; and 5) Comparing the density estimates of the propensity scores of the control units with those of the treated units. We investigated seven different matching techniques and how they performed with regard to proposed five steps. Moreover, we estimate the average treatment effect with multivariate analysis and compared the results with the estimates of propensity score matching techniques. The Medstat MarketScan Data Base provided data for use in empirical examples of the utility of several matching methods. We conducted nearest neighborhood matching (NNM) analyses in seven ways: replacement, 2 to 1 matching, Mahalanobis matching (MM), MM with caliper, kernel matching, radius matching, and the stratification method. RESULTS: Comparing techniques according to the above criteria revealed that the choice of matching has significant effects on outcomes. Patients with asthma are compared with patients without asthma and cost of illness ranged from 2040 dollars to 4463 dollars depending on the type of matching. After matching, we looked at the insignificant differences or larger P-values in the mean values (criterion 1); low mean differences as a percentage of the average standard deviation (criterion 2); 100% reduction bias in the means of explanatory variables (criterion 3); and insignificant differences when comparing the density estimates of the treatment and control groups (criterion 4 and criterion 5). Mahalanobis matching with caliber yielded the better results according all five criteria (Mean = 4463 dollars, SD = 3252 dollars). We also applied multivariate analysis over the matched sample. This decreased the deviation in cost of illness estimates more than threefold (Mean = 4456 dollars, SD = 996 dollars). CONCLUSION: Sensitivity analysis of the matching techniques is especially important because none of the proposed methods in the literature is a priori superior to the others. The suggested joint consideration of propensity score matching and multivariate analysis offers an approach to assessing the robustness of the estimates.

Confounding Factors, Epidemiologic↗
PubMed2006

Nonparametric statistical methods for cost-effectiveness analyses.

Two measures often used in a cost-effectiveness analysis are the incremental cost-effectiveness ratio (ICER) and the net health benefit (NHB). Inferences on these two quantities are often hindered by highly skewed cost data. In this article, we derive the Edgeworth expansions for the studentized t-statistics for the two measures and show how they could be used to guide inferences. In particular, we use the expansions to study the theoretical performance of existing confidence intervals based on normal theory and to derive new confidence intervals for the ICER and the NHB. We conduct a simulation study to compare our new intervals with several existing methods. The methods evaluated include Taylor's interval, Fieller's interval, the bootstrap percentile interval, and the bootstrap bias-corrected acceleration interval. We found that our new intervals give good coverage accuracy and are narrower compared to the current recommended intervals.

Biometry↗
PubMed2003

Obtaining power or obtaining precision. Delineating methods of sample-size planning.

Sample-size planning historically has been approached from a power analytic perspective in order to have some reasonable probability of correctly rejecting the null hypothesis. Another approach that is not as well-known is one that emphasizes accuracy in parameter estimation (AIPE). From the AIPE perspective, sample size is chosen such that the expected width of a confidence interval will be sufficiently narrow. The rationales of both approaches are delineated and two procedures are given for estimating the sample size from the AIPE perspective for a two-group mean comparison. One method yields the required sample size, such that the expected width of the computed confidence interval will be the value specified. A modification allows for a defined degree of probabilistic assurance that the width of the computed confidence interval will be no larger than specified. The authors emphasize that the correct conceptualization of sample-size planning depends on the research questions and particular goals of the study.

Analysis of Variance↗
PubMed1996

Seven reasons why you should not categorize continuous data.

I have shown that there are a variety of logical, philosophical, measurement, and statistical reasons why every attempt should be made to measure continuous attributes using measures which reflect that continuity and that following statistical analyses should also retain that continuity. I encourage you to look back at some of the research articles which you have read and see if any of these issues is pertinent. Check out the conclusions and see if they are warranted by the data.

Data Interpretation, Statistical↗
PubMed1989

Colorado and the outcomes revolution.

How can we measure this thing called quality? A state agency attacks the problem with a uniform clinical data set and breaks new ground in effectiveness assessment.

Colorado↗
PubMedDate not supplied

The Parent Satisfaction with Foster Care Services Scale.

Client satisfaction measures are an essential component of program evaluation. This article describes the development of a scale for measuring the satisfaction levels of parents whose children have received foster care services. Subjected to various statistical measures, the Parent Satisfaction with Foster Care Services Scale appears to be a reliable instrument with the promise of utility for social work researchers, practitioners, and administrators.

Clinical Competence↗
PubMed2001

Statistical analyses to support forensic interpretation for a new ten-locus STR profiling system.

A new ten-locus STR (short tandem repeat) profiling system was recently introduced into casework by the Forensic Science Service (FSS) and statistical analyses are described here based on data collected using this new system for the three major racial groups of the UK: Caucasian. Afro-Caribbean and Asian (of Indo-Pakistani descent). Allele distributions are compared and the FSS position with regard to routine significance testing of DNA frequency databases is discussed. An investigation of match probability calculations is carried out and the consequent analyses are shown to provide support for proposed changes in how the FSS reports DNA results when very small match probabilities are involved.

Asian People↗
PubMed1982

Applications of a minicomputer to clinical pharmacy services.

The application of a minicomputer system to clinical services and administrative record keeping is described. All applications were designed and implemented by pharmacists. Programs were created to increase the efficiency of collecting workload statistics for clinical services and to perform pharmacokinetic analyses of patient-specific data. The time required to generate quarterly workload reports decreased from 15-18 hours to 20-30 minutes. The use of the minicomputer also permitted a substantial expansion in the scope of the information collected and reported. The pharmacokinetic programs required approximately two minutes to calculate patient-specific peak and trough concentrations. The minicomputer system has increased the pharmacy department's administrative efficiency and encouraged staff pharmacist involvement in clinical services.

Computers↗
PubMed1964

THE GOLDEN BED.

Explore the source record for details and available documents.

Beds↗
PubMed2004

[Annual costs of bipolar disorders in Germany].

With costs of approximately 5.8 billion EUR annually, bipolar disorders represent a substantial burden on German society. The costs are mainly due to high indirect costs caused by morbidity-related unemployment, suicide-related losses of productivity, time off from work, and early retirement. Inpatient care, with a considerable average length of stay for patients with bipolar disorders, accounts for two-thirds of direct costs. This paper refers to statistics on use of healthcare services based primarily on the International Statistical Classification of Diseases, Tenth Revision. Representing a relatively narrow definition of bipolar disorders in comparison with the clinically relevant spectrum, this classification leads to a conservative estimate of the total costs. The significant lag between first acute episode and a correct diagnosis causes a delayed onset of maintenance treatment that leads to increased costs. Increasing public awareness, destigmatizing the disease, and educating physicians are necessary steps to limit the substantial economic burden for society.

Absenteeism↗