EPIDEMIOLOGY BIOSTATISTICS EXAM Exam 1, 2000.pdf
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This is NOT an all-inclusive study guide. This is a summary of topics that arecommonly requested that we cover during the review session for exam #1. Inaddition to content included here, I would recommend going through the learningobjectives for each lecture, revisiting the questions from the lectures and in-classactivities, going back to the quiz questions, and looking over the homeworkassignments.
Scenario: Researchers interested in identifying risk factors for liver failure examine the correlation between the number of cases of liver failure in countries around the world. The find a positive correlation between the amount of alcohol consumer per capita and number of liver failure cases.
It is true that the smaller the P value, the more unusual the data would be if every single assumption were correct; but a very small P value does not tell us which assumption is incorrect. For example, the P value may be very small because the targeted hypothesis is false; but it may instead (or in addition) be very small because the study protocols were violated, or because it was selected for presentation based on its small size. Conversely, a large P value indicates only that the data are not unusual under the model, but does not imply that the model or any aspect of it (such as the targeted hypothesis) is correct; it may instead (or in addition) be large because (again) the study protocols were violated, or because it was selected for presentation based on its large size.
Methods: The growth charts were developed with data from five national health examination surveys and limited supplemental data. Smoothed percentile curves were developed in two stages. In the first stage, selected empirical percentiles were smoothed with a variety of parametric and nonparametric procedures. In the second stage, parameters were created to obtain the final curves, additional percentiles and z-scores. The revised charts were evaluated using statistical and graphical measures.
HCV can be passed from an infected mother to her baby and via sexual practices that lead to exposure to blood (for example, people with multiple sexual partners and among men who have sex with men); however, these modes of transmission are less common.
1 EPIDEMIOLOGY-BIOSTATISTICS Exam 1, 2005 Print Your Legal Name: ID Number: Instructions: This exam is 25% of your course grade. The maximum number of points for the course is 1,000 hence this exam is worth 250 points. There are 20 questions on this exam. Each question is worth 10.5 points to yield the maximum of 250 points for this exam. For questions 1-10, record the best answer in pencil on the answer sheet provided. For questions 11-20, write your answers neatly in the spaces provided. Be sure you have printed your legal name and ID number on the top of each page. 1. Which of the following is not a part of the informed consent process for participating as a subject in a randomized controlled trial: a. Informing the subject that the lead investigator will determine the subject s assignment arm based on the subject s past medical history. b. Informing the subject about the potential pros and cons (risks and benefits) of participating in the trial. c. Providing the subject a description of study procedures, identifying any that are experimental. d. Telling the subject the duration of the trial and their length of participation. e. Providing the subject with a statement concerning the purpose of the 2. Which of the following epidemiologic study designs is best suited for assessing a possible association between an exposure and a rare outcome. a. Case Series b. Prospective Cohort Study c. Case Control Study d. Retrospective Cohort Study e. Cross-Sectional Study 3. Which of the following epidemiologic study designs is most prone to interviewer bias? a. Randomized Controlled Trial b. Case Control Study c. Prospective Cohort Study d. Case Series e. Cross-Sectional Study
Sample Multiple Choice Questions for the material since Midterm 2. Sample questions from Midterms and 2 are also representative of questions that may appear on the final exam.. A randomly selected sample
BRM.1 The proportion of individuals with a particular disease who die from that condition is called... BRM.2 This study design examines factors that may contribute to a condition by comparing subjects
Ease of interpretation The odds ratio is the hardest summary statistic to understand and to apply in practice, and many practising clinicians report difficulties in using them. There are many published examples where authors have misinterpreted odds ratios from meta-analyses as risk ratios. Although odds ratios can be re-expressed for interpretation (as discussed here), there must be some concern that routine presentation of the results of systematic reviews as odds ratios will lead to frequent over-estimation of the benefits and harms of interventions when the results are applied in clinical practice. Absolute measures of effect are thought to be more easily interpreted by clinicians than relative effects (Sinclair and Bracken 1994), and allow trade-offs to be made between likely benefits and likely harms of interventions. However, they are less likely to be generalizable.
