Pediatric Clinics of North America - CIPERJ

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DECISION ANALYSIS IN PEDIATRIC HEMATOLOGY 301 hypothetical patients in the decision analysis model or at least the populations included in the sensitivity analysis and must consider whether or not the patient utility values used in a model are similar to the health preferences and values held by their own patient populations. Readers of a decision analysis study need to be aware of the limitations inherent in this type of research [37]. The complexity of any disease process in real life is higher than can be expressed in a decision analysis model. More complex and sophisticated models are being developed, but the more complex the model, the more data are required. The lack of data for clinical probabilities, long-term effects, and patient utilities is a major limitation to model building, particularly in pediatrics. Finally, most models are built from only one perspective, for example, a patient, third-party payer, or heath policy maker. Finding the ‘‘true’’ optimal clinical strategy often may require taking all of these perspectives under consideration. Summary Decision analysis is the application of explicit, quantitative methods to analyze decisions under conditions of uncertainty [34]. This method is a valuable tool for addressing clinical dilemmas in pediatric hematology for which traditional clinical studies may be impractical or even impossible. Decision analysis also can help inform the design of future clinical research by identifying key probability values in the sensitivity analysis that affect model results strongly. Models that are extremely sensitive to patient utility values emphasize the need for increased quality-of-life research in a particular disease state. As with any research method, there are strengths and weaknesses associated with decision analysis. The more accurate and unbiased the estimates of probabilities and outcomes that go into the model, the stronger the desired results. With the current emphasis on costs in the health care system, pediatric hematologists need to be aware of the methodology of economic evaluations, such as cost-effectiveness and cost-utility analyses, and be able to interpret the results of these studies. Several excellent review articles and textbooks have been published on the topic of decision analysis [5,9,10,14,18,22–24,34]. Many universities offer introductory courses in cost-effectiveness and decision analysis through their master’s degree programs in public health, health administration, or clinical research. The Society for Medical Decision Making and the International Society for Pharmacoeconomics and Outcomes Research offer introductory short courses coinciding with their annual meetings. Recognizing that many beginning decision analysis investigators may not have a mentor at their own institution, the Society for Medical Decision Making has started a yearlong mentorship program for junior investigators. This program starts with a one-on-one meeting with a senior investigator during the annual meeting. Finally, decision analysis software companies may offer introductory courses for new users. Tree Age Software offers a 1-day training course
302 O’BRIEN several times a year focused on new investigators in the health care field [38]. All these resources provide excellent opportunities for interested readers to learn more about decision analysis and receive introductory training in this increasingly important research technique. Glossary of terms Base-case Analysis: a model analysis that uses the best estimate for each variable in the model, uses the estimates that the investigator believes are closest to the actual state of affairs Clinical Practice Guidelines: a group process used to generate clinical recommendations; techniques include informal peer committees, nominal group techniques, the Delphi method, and expert or nonexpert consensus conferences Cost-effectiveness Analysis: costs are related to a single, common effect that may differ in magnitude between the clinical strategies being compared (i.e. cost per DVT averted, cost per life saved) Cost-utility Analysis: allows for quality of life adjustments to the clinical outcomes, strategies are compared using the outcome of quality-adjusted life-year (QALY) Decision Analysis: the application of explicit, quantitative methods to analyze decision making under conditions of uncertainty Folding or Rolling Back: the process of analyzing a decision tree, comparing the overall benefits expected from choosing each strategy Markov Model: a type of decision analysis ideal for modeling clinical problems with ongoing risks, the patient transitions between a finite number of health states referred to as Markov states Meta-analysis: the statistical aspects of a systematic review, includes calculating summary effect estimates and variance, statistical tests of heterogeneity and statistical estimates of publication bias Quality-Adjusted Life-Year (QALY): the number of years spent in a particular health state multiplied by the utility of that health state Robust model: a decision analysis model that is insensitive to variation of most parameters during the sensitivity analysis Sensitivity Analysis: the process of repeatedly folding back a decision tree using different values for probability, cost, and utility variables. In one-way sensitivity analysis, each model input is varied one at a time. Probabilistic sensitivity analysis allows for simultaneous variation of multiple parameters. Systematic Reviews: a well-defined and uniform approach to identifying all relevant studies addressing the same research question, displaying the results of eligible studies, and if appropriate, calculating summary estimates Utility: a person’s preference for a particular health state ranging from 0 (death) to 1 (perfect health); measured using a quality of life instrument, direct ratings, or choice-based valuation technique (standard gamble, time trade-off)
