TAILIEUCHUNG - Statistical Methods for Survival Data Analysis 3rd phần 5

Ví dụ Trong một nghiên cứu về bệnh bạch cầu lymphocytic mãn tính và myelocytic, Feinleib và MacMahon (1960) đã áp dụng việc phân phối lognormal phân tích dữ liệu sống còn của 649 cư dân trắng của Brooklyn chẩn đoán 1943-1952. | 200 GRAPHICAL METHODS FOR SURVIVAL DISTRIBUTION FITTING the data have a long tail to the right and could be from a distribution with a positively l d density fnnctinn such as in Fiuuee . From the discussion in Chapter 6 we may tty to fit a tggnormat or gamma dirSriUution. The advantages of graphical methods can be summarized as follows 1. They ree asst add simpee to see m contest with uamurigal methoss which may lee computaSionaUy todtoat Odd eeuuéee cadoidetbble agaiyti-cal sophistication. The additional accuracyof numerical methods is usuallynot great enough in practice to warrant the effort involved. 2. Probability Odd azaadd ptots provide approsãmate estimates of hhe parameters of the distribution bysimple graphical means. 3. They allow nee to sses wetter a gurtisutur tdcueetigal distriSetlad provides an adequate fit to the data. 4. Peculiar appearance of a plot or points in a plot can provide insight into the data when the reasons for the peculiarities are determined. 5. A graph provides a visual representation of the data that is easy to grasp. This is useful not onlyfor oneself but also in presenting data to others since a plot allows one to assess conclusions drawn from the data by graphical or numerical means. PROBABILITY PLOTTING The basic ideas in probability ptotsidg are ilfustraeed l CIo Collowing egampee. Example Consider the white blood cell counts WBCs of 23 pediatric leukemia patients given in Table ranging from 8000 to 120 000. A sample cumulative distribution is constructed by uoduridg tee a te from amadast to largest as shown in Table . A sample cumulative distribution curve can then be made by ptotsidg each WBC vatoe versus the percedaage of the a Ịlh equal to or less than that value. That is the ith ordered data value in a sample of n values is plotted against the percentage 100i n. Note that for tied observations we compute and plot the sample distribution onlyfor the one with the largest i value. This gives a conservative

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