Where data have been analysed on a log scale, results are commonly presented as geometric means and ratios of geometric means. A meta-analysis may be then performed on the scale of the log-transformed data; an example of the calculation of the required means and SD is given in Chapter 6, Section 22.214.171.124. This approach depends on being able to obtain transformed data for all studies; methods for transforming from one scale to the other are available (Higgins et al 2008b). Log-transformed and untransformed data should not be mixed in a meta-analysis.
Occasionally authors encounter a situation where data for the same outcome are presented in some studies as dichotomous data and in other studies as continuous data. For example, scores on depression scales can be reported as means, or as the percentage of patients who were depressed at some point after an intervention (i.e. with a score above a specified cut-point). This type of information is often easier to understand, and more helpful, when it is dichotomized. However, deciding on a cut-point may be arbitrary, and information is lost when continuous data are transformed to dichotomous data.
Rate data occur if counts are measured for each participant along with the time over which they are observed. This is particularly appropriate when the events being counted are rare. For example, a woman may experience two strokes during a follow-up period of two years. Her rate of strokes is one per year of follow-up (or, equivalently 0.083 per month of follow-up). Rates are conventionally summarized at the group level. For example, participants in the comparator group of a clinical trial may experience 85 strokes during a total of 2836 person-years of follow-up. An underlying assumption associated with the use of rates is that the risk of an event is constant across participants and over time. This assumption should be carefully considered for each situation. For example, in contraception studies, rates have been used (known as Pearl indices) to describe the number of pregnancies per 100 women-years of follow-up. This is now considered inappropriate since couples have different risks of conception, and the risk for each woman changes over time. Pregnancies are now analysed more often using life tables or time-to-event methods that investigate the time elapsing before the first pregnancy.
The scope of a review will largely determine the extent to which studies included in a review are diverse. Sometimes a review will include studies addressing a variety of questions, for example when several different interventions for the same condition are of interest (see also Chapter 11) or when the differential effects of an intervention in different populations are of interest. Meta-analysis should only be considered when a group of studies is sufficiently homogeneous in terms of participants, interventions and outcomes to provide a meaningful summary. It is often appropriate to take a broader perspective in a meta-analysis than in a single clinical trial. A common analogy is that systematic reviews bring together apples and oranges, and that combining these can yield a meaningless result. This is true if apples and oranges are of intrinsic interest on their own, but may not be if they are used to contribute to a wider question about fruit. For example, a meta-analysis may reasonably evaluate the average effect of a class of drugs by combining results from trials where each evaluates the effect of a different drug from the class.
There may be specific interest in a review in investigating how clinical and methodological aspects of studies relate to their results. Where possible these investigations should be specified a priori (i.e. in the protocol for the systematic review). It is legitimate for a systematic review to focus on examining the relationship between some clinical characteristic(s) of the studies and the size of intervention effect, rather than on obtaining a summary effect estimate across a series of studies (see Section 10.11). Meta-regression may best be used for this purpose, although it is not implemented in RevMan (see Section 10.11.4).
The summary estimate and confidence interval from a random-effects meta-analysis refer to the centre of the distribution of intervention effects, but do not describe the width of the distribution. Often the summary estimate and its confidence interval are quoted in isolation and portrayed as a sufficient summary of the meta-analysis. This is inappropriate. The confidence interval from a random-effects meta-analysis describes uncertainty in the location of the mean of systematically different effects in the different studies. It does not describe the degree of heterogeneity among studies, as may be commonly believed. For example, when there are many studies in a meta-analysis, we may obtain a very tight confidence interval around the random-effects estimate of the mean effect even when there is a large amount of heterogeneity. A solution to this problem is to consider a prediction interval (see Section 10.10.4.3). 2b1af7f3a8