Mark Corson

By I. Sigmor. North Georgia College and State University, the Military College of Georgia.

Addition of metolazone (a thiazide diuretic) may have a potentiating effect for frusemide in diuretic resistant cases sustiva 500 mg on line. So it is indicated only when there is severe oedema resistant to large doses of diuretics and if the nephrotic patient is to be subjected to surgery or invasive procedure (e order 500mg sustiva otc. This improves circulating blood volume and prevents hypotension or shock during the procedure effective 500mg sustiva. Corticosteroids are given when there is no response to previous lines of treatment. Minimal change glomerulonephritis gives the best response while mesangiocapillary glomerulonephritis is always steroid resistant. The dose and duration of steroid treatment depends on the type of disease and response. In primary (idiopathic) minimal change nephritis 40-60 mg daily prednisone are given orally (for children 1- 2 mg/kg/d), for 4-6 weeks followed by gradual withdrawal. Other immunosuppressive drugs as cyclophosphamide, azathioprine and ciclosporin are indicated in selected cases. The period between infection and the appearance of glomerulonephritis (latent period) is 1-3 weeks for pharyngeal infection and 2-4 weeks for skin infection. Clinical picture: Usually the patients present with manifestations of acute nephritic syndrome with oliguria, smoky urine, puffiness of the face and headache (as a result of hypertension). Some patients may develop encephalopathy as a result of severe hypertension or hyponatraemia or they develop heart failure because of hypertension and fluid retention. Streptococcal antigens stimulate the body to form antibodies against them with the subsequent immune complex formation. Urine may show red cell casts, proteinuria (less than in nephrotic syndrome), haematuria or leucocyturia. Severe cases may show glomerular crescents (cases presenting clinically with rapidly progressive glomerulonephritis). Treatment: Treatment of poststreptococcal glomerulonephritis is mainly symptomatic (rest, salt restriction, diuretics, antihypertensives, treatment of infection and dialysis if renal failure develops). Prognosis: Most of the cases (85%) recover completely, 5% die in early phases from complications (hypertensive encephalopathy or heart failure). The rest of the cases pass to chronic glomerulonephritis and develop chronic renal failure. Signs of bad prognosis are persistently rising serum creatinine, heavy proteinuria, persistent hypertension with gross haematuria and presence of glomerular crescents in renal biopsy. Renal involvement may be the dominant lesion or may be just an incidental finding. Generally, when the kidney is involved, the prognosis and type of treatment are changed drastically. It affects caucasian more than black and occurs more in adolescents than in elderly. Most probably the disease reflects an exaggerated response to common environmental agents in a genetically susceptible host. But, if kidney biopsies are obtained and examined thoroughly, all patients will show glomerular disease. In clinical practice lupus nephritis is responsible for more than 5% of patients presenting with glomerulonephritis. Clinical Manifestations of Lupus Nephritis: It is known that 50-90% of lupus patients will show manifestation(s) of renal disease. Many of such patients may not show any clinically apparent renal disease, but when subjected to kidney biopsy glomerular lesions will be detected. Clinical presentation of lupus nephritis patient may vary from asymptomatic urine abnormality to rapidly progressive glomerulonephritis. Furthermore, some patients show manifestations of tubulointerstitial nephritis (e. Treatment: There is no standard regimen for the treatment of lupus nephritis patient. The available treatment protocols include: (1) Prednisolone, oral, 1mg/kg/d, (2) 3-5 days pulses of methyl prednisolone 500-1000 mg each, (3) Cytoxan (cyclophosphamide) 2-3 mg/kg orally/d (4) cytoxan 0. Generally, the target of treatment is to induce remission, then to maintain it by small doses of either one drug (Prednisolone) or combined (e. The more active the disease, the more aggressive the treatment will be and vice versa. The classic type of polyarteritis nodosa may present with ischaemic renal changes, hypertension, immobilization with renal infarctions or haemorrhage related to the kidney (haematuria, peri-renal hematoma resulting from rupture of aneurysm). Treatment: Patients with active urine sediment (proteinuria, haematuria, casts), renal impairment and documented lesions in renal biopsy should be treated by immunosuppressive drugs to achieve remission. The dose and whether prednisolone alone or combined drug regimen, depend on disease activity and initial reponse to treatment. Cyclosporin A 5mg/kg/d can be used when these drugs are toxic or have no satisfactory response. Renal involvement is documented in 10-30% of the cases, but in some series, it reaches up to 90% of the cases. The primary abnormality is most probably defective handling of mucosally presented antigen. Light microscopy usually shows changes variable from minimal abnormalities, mesangial proliferation, focal mesangial proliferation with crescent formation to membranoproliferative glomerulonephritis. Immunofluorescent microscopy will show predominant IgA deposits which are mainly mesangial, and this is usually accompanied with C3, IgG and to a lesser extent IgM. Clinical features: 1- The disease usually occurs in winter, following upper respiratory infection or following exposure to allergen.

