[FREE] Chapter 13 Test Form 2a Answers
The simplest way to ensure that the interpretation is correct is first to convert the odds into a risk. For example, when the odds are , or 0. In a sample of , about 9 individuals will have the event and 91 will not. The difference between odds and...
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Chapter 13 Test, Form 2A (continued)
For example, a risk of 0. Effect measures for randomized trials with dichotomous outcomes involve comparing either risks or odds from two intervention groups. To compare them we can look at their ratio risk ratio or odds ratio or the difference in risk risk difference. The risk ratio RR, or relative risk is the ratio of the risk of an event in the two groups, whereas the odds ratio OR is the ratio of the odds of an event see Box 6.
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For both measures a value of 1 indicates that the estimated effects are the same for both interventions. Neither the risk ratio nor the odds ratio can be calculated for a study if there are no events in the comparator group. This is because, as can be seen from the formulae in Box 6. The odds ratio also cannot be calculated if everybody in the intervention group experiences an event. In the case where no events or all events are observed in both groups the study provides no information about relative probability of the event and is omitted from the meta-analysis.
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This is entirely appropriate. Zeros arise particularly when the event of interest is rare, such as unintended adverse outcomes. For further discussion of choice of effect measures for such sparse data often with lots of zeros see Chapter 10, Section Risk ratios describe the multiplication of the risk that occurs with use of the experimental intervention. For example, a risk ratio of 3 for an intervention implies that events with intervention are three times more likely than events without intervention. Similarly, a risk ratio of 0. This is known as the relative risk reduction see also Chapter 15, Section The interpretation of the clinical importance of a given risk ratio cannot be made without knowledge of the typical risk of events without intervention: a risk ratio of 0. What constitutes clinically important will depend on the outcome and the values and preferences of the person or population. This means that for common events large values of risk ratio are impossible.
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For example, when the observed risk of events in the comparator group is 0. This boundary applies only for increases in risk, and can cause problems when the results of an analysis are extrapolated to a different population in which the comparator group risks are above those observed in the study. Odds ratios, like odds, are more difficult to interpret Sinclair and Bracken , Sackett et al Odds ratios describe the multiplication of the odds of the outcome that occur with use of the intervention. To understand what an odds ratio means in terms of changes in numbers of events it is simplest to convert it first into a risk ratio, and then interpret the risk ratio in the context of a typical comparator group risk, as outlined here. The formula for converting an odds ratio to a risk ratio is provided in Chapter 15, Section Sometimes it may be sensible to calculate the RR for more than one assumed comparator group risk.
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This non-equivalence does not indicate that either is wrong: both are entirely valid ways of describing an intervention effect. Problems may arise, however, if the odds ratio is misinterpreted as a risk ratio. For interventions that reduce the chances of events, the odds ratio will be smaller than the risk ratio, so that, again, misinterpretation overestimates the effect of the intervention. This error in interpretation is unfortunately quite common in published reports of individual studies and systematic reviews. The risk difference can be calculated for any study, even when there are no events in either group.
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The risk difference is straightforward to interpret: it describes the difference in the observed risk of events between experimental and comparator interventions; for an individual it describes the estimated difference in the probability of experiencing the event. However, the clinical importance of a risk difference may depend on the underlying risk of events in the population. For example, a risk difference of 0. Although the risk difference provides more directly relevant information than relative measures Laupacis et al , Sackett et al , it is still important to be aware of the underlying risk of events, and consequences of the events, when interpreting a risk difference.
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Absolute measures, such as the risk difference, are particularly useful when considering trade-offs between likely benefits and likely harms of an intervention. The risk difference is naturally constrained like the risk ratio , which may create difficulties when applying results to other patient groups and settings. For example, if a study or meta-analysis estimates a risk difference of —0. Similar scenarios for increases in risk occur at the other end of the scale. Such problems can arise only when the results are applied to populations with different risks from those observed in the studies.
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The number needed to treat is obtained from the risk difference. Although it is often used to summarize results of clinical trials, NNTs cannot be combined in a meta-analysis see Chapter 10, Section However, odds ratios, risk ratios and risk differences may be usefully converted to NNTs and used when interpreting the results of a meta-analysis as discussed in Chapter 15, Section In the context of dichotomous outcomes, healthcare interventions are intended either to reduce the risk of occurrence of an adverse outcome or increase the chance of a good outcome.
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For example, when participants have particular symptoms at the start of the study the event of interest is usually recovery or cure. If participants are well or, alternatively, at risk of some adverse outcome at the beginning of the study, then the event is the onset of disease or occurrence of the adverse outcome. It is possible to switch events and non-events and consider instead the proportion of patients not recovering or not experiencing the event. In RevMan, these can be entered as the numbers with the outcome and the total sample sizes for the two groups.
