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1. IntroductionThis articles investigates inferences for several epidemiological measures of practical interest, in the absence or presence of covariates. In the latter scenario, both the logistic regression model and the log-binomial model will be considered. The logistic regression model plays a crucial role in the analysis of binary data arising from clinical trials and observational studies, and the focus of inferences is very often the odds ratio (OR). Another index that is very often used is the relative risk, or the risk ratio (RR). The RR measures the strength of association between a risk factor (or an exposure variable) and disease. Other related indices are the risk difference (RD), and the relative risk difference (RRD). The odds ratio computed under the logistic regression model is known to be a good approximation for the risk ratio for a rare outcome, but not so for an outcome that is common (i.e., not rare). Another epidemiological measure of interest is the prevalence ratio (PR), which measures the association between prevalence of the health outcome and an exposure variable or risk factor. The log-binomial model can be used to estimate the risk ratio in the presence of covariates, when the outcome is not rare. Yet another measure of interest is the number needed to treat (NNT), which is the average number of patients needed to be treated to prevent an additional adverse outcome; the NNT is simply the reciprocal of the risk reduction. For randomized controlled trials with binary outcomes, the NNT is now widely used to measure the benefit of the treatment.All of the above epidemiological measures are functions of the unknown parameters from the regression models, and inferences concerning them has been widely discussed in the literature, very often using standard likelihood based asymptotic methods [1,2,3,4]. We refer to these articles for background information and earlier literature on the point and interval estimation of the above epidemiological measures. The purpose of the present investigation is to explore the methodologies based on generalized confidence intervals and fiducial intervals for the interval estimation of the above quantities, and to assess their performance relative to the likelihood based large sample methods; performance in small sample scenarios will be of particular interest. The generalized confidence interval methodology is due to Weerahandi [5], and it has found numerous applications in interval estimation problems, resulting in confidence intervals that exhibit satisfactory performance in small samples; see also the books by Weerahandi [6,7]. In the context of binary data, the methodology was adopted to obtain satisfactory confidence intervals in a quantal assay problem [8] and in surrogate endpoint validation [9]. Recently, the fiducial approach has seen a revival; in fact, some of the generalized confidence intervals are indeed fiducial

Khi sử dụng phần mềm HTKK, có thể bạn là kế toán, bạn là IT thì các bạn cũng có lúc gặp lỗi không hiện thị tiếng việt, không gõ được tiếng việt trong việc sử dụng HTKK dù bất kỳ ở phiên bản nào.Công ty TNHH Công nghệ số Hùng Vương xin hướng dẫn bạn khắc phục vấn đề phiền phức trên.1/ Đối với việc không hiển thị chuẩn Tiếng ViệtDownload bộ font sau: tại đâyGiải nén, copy nội dung vào thư mục: C:\Windows\Fonts (đối với cài windows vào ổ C, ổ khác tương tự)(Khi copy, máy có hỏi gì thì yes).2/ Đối với việc không gõ được tiếng Việt.Bạn gỡ bỏ phiên bản Unikey hoặc Vietkey đang sử dụng.Tải bộ 3 phần mềm unikey mới tại đâyTùy theo hệ điều hành mà sử dụng:– Unikey 4.0 RC 2 (Dùng cho Windows XP)– UniKey 4.2 RC4, 32 bit (Dùng cho windows 32-bit 7, 8, 8.1)– UniKey 4.2 RC4, 64 bit (Dùng cho windows 64-bit 7, 8, 8.1)Tiếp theo bạn mở Unikey lên, ấn nút “Mở rộng” và kiểm tra nút Check chọn vào ô “Luôn sử dung clipboard cho unicode”.Nên sử dung kiểu gõ Telex và Bảng mã Unicode khi sử dụng HTKK.Trong trường hợp Unikey bị treo hoặc chạy ngầm, không hiển thị dưới thanh taskbar (gần chỗ đồng hồ máy tính) NNT làm theo cách sau:– Ấn tổ hợp phím Windows + R – Tiếp theo gõ vào lệnh sau: taskkill /f /im UnikeyNT.exe =>rồi Enter hoặc OKSau đó NNT download và cài đặt lại Unikey theo hướng dẫn bên trên.Chúc bạn thành công!  Đăng nhập

Details of extension .jntOn this page, we're going to take a closer look at the .jnt file extension, which is related to Microsoft Windows Journal Document.We'll explain what exactly a .jnt file format is and how you can use it. If you're interested in learning more details about this type of file, we'll show you where to find them. And if you ever need to convert .jnt files into different formats, we'll give you some tips on how to do that too.Let's see the file format associated with this file extension!ContentsMicrosoft Windows Journal DocumentWe trust that our website will provide valuable information for you. If you have any questions, just ask!Microsoft Windows Journal DocumentThe JNT file belongs to the Document category and works with Microsoft Windows Journal Viewer, being used as a Microsoft Windows Journal Document. Windows Journal is a notetaking application, created by Microsoft and included in certain editions of Windows XP and Windows Vista. The developer is still actively supporting the Microsoft Windows Journal Viewer, likely. According to our data, Microsoft Windows Journal Viewer uses one more file types.Program name: -Mime-type: application/octet-streamAliases:-Related extensions:.jtp Microsoft Windows Journal TemplateThe .jnt file extension might not only belong to a single type of file, there could be various kinds that use it. Keep in mind that files with the .jnt extension may contain various content types. If you have helpful information about this extension, write to us!Could someone have spelled the .jnt file extension wrong?In our database, we came across the following similar extensions:.unt EES Units List.mnt Visual FoxPro Menu Memo.jgt SigmaWin+ Jog Speed Table Data.knt Miranda IM Keyboard Notify Theme.int BSL Integrate DataThe .jnt extension is frequently misusedPeople sometimes confuse the .jnt filename extension. Based on the searches on our site, here are the most common misspellings from the past year.nt (1), knt (1), jbt (1), jtn (1), hnt (1), njt (1), unt (1), jgt (1), jng (1), nnt (1)Having trouble opening a .jnt file?If you want to open a .jnt file on your computer, you just need to have the appropriate program installed. Incorrect settings for the .jnt association can trigger this error.Windows can't open this file:File: example.jntTo open this file, Windows needs to know what program you want to use to open it. Windows can go online to look it up automatically, or you can manually select from a list of programs that are installed on your computer.To change