The sample size required to support a model depends on both the R value of the model and the number of variables that are included discount sustiva 200mg. More detailed estimates are available in web-based programs; for example buy sustiva 200 mg low price, the StatsToDo website detailed in the ‘Useful Websites’ section order 500mg sustiva amex. Null hypothesis: That there is no relationship between length and weight at 1 month. This is also a large effect size and from this, it can be concluded that body length has an important associ- ation with weight at 1 month. This also indicates that the regression model overall significantly predicts weight. In the Model table, the null hypotheses being tested are firstly that the Constant value (the Intercept or value a in the regression model) is equal to zero and secondly, that the regression coefficient or slope of the line (the value b in the regression model) is equal to zero. The t values, which are calculated by dividing the beta values (unstandardized coefficient B) by their standard errors, are a test of whether each regression coefficient is significantly different from zero and as such are equivalent to a one-sample t-test. If the regression coefficient is equal to zero this means that for a unit change in the explanatory variable, the predicted value of the outcome variable remains the same. That is, the explanatory variable does not significantly predict the outcome variable. In this example, both the constant (intercept) and slope of the regression line are sig- nificantly different from zero at P < 0. Correlation and regression 211 The Coefficients table shows the unstandardized coefficients that are used to formulate the regression equation in the form of y = a + bx as follows: Weight =−5. Because length is the only explanatory variable in the model, the standardized beta coefficient, which indicates the relative contribution of a variable to the model, is the same as the R value shown in the first table. Thus, this regression model only describes the relation between weight and length in 1-month-old babies who were term births because premature birth was an exclusion criterion for study entry. The model could not be used to predict normal pop- ulation values because the data are not from a random population sample, which would include premature births. However, the model could be used to predict the normal birth weight values for term babies. This interval band is slightly curved because the errors in estimat- ing the intercept and the slope are included in addition to the error in predicting the outcome variable. The 95% individual prediction interval is in which 95% of the data points lie is the distance between the 2. Clearly, any definition of normality is specific to the context but normal values should only be based on large sample sizes, preferably of at least 200 participants. For multiple regression, the equation that explains the line of best fit, that is, the regression line, is y = a + b1x1 + b2x2 + b3x3 +… where ‘a’ is the intercept and ‘bi’ is the slope for each explanatory variable. In multiple regression models, the coefficient for a variable can be interpreted as the unit change in the outcome variable with each unit change in the explanatory variable, when all of the other explanatory variables are held constant. Multiple regression is used when there are several explanatory variables that predict an outcome or when the effect of an observational or experimental factor is being tested. For example, height, age and gender could be used to predict lung function and then the effects of other potential explanatory variables such as current respiratory symptoms or smoking history could be tested. In multiple regression models, all explanatory variables that have an important association with the outcome should be included. In multiple regression, each explanatory variable should ideally have a significant correlation with the outcome variable but the explanatory variables should not be highly correlated with one another, that is collinear. In addition, models should not be over-fitted with a large number of vari- ables that increase the R square by small amounts. In over-fitted models, the R square may decrease when the model is applied to other data. Decisions about which variables to remove or include in a model should be based on expert knowledge and biological plausibility in addition to statistical considerations. These decisions often need to take cost, measurement error and theoretical constructs into account in addition to the strength of association indicated by R values, P values and standardized coefficients. The ideal model should be parsimonious, that is comprised of the smallest number of variables that predict the largest amount of variation. Once a decision has been made about which explanatory variables to test in a model, the distribution of both the outcome and the continuous explanatory variables should be examined using methods outlined in Chapter 2, largely to identify any univariate outliers. The order in which the explanatory variables are entered into the regression model is important because this can make a difference to the amount of variance that is explained by each variable, especially when explanatory variables are significantly related to each other. However, an explanatory variable that is correlated with the outcome variable may not be a significant predictor when the other explanatory variables have accounted for a large proportion of the variance so that the remaining variance is small. In forward selection, variables are added one at a time until the addition of another variable accounts only for a small amount of variance. In backward selection, all variables are entered and then are deleted one at a time if they do not contribute significantly to the prediction of the outcome. Forward selection and backward deletion may not result in the same regression equation. When each new variable is entered, the variance contributed by the variable, possible multicollinearity with other variables and the influence of the variable on the model are assessed. Variables can be entered one at a time or together in blocks and the sig- nificance of each variable, or each variable in the block, is assessed at each step. This method delivers a stable and reliable model and provides invaluable information about the inter-relationships between the explanatory variables. A simple rule that has been suggested for predictive equations is that the minimum number of cases should be at least 100 or, for stepwise regression, that the number of cases should be at least 40 × m,wherem is the number of variables in the model. It is important not to include too many explanatory variables in the model relative to the number of cases because this can inflate the R2 value. When the sample size is very small, the R2 value will be artificially inflated, the adjusted R2 value will be reduced and the imprecise regression estimates may have no sensible interpretation.