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Examples of truly continuous data are weight, area and volume. In practice, we can use the same statistical methods for other types of data, most commonly measurement scales and counts of large numbers of events see Section 6. A common feature of continuous data is that a measurement used to assess the outcome of each participant is also measured at baseline, that is, before interventions are administered.
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This gives rise to the possibility of computing effects based on change from baseline also called a change score. When effect measures are based on change from baseline, a single measurement is created for each participant, obtained either by subtracting the post-intervention measurement from the baseline measurement or by subtracting the baseline measurement from the post-intervention measurement. Analyses then proceed as for any other type of continuous outcome variable. Two summary statistics are commonly used for meta-analysis of continuous data: the mean difference and the standardized mean difference. These can be calculated whether the data from each individual are post-intervention measurements or change-from-baseline measures.
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Methods for meta-analysis of continuous outcome data are covered in Chapter 10, Section It estimates the amount by which the experimental intervention changes the outcome on average compared with the comparator intervention. It can be used as a summary statistic in meta-analysis when outcome measurements in all studies are made on the same scale. This name is potentially confusing: although the meta-analysis computes a weighted average of these differences in means, no weighting is involved in calculation of a statistical summary of a single study. In this circumstance it is necessary to standardize the results of the studies to a uniform scale before they can be combined. The SMD expresses the size of the intervention effect in each study relative to the between-participant variability in outcome measurements observed in that study.
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Again in reality the intervention effect is a difference in means and not a mean of differences. Thus, studies for which the difference in means is the same proportion of the standard deviation SD will have the same SMD, regardless of the actual scales used to make the measurements. However, the method assumes that the differences in SDs among studies reflect differences in measurement scales and not real differences in variability among study populations.
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If in two trials the true effect as measured by the difference in means is identical, but the SDs are different, then the SMDs will be different. This may be problematic in some circumstances where real differences in variability between the participants in different studies are expected. For example, where early explanatory trials are combined with later pragmatic trials in the same review, pragmatic trials may include a wider range of participants and may consequently have higher SDs. The overall intervention effect can also be difficult to interpret as it is reported in units of SD rather than in units of any of the measurement scales used in the review, but several options are available to aid interpretation see Chapter 15, Section Effect sizes typically, though not always, refer to versions of the SMD.
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How to approach the creation of your repayment plan What is a Chapter 13 repayment plan? Bankruptcy can help you get out from under considerable debt, but not all forms of bankruptcy allow you to keep many of your most important assets along the way. Those with regular income can file a Chapter 13 bankruptcy to help keep key assets like a home or car. In Chapter 13 , debts are restructured over a three- or five-year period. If you make regular payments over that time, then some or all of your debts may be discharged. It must be drawn up and filed with the bankruptcy court within 14 days of filing the bankruptcy petition unless you get an extension , after which the judge and your creditors will have a chance to assess and possibly challenge the plan. Generally, your debts will be split into three different categories in your Chapter 13 repayment plan. Priority debts Priority debts are those that must be paid off during the course of your plan, with certain exceptions.
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These are debts like back taxes you owe, the cost of filing for bankruptcy, and child- and spousal-support payments that need to be brought current. Secured debts Secured debts are those that are backed by collateral — a home mortgage or auto loan, for example. Depending on the specifics of the secured loan, you can be required to pay back the value of the collateral or the full payment of the debt. We recommend reaching out to an attorney to learn more about the proper treatment of secured claims in the plan. Unsecured debts Last are unsecured debts, like those from credit cards, unsecured personal loans and medical bills. If that happens, those debts may eventually be discharged. How to approach the creation of your repayment plan The calculation of the Chapter 13 repayment plan can be a complicated process full of uncertainty. For this reason, we strongly suggest working with a lawyer to determine your eligibility and to draw up the particulars of your repayment plan.
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The beginning calculation process features two primary stages — the Chapter 13 means test and the creation of the plan itself. The Chapter 13 means test In simple terms, the Chapter 13 means test determines the basic structure of the repayment plan. Form C-1 requires the filer to add up all sources of household income. If your average monthly income falls below the state median, then your repayment plan can cover three years. The length will ultimately be determined by the court, but this form sets a starting point as you work on the initial version of your repayment plan.
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Form C-2 then uses your average monthly income as a baseline for determining the disposable income that can be used to pay back creditors. The filer can claim numerous deductions on everything from the cost of food to health insurance in order to determine disposable income, but the restrictions on how much can be claimed in each category are often strictly tied to IRS standards.
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Creating and filing the repayment plan Once the means test is complete, you can start to work on drafting the repayment plan itself. The details of the plan will depend on your unique debts and the disposable income you calculated during the means test, so we strongly advise that you work on it with a trained expert like a bankruptcy lawyer. Not all Chapter 13 filers need the same advice for creating a repayment plan. The court will assess the plan and hold a hearing to give your creditors a chance to make any objections.
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