Interval for the NNT can be obtained from a confidence interval for p 2 − p 1 . An approximate GPQ as well as fiducial quantities can be used for computing confidence intervals for p 2 − p 1 . In fact, fiducial quantities for these parameters based on Equation (2) are given in [12]. 3.4. Epidemiological Measures under the Logistic and Log-Binomial ModelsSo far, our adaptation of the methodology based on GPQs and fiducial quantities has been for situations where covariates are absent. Clearly, the odds ratio, as well as the other epidemiological measures, have extensive practical applications in the context of binomial responses that depend on covariates. The logistic model is very often used to model the response probability. The log-binomial model is sometimes used to estimate the risk ratio in the presence of covariates, when the outcome is not rare. As noted in Section 2.4, under the log-binomial model, the binomial success probabilities p satisfies ln ( p ) = x ′ β , where x is a covariate vector. Writing x = ( x 1 , x 2 , . . . . , x s ) ′ and β = ( β 1 , β 2 , . . . . , β s ) ′ , where s is the number of covariates, the parameter β 1 is the prevalence ratio (PR) for a one unit increase in x 1 , adjusted for the other covariates. We recall that GPQs and fiducial quantities for β are given in Section 2.3 and Section 2.4 for the logistic model and the log-binomial model, respectively. From this, GPQs and fiducial quantities can be constructed for any function of β ; in particular, for the various epidemiological measures, including the prevalence ratio. 4. Numerical ResultsThe accuracy of the proposed procedures based on GPQs and fiducial quantities is assessed using simulations. Here, we have presented the results for only two scenarios: interval estimation of a common odds ratio (under binomial distributions without covariates), and the interval estimation of a prevalence ratio (under the log-binomial model). We refer to [12] for numerical results on the performance of fiducial intervals for a few other parameters, including that for the difference between binomial proportions. Note that coverage probability for the latter is equivalent to that for the NNT. 4.1. Common Odds RatioTable 1 gives the coverage probabilities of the confidence intervals based on different approaches for a common odds ratio from K = 5 studies, for a 95% nominal level. We also assume that, for the different studies, n 1 k = n 1 , and n 2 k = n 2 (k = 1, 2, …, 5), where we have used

And median lengths are also given (the numbers within parenthesis in Table 2). It appears that all the approaches perform well in terms of coverage probabilities; the minor differences noted among the mean lengths and median lengths among the GPQ-based and fiducial-based solutions are perhaps due to the minor differences among the coverage probabilities. We also note that in terms of median lengths, the ML solution has a slight edge over the other solutions. However, its mean length is unusually large in a few cases. This could be a reflection of the convergence problems while maximizing the likelihood; we suspect that the information matrix is becoming close to being singular, resulting in wide intervals. Note that the solutions based on the GPQ approach and the fiducial approach are both free of this drawback. 5. ExamplesWe present four examples in this section in order to illustrate our interval estimation methodologies, and for making comparisons with other available intervals. 5.1. NNT: Depression and InsomniaThis example is based on data from a cross-sectional study of sleep disturbances among HIV-infected persons in an investigation of the association between depression and insomnia [19]. Insomnia was assessed using the Pittsburgh Sleep Quality Index (PSQI) (with a global score greater than five taken as indication of insomnia). Depression was assessed using the Beck Depression Inventory (BDI). The problem is to estimate the NNT, or, more precisely, the number needed to expose (NNE). The data are reported in Table 3.Among subjects with normal levels of depression (BDI ≤ 9 ), 36.6% have insomnia (56/(56 + 97)), while among subjects with at least a mild level of depression (BDI ≥ 10 ), 82.5% have insomnia (33/(33 + 7)).Thus, the estimated NNE is 1/(0.825 – 0.366) = 2.18. This means that, on average, among approximately every two subjects with a level of depression mild or above, there will be one additional insomnia case relative to the normal group. Ninety-five percent confidence intervals for the NNE are reported in Table 4. We have also included the Wald–Yates and Agresti–Caffo intervals [20] for comparison.We note that the intervals based on the GPQ, as well as those based on F1 and F2, are shorter compared to the other two intervals. 5.2. Common Odds Ratio: Viral SuppressionThe U.S. Military HIV Natural History Study (NHS) is a prospective continuous enrollment cohort study of consenting military beneficiaries with HIV infection including active duty personnel, retirees, and dependents [21]. In this example, we consider the subjects on highly active antiretroviral therapy (HAART) with at least one viral load value (VL) in the first year. A subject is considered viral suppressed (VS) if the VL value at the last visit during the first year is below 400 copies/mL.

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