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PubMed1982

The application of variance analysis to the study of biological rhythms of known periods.

This paper describes a statistical technique which permits study of, for example, complex biological rhythms with a known fundamental period. This technique uses the principles of variance analysis. The main calculation phases are: the regression calculation on a periodic time function; the study (by variance analysis) of the validity of the mathematical model; the study of the parallelism between subjects; and, finally, the study of the significance of each harmonic component. The calculation is completed by determining confidence intervals: those of the harmonic function parameters, followed by those of the function extremes.

Analysis of Variance↗
PubMed2001

Multiple-point variance analysis for optimal adjustment of a monitoring network.

The selection of sampling sites is one of the major tasks in the design of a monitoring network. Many environmental networks suffer from either insufficient information or redundant information. This study presents a new, effective algorithm that addresses the issues of insufficient and reduction information. The new algorithm is denoted as Multiple-Point Variance Analysis (MPV). MPV includes both Multiple-Point Variance Reduction Analysis (MPVR) for adding information-effectives sites, and Multiple-Point Variance Increase Analysis (MPVI) for deleting information-redundant sites. The MPVR and MPVI equations are verified under two hypothetical cases. The optimal procedures of this new algorithm include determination of simultaneous additions or deletions of groups of sampling points. These proposed optimization procedures eliminate the need for any spatial discretizations or sequential selections. The efficiency of these optimal procedures is tested under actual field conditions. The results show that the optimal MPV is an effective tool for adjustment of existing sampling networks.

Algorithms↗
PubMed1991

Variance analysis. Part II, The use of computers.

This is the second in a two-part series on variance analysis. In the first article (JONA, July/August 1991), the author discussed flexible budgeting, including the calculation of price, quantity, volume, and acuity variances. In this second article, the author focuses on the use of computers by nurse managers to aid in the process of calculating, understanding, and justifying variances.

Accounting↗
PubMed1979

Holy Cross Hospital, Chicago: cost variance analysis in a health care setting.

By knowing and understanding basic cost accounting techniques, administrators are better equipped to keenly judge their own institution's health care expenses. Managers who implement cost variance analysis are less likely to be forced into a defensive position with either board members or the general public.

Accounting↗
PubMed1983

Uses and abuses of analysis of variance.

Analysis of variance is a term often quoted to explain the analysis of data in experiments and clinical trials. The relevance of its methodology to clinical trials is shown and an explanation of the principles of the technique is given. The assumptions necessary are examined and the problems caused by their violation are discussed. The dangers of misuse are given with some suggestions for alternative approaches.

Analysis of Variance↗
PubMed1997

Cost-variance analysis by DRGs; a technique for clinical budget analysis.

In this article it is shown how a cost accounting system based on DRGs can be valuable in determining changes in clinical practice and explaining alterations in expenditure patterns from one period to another. A cost-variance analysis is performed using data from the orthopedic department from the fiscal years 1993 and 1994. Differences between predicted and observed cost for medical care, such as diagnostic procedures, therapeutic procedures and nursing care are analyzed into different components: changes in patient volume, case-mix differences, changes in resource use and variations in cost per procedure. Using a DRG cost accounting system proved to be a useful technique for clinical budget analysis. Results may stimulate discussions between hospital managers and medical professionals to explain cost variations integrating medical and economic aspects of clinical health care.

Accounting↗
PubMed2004

Using variance analysis to detect hazards in a bar-code-assisted medication preparation process.

BACKGROUND: Medication errors have received significant attention, with studies pinpointing problems in the physician ordering, pharmacy dispensing, and nurse administering processes. Yet, the nursing process for preparing medications, which typically occurs in a medication room on the unit, has not received much attention. This process is deceptively complex, and without proper design, it could break down at numerous points. HUMAN FACTORS ENGINEERING ANALYSIS: Prospective hazard analysis methods allow the detection of potential hazards during the planning, assessment, and design phases of a process or technology. A specific technique-variance analysis-is used within one type of prospective hazard analysis, the sociotechnical systems analysis (STSA). STSA provides guidance to (1) analyze existing or planned systems to understand the social, technical, and environmental system components; (2) collect and analyze the system data; and (3) use the analysis to design or redesign the system. DISCUSSION: The STSA variance analysis is an additional tool that health care clinicians, administrators, and risk managers can use to proactively identify hazards for control. Although this larger analysis is more time consuming, it forces the analysts to conduct a true systems analysis before implementing technical, social, environmental, or organizational changes.

Analysis of Variance↗
PubMed1984

Variance analysis. Using standards to predict nurse staffing patterns.

The use of standards to predict required nurse staffing patterns has received attention in recent years because of pressures for cost containment, revenue limitations and the increased availability of data. The establishment of a standard hour system can lead to a comparison of actual hours to the standard hours predicted and adjusted for case mix and changes in volume. The authors previously developed nurse staffing prediction models based on information from a 220-bed short-term hospital. Further study in terms of variance analysis (standard hours to actual hours) is explored in this article.