After finding a significant result buy cheap sustiva 500 mg on-line, however buy discount sustiva 200 mg, we are confident that we did not make a Type I error because the probability of doing so is less than 500mg sustiva visa. You then compute something like a z-score for your data on the sampling distribution when H0 is true. If the z-score is larger than the critical value, the results are unlikely to represent the populations described by H0, so we reject H0 and accept Ha. The results are called significant, meaning essentially that they are “believable“: The relationship depicted in the sample data can be believed as existing in nature rather than being a chance pattern resulting from sampling error. Inferential statistics are procedures for deciding whether sample data represent a particular relationship in the population. Parametric inferential procedures require assumptions about the raw score populations being represented. Nonparametric inferential procedures do not require stringent assumptions about the populations being represented. The alternative hypothesis 1Ha2 is the statistical hypothesis that describes the population s being represented if the predicted relationship exists. The null hypothesis 1H02 is the statistical hypothesis that describes the population s being represented if the predicted relationship does not exist. A two-tailed test is used when we do not predict the direction in which the dependent scores will change. The z-test is the parametric procedure used in a one-sample experiment if (a) the population contains normally distributed interval or ratio scores and (b) the standard deviation of the population 1σX2 is known. If zobt lies beyond zcrit, then the corresponding sample mean is unlikely to occur when sampling from the population described by H0. This is a significant result and is evidence of the predicted relationship in the population. If zobt does not lie beyond zcrit, then the corresponding sample mean is likely to occur when sampling the population described by H0. This is a nonsignificant result and is not evidence for or against the predicted relationship. Why does the possibility of sampling error present a problem to researchers when inferring a relationship in the population? Describe the experimental hypotheses and the independent and dependent variables when we study: (a) whether the amount of pizza consumed by college students dur- ing finals week increases relative to the rest of the semester, (b) whether breathing exercises alter blood pressure, (c) whether sensitivity to pain is affected by increased hormone levels, and (d) whether frequency of day-dreaming decreases as a function of more light in the room. For each study in question 11, indicate whether a one- or a two-tailed test should be used and state the H0 and Ha. We ask whether attending a private school leads to higher or lower performance on a test of social skills. Foofy claims that a one-tailed test is cheating because we use a smaller zcrit, and therefore it is easier to reject H0 than with a two-tailed test. If the independent variable doesn’t work, she claims, we are more likely to make a Type I error. Poindexter claims that the real cheating occurs when we increase power by increasing the likelihood that results will be significant. He reasons that if we are more likely to reject H0, then we are more likely to do so when H0 is true. Researcher A finds a significant relationship between increasing stress level and ability to concentrate. A report indicates that brand X toothpaste significantly reduced tooth decay rela- tive to other brands, with p 6. We ask if the attitudes toward fuel costs of 100 owners of hybrid electric cars 1X 5 762 are different from those on a national survey of owners of non-hybrid cars 1 5 65, σX 5 242. We ask if visual memory ability for a sample of 25 art majors 1X 5 492 is better than that of engineers who, on a nationwide test, scored 5 45 and σX 5 14. We measure the self-esteem scores of a sample of statistics students, reasoning that this course may lower their self-esteem relative to that of the typical college student 1 5 55 and σX 5 11. We obtain these scores: 44 55 39 17 27 38 36 24 36 (a) Summarize your sample data. For the following, identify the independent variable and the dependent variable and explain why we should use a parametric or nonparametric procedure? We have a X of 40 under the condition of people tested in the morning versus a X of 60 for people tested in the evening. Assuming they accurately represent their populations, how do you envision this relationship in the population? The logic of hypothesis testing discussed in the previous chapter is common to all infer- ential statistical procedures. Your goal now is to learn how slightly different procedures are applied to different research designs. This chapter begins the process by introducing the t-test, which is very similar to the z-test. The chapter presents (1) when and how to perform the t-test, (2) how to use a similar procedure to test correlation coefficients, and (3) a new procedure—called the confidence interval—that is used to estimate. The t-test is important because, like the z-test, the t-test is used for significance testing in a one-sample experiment. In fact, the t-test and the “t-distribution” are used more often in behavioral research. That’s because with the z-test we must know the standard deviation of the raw score population 1σX2. However, usually researchers do not know such things because they’re exploring uncharted areas of behavior. Instead, we usually estimate σX by using the sample data to compute the unbiased, estimated population standard deviation 1sX2. The one-sample t-test is the parametric inferential procedure for a one-sample experiment when the standard deviation of the raw score population must be estimated.