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CARE OF PATIENTS WITH VASCULAR ANOM
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358 RODRIGUEZ & HOOTS Fig. 1. X-lin
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Table 1 Clotting factor concentrate
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Table 1 (continued ) Factor VIII (o
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364 RODRIGUEZ & HOOTS was administe
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366 RODRIGUEZ & HOOTS Antifibrinoly
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368 RODRIGUEZ & HOOTS infections an
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370 RODRIGUEZ & HOOTS Fig. 3. Schem
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372 RODRIGUEZ & HOOTS protein is no
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374 RODRIGUEZ & HOOTS [2] Berntorp
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376 RODRIGUEZ & HOOTS [42] Carcao M
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378 ROBERTSON et al von Willebrand
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380 ROBERTSON et al Fig. 2. A propo
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382 ROBERTSON et al a heterogenous
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384 ROBERTSON et al of considering
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386 ROBERTSON et al Type 2M Type 2M
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388 ROBERTSON et al Desmopressin is
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390 ROBERTSON et al References [1]
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392 ROBERTSON et al [46] Nesbitt IM
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394 BLANCHETTE & BOLTON-MAGGS A key
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396 BLANCHETTE & BOLTON-MAGGS recep
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400 BLANCHETTE & BOLTON-MAGGS Fig.
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404 BLANCHETTE & BOLTON-MAGGS the s
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410 BLANCHETTE & BOLTON-MAGGS splen
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414 BLANCHETTE & BOLTON-MAGGS react
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416 BLANCHETTE & BOLTON-MAGGS [16]
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422 FASANO & LUBAN of storage and t
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424 FASANO & LUBAN Leukocyte reduct
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426 FASANO & LUBAN RBCs is decrease
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Table 1 Blood component characteris
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430 FASANO & LUBAN Alloimmunization
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432 FASANO & LUBAN of individual un
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434 FASANO & LUBAN patients have an
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436 FASANO & LUBAN adults; therefor
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438 FASANO & LUBAN [21]. Meta-analy
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440 FASANO & LUBAN Pretransfusion m
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442 FASANO & LUBAN [55,56]. This is
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444 FASANO & LUBAN [20] Roseff SD,
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Pediatr Clin N Am 55 (2008) 447-460
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THALASSEMIA UPDATE 449 hypochromia
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THALASSEMIA UPDATE 451 Fig. 1. Chan
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THALASSEMIA UPDATE 453 delivery of
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THALASSEMIA UPDATE 455 thromboses i
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THALASSEMIA UPDATE 457 On the basis
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THALASSEMIA UPDATE 459 [31] Kan YW,
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ORAL IRON CHELATORS 463 large iron-
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ORAL IRON CHELATORS 465 of 50 child
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ORAL IRON CHELATORS 467 Table 2 Com
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ORAL IRON CHELATORS 469 use in Nort
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ORAL IRON CHELATORS 471 needed to d
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ORAL IRON CHELATORS 473 in liver fi
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ORAL IRON CHELATORS 475 in the lowe
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ORAL IRON CHELATORS 477 Other oral
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ORAL IRON CHELATORS 479 [12] Borgna
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ORAL IRON CHELATORS 481 [55] Wonke
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HYDROXYUREA FOR CHILDREN WITH SICKL
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504 TRACY & RICE congenital spheroc
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506 TRACY & RICE and antibodies aga
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512 TRACY & RICE German experience
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518 TRACY & RICE [13] Bader-Meunier
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