Imbalance with baselines

When there is evidence to suggest a baseline imbalance with respect to a characteristic that... 

Adjusted analyses presented earlier in this chapter also share some of these advantages and provide improvements in efficiency, can also account for baseline imbalances and allow the evaluation of the homogeneity of the treatment effect. On this final point, however, adjusted analyses are less able to identify the nature of those interactions. With ANCOVA it is possible to say which particular covariates are causing such interactions. A further point to note here and, as mentioned... 

In the final section of this chapter we will say more about baseline imbalances and how to deal with them. 

Obviously, the degree of imbalance observed in a randomized clinical trial will vary randomly. On average the difference at baseline between groups will be zero. This does not imply, however, that the average value of A 2 will be 1. A2 is, in fact related to familiar quantities from the analysis of variance the numerator of the second term is simply the between sum of squares and the denominator is the within sum of squares for a one-way layout with n observations in each group. Since the sum of squares between has one degree of freedom, it is equal to the mean square between, MSB, whereas the within sums of squares has 2(n — 1) degrees of freedom and hence is 2(n — 1) MSW, where MSW is the mean square within. Hence,... 

Of course, in a clinical trial we would never see imbalances like this at baseline and would therefore very rarely expect to be faced with Simpson s paradox. Its importance is far greater in an epidemiological context. For example, we might suspect when a new drug is launched on the market that patients with the most refractory disease might be more likely to take it. In the example there were 600 severe patients who took A and only 60 who took B, the numbers being reversed for moderate disease, so this might be a possible pattern if A were a newer treatment. Disease severity would then be a confounder for which it was appropriate to adjust. 

Senn SJ (1989) Covariate imbalance and random allocation in clinical trials [see comments]. Statistics in Medicine 8 467-475. [Comment in Statistics in Medicine 10(5) 797-799 (1991)]. Senn SJ (1994) Testing for baseline balance in clinical trials. Statistics in Medicine 13 1715-1726. Senn SJ (2000) Consensus and controversy in pharmaceutical statistics (with discussion). The Statistician 49 135-176. 

An ideal DSC curve showing the change of heat flow process is in Fig. 6.42. Exothermic and endothermic peaks are marked as EX and EN. Due to the imbalance in the thermal capacities of the sample crucible, its contents etc., offset 0 is observed. The base line of the curve (B) is decided by the heat capacity of the sample. For a precise measurement, the baseline corrections can be made by comparing the empty and with the sample loaded pan data. 

Fluids and Electrolytes Demystified is a detailed overview of the critical concepts involved in fluid, electrolyte, and acid-base balance and imbalance, collectively one of the hardest topics to master in undergraduate nursing. Fluid and electrolyte balance and acid-base balance have challenged students for ages. Some of the difficulty in understanding this content may lie in the attempt to remember each individual detail or symptom associated with an imbalance combined with the difficulty of mastering the physiology involved in each process. The list of symptoms of imbalance can be extensive there is often duplication and overlap between electrolyte and acid-base imbalances. In Fluids and Electrolytes Demystified, the normal functions are discussed to provide baseline data. The concepts of imbalance are presented individually, but the links between concepts are addressed. The reader is led toward two facts that many of the fluid, electrolyte, and acid-base imbalance symptoms are interrelated and that the imbalances themselves are interlinked. 

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