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AbstractIn biometrical research joint modeling of longitudinal measures and event time data has become a very popular tool to account for informative dropouts, to incorporate longitudinal trends and to link disease processes that occur simultaneously. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: Approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modeled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in a uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research. The increasing variety of joint model approaches are a promising framework to shed light on biometrical questions associated with interacting outcomes.

In biometrical research joint modeling of longitudinal measures and event time data has become a very popular tool to account for informative dropouts, to incorporate longitudinal trends and to link disease processes that occur simultaneously. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modeled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in a uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research. The increasing variety of joint model approaches are a promising framework to shed light on biometrical questions associated with interacting outcomes.

The acute renal effects of systemic hypoxemia and the ability of atrial natriuretic peptide (ANP) to reverse these effects were assessed in seven anesthetized and mechanically ventilated adult rabbits. Throughout the experiment, arterial pH, PaCO2 and HCO3 remained unchanged. Hypoxemia induced a significant increase in rabbit-ANP plasma levels from 151 +/- 26 to 246 +/- 65 pg/ml. During the normoxemic period (PaO2 = 131 +/- 12 mm Hg), glomerular filtration rate (GFR), renal blood flow (RBF), renal vascular resistance (RVR) and urinary sodium excretion (UNaV) were similar in both kidneys. The subsequent hypoxemic period (PaO2 = 30 +/- 1 mm Hg) caused a decrease in right and left kidney function: GFR, -26 +/- 5 and -29 +/- 6%; RBF, -17 +/- 9 and -29 +/- 8%; RVR, +28 +/- 16 and +59 +/- 30%; urine flow rate, -38 +/- 6 and -36 +/- 6%; and UNaV, -51 +/- 7 and -50 +/- 7%, respectively. Human-ANP infusion in the left renal artery (100 ng/min) during sustained systemic hypoxemia induced a significant improvement in GFR (+57 +/- 18%), RBF (+21 +/- 8%), RVR (-20 +/- 7%), urine flow rate (+151 +/- 27%) and UNaV (+270 +/- 48%) in the left experimental kidney, as compared with the preceding hypoxemic period. In contrast, the function of the right control kidney remained impaired.