Analysis of Variance↗
PubMed1999

Variance analysis of current fluctuations of adenosine- and baclofen-activated GIRK channels in dissociated neocortical pyramidal cells.

Whole cell recordings were obtained from pyramidal cell somata acutely isolated from rat neocortex. In voltage-clamp mode, adenosine (0.3-1000 microM), and the GABA(B) receptor agonist, baclofen (1-300 microM), induced K+ current responses mediated by G protein-activated inwardly rectifying K+ (GIRK) channels. In our preparation, adenosine activated GIRK currents with an average EC(50) of 2 microM. Baclofen had an average EC50 of 26 microM. To estimate and compare unitary conductance and density of GIRK channels activated by either adenosine or baclofen, we performed variance analysis of current fluctuations associated with the application of the two agonists at increasing concentrations. Irrespective of the agonist tested, GIRK channels displayed an average single-channel conductance of 25 pS at our recording conditions ([K+]o: 60 mM). Assuming that GIRK channel conductance increases in proportion to the square root of [K+]o, this would translate into 5-6 pS at physiological ion gradients. GIRK channels activated by adenosine or baclofen were not only identical in terms of unitary conductance, they also displayed the same average density of 0.5 channels micron(-2) for both agonists. Our data strongly suggest that the two compounds recruit the same type of channel and thus most likely share a common transduction and effector system.

Adenosine↗
PubMed2003

Managing risk and expected financial return from selective expansion of operating room capacity: mean-variance analysis of a hospital's portfolio of surgeons.

UNLABELLED: Surgeons using the same amount of operating room (OR) time differ in their achieved hospital contribution margins (revenue minus variable costs) by >1000%. Thus, to improve the financial return from perioperative facilities, OR strategic decisions should selectively focus additional OR capacity and capital purchasing on a few surgeons or subspecialties. These decisions use estimates of each surgeon's and/or subspecialty's contribution margin per OR hour. The estimates are subject to uncertainty (e.g., from outliers). We account for the uncertainties by using mean-variance portfolio analysis (i.e., quadratic programming). This method characterizes the problem of selectively expanding OR capacity based on the expected financial return and risk of different portfolios of surgeons. The assessment reveals whether the choices, of which surgeons have their OR capacity expanded, are sensitive to the uncertainties in the surgeons' contribution margins per OR hour. Thus, mean-variance analysis reduces the chance of making strategic decisions based on spurious information. We also assess the financial benefit of using mean-variance portfolio analysis when the planned expansion of OR capacity is well diversified over at least several surgeons or subspecialties. Our results show that, in such circumstances, there may be little benefit from further changing the portfolio to reduce its financial risk. IMPLICATIONS: Surgeon and subspecialty specific hospital financial data are uncertain, a fact that should be taken into account when making decisions about expanding operating room capacity. We show that mean-variance portfolio analysis can incorporate this uncertainty, thereby guiding operating room management decision-making and reducing the chance of a strategic decision being made based on spurious information.

Analysis of Variance↗
PubMed1982

Heritabilities of ego strength (factor C), super ego strength (factor G), and self-sentiment (factor Q3) by multiple abstract variance analysis.

Tested over 3,000 boys (identical and fraternal twins, ordinary sibs, general population) aged 12-18 on Ego Strength, Super Ego Strength, and Self Sentiment. The Multiple Abstract Variance Analysis (MAVA) method was used to obtain estimates of abstract (hereditary, environmental) variances and covariances that contribute to total variation in the three traits. Within-family heritabilities for these traits were about .30, .05, and .65. Between-family heritabilities were .60, .08, and .45. Within-family correlations of genetic and environmental deviations were trivial, unusually so among personality variables, but between-family values showed the usual high negative values, consistent with the law of coercion to the biosocial mean.

Adolescent↗
PubMed2006

Empirical considerations regarding the use of ensemble-variance analysis of macroscopic currents.

Two fundamental properties of an ion channel are its single channel current (i) and its open probability (Po), which are most directly defined by single channel recordings. However, under some circumstances either practical limitations (e.g., extreme voltages) or unique channel properties may preclude the use of single channel recordings for definition of such properties. As an alternative, the variance in macroscopic currents (non-stationary noise analysis or variance-mean analysis) has been exploited to define fundamental elementary properties of the underlying channels. Although some limitations of the variance-mean approach have been considered in previous work by others, here simulation methods were used to define the conditions under which variance analysis can be suitable for providing estimates of i and Po. Of particular interest is the extent to which reasonably reliable estimates of Po can be obtained, even under conditions of Po less than 0.5. Empirically, the analysis indicates that, with sufficient numbers of sweeps and with constraints on initial estimates of i, reasonably reliable estimates of Po can be made down to 0.2. The impact of a number of other factors on the utility of variance-mean analysis are also considered, including effects of inactivation, filtering, and the consequences of particular gating schemes.

Animals↗