Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Non B-DNA structures and genomic instability associated with myotonic dystrophy type 2 (CCTG).(CAGG) repeats

There are many diseases associated with the expansion of DNA repeats in humans. Myotonic dystrophy type 2 is one of such diseases, characterized by expansions of a (CCTG)•(CAGG) repeat tract in intron 1 of zinc finger protein 9 (ZNF9) in chromosome 3q21.3. The DM2 repeat tract contains a flanking region 5' to the tract that consists of a polymorphic repetitive sequence (TG)14-25(TCTG)4-11(CCTG) n. The (CCTG)•(CAGG) repeat is typically 11-26 repeats in persons without the disease, but can expand up to 11,000 repeats in affected individuals, which is the largest expansion seen in DNA repeat diseases to date. This DNA tract remains one of the least characterized disease-associated DNA repeats, and mechanisms causing the repeat expansion in humans have yet to be elucidated. Alternative, non B-DNA structures formed by the expanded repeats are typical in DNA repeat expansion diseases. These sequences may promote instability of the repeat tracts. I determined that slipped strand structure formation occurs for (CCTG)•(CAGG) repeats at a length of 42 or more. In addition, Z-DNA structure forms in the flanking human sequence adjacent to the (CCTG)•(CAGG) repeat tract. I have also performed genetic assays in E. coli cells and results indicate that the (CCTG)•(CAGG) repeats are more similar to the highly unstable (CTG)•(CAG) repeat tracts seen in Huntington's disease and myotonic dystrophy type 1, than to those of the more stable (ATTCT)•(AGAAT) repeat tracts of spinocerebellar ataxia type 10. This instability, however, is RecA-independent in the (CCTG)•(CAGG) and (ATTCT)•(AGAAT) repeats, whereas the instability is RecA-dependent in the (CTG)•(CAG) repeats. Structural studies of the (CCTG)•(CAGG) repeat tract and the flanking sequence, as well as genetic selection assays may reveal the mechanisms responsible for the repeat instability in E. coli, and this may lead to a better understanding of the mechanisms contributing to the human disease state. ^

COMPARISON OF THE ORGANIZATIONAL STRUCTURES AND EFFECTIVENESS OF THE INVESTOR-OWNED AND VOLUNTARY MULTIHOSPITAL SECTORS

Objectives. The central objective of this study was to systematically examine the internal structure of multihospital systems, determining the management principles used and the performance levels achieved in medical care and administrative areas.^ The Universe. The study universe consisted of short-term general American hospitals owned and operated by multihospital corporations. Corporations compared were the investor-owned (for-profit) and the voluntary multihospital systems. The individual hospital was the unit of analysis for the study.^ Theoretical Considerations. The contingency theory, using selected aspects of the classical and human relations schools of thought, seemed well suited to describe multihospital organization and was used in this research.^ The Study Hypotheses. The main null hypotheses generated were that there are no significant differences between the voluntary and the investor-owned multihospital sectors in their (1) hospital structures and (2) patient care and administrative performance levels.^ The Sample. A stratified random sample of 212 hospitals owned by multihospital systems was selected to equally represent the two study sectors. Of the sampled hospitals approached, 90.1% responded.^ The Analysis. Sixteen scales were constructed in conjunction with 16 structural variables developed from the major questions and sub-items of the questionnaire. This was followed by analysis of an additional 7 structural and 24 effectiveness (performance) measures, using frequency distributions. Finally, summary statistics and statistical testing for each variable and sub-items were completed and recorded in 38 tables.^ Study Findings. While it has been argued that there are great differences between the two sectors, this study found that with a few exceptions the null hypotheses of no difference in organizational and operational characteristics of non-profit and for-profit hospitals was accepted. However, there were several significant differences found in the structural variables: functional specialization, and autonomy were significantly higher in the voluntary sector. Only centralization was significantly different in the investor owned. Among the effectiveness measures, occupancy rate, cost of data processing, total manhours worked, F.T.E. ratios, and personnel per occupied bed were significantly higher in the voluntary sector. The findings indicated that both voluntary and for-profit systems were converging toward a common hierarchical corporate management approach. Factors of size and management style may be better descriptors to characterize a specific multihospital group than its profit or nonprofit status. (Abstract shortened with permission of author.) ^