Personal Growth Foundations Of Clinical Research Ebook


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eBook features: Highlight, take notes, and search in the book; In this edition, page numbers are just like the physical edition; Length: pages; Format: Print . Foundations of clinical research: applications by Leslie Gross Portney · Foundations of clinical research eBook: Document. English. Vancouver, BC. Foundations of Clinical Research: Applications to Practice, 3/e provides the foundations that are necessary for finding and interpreting research evidence across.

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This book serves as a comprehensive reference for a variety of research situations. This edition reflects the popular approaches to analysis and design, and. Draw upon the foundations necessary for finding and interpreting research Revised to reflect the most current changes in the field of clinical research in. Online PDF Foundations of Clinical Research: Applications to Practice, 3rd, 3rd, Leslie Portney ebook Foundations of Clinical Research: Applications to.

A common design feature is the use of central labs for quantitating laboratory parameters to eliminate between-lab variation or the use of central evaluators to eliminate between-evaluator variation. Variation can also be reduced with standardization of the manner in which study participants are treated and evaluated via training. For example, in studies that involve imaging, it is very important to have an imaging protocol that standardizes the manner in which images are collected to reduce added variation due to inconsistent patient positioning.

Training modules can be developed to instruct site personnel on the appropriate administration of evaluations.

It essentially eliminates the bias associated with treatment selection. Although randomization cannot ensure between-treatment balance with respect to all participant characteristics, it does ensure the expectation of balance.

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Importantly randomization ensures this expectation of balance for all factors even if the factors are unknown or unmeasured. This expectation of balance that randomization provides combined with the ITT principle, provides the foundation for statistical inference. Trials commonly employ stratified randomization to ensure that treatment groups are balanced with respect to confounding variables.

In stratified randomization, separate randomization schedules are prepared for each stratum. For example, gender is a potential confounder for estimating the effects of interventions to treat or prevent stroke e. Thus trials investigating the effects of such interventions might employ stratified randomization based on gender. For example, two randomization schedules may be utilized; one for males and another for females. Stratified randomization ensures that the number of male participants in each treatment group is similar and that the number of female participants in each treatment group is similar.

Stratification has a few limitations. First, stratification can only be utilized for known and measurable confounders.

Secondly, although one can stratify on multiple variables, one has to be wary of over-stratification i. The sample size must be large enough to enroll several participants for each treatment from each stratum. Blinding refers to keeping study participants, investigators, or assessors unaware of the assigned intervention so that this knowledge will not affect their behavior, noting that a change in behavior can be subtle, unnoticeable, and unintentional. When study participants are blinded, they may be less likely to have biased psychological or physical responses to intervention, less likely to use adjunct intervention, less likely to drop out of the study, and more likely to adhere to the intervention.

Blinding of study participants is particularly important for patient reported outcomes e. When trial investigators are blinded, they may be less likely to transfer inclinations to study participants, less likely to differentially apply adjunctive therapy, adjust a dose, withdraw study participants, or encourage participants to continue participation. When assessors are blinded, they may be less likely to have biases affect their outcome assessments. In a placebo controlled trial for an intervention for multiple sclerosis, an evaluation was performed by both blinded and unblinded neurologists.

A benefit of the intervention was suggested when using the assessments from neurologists that were not blinded, but not when using the assessments from the blinded neurologists. In this case, the blinded assessment is thought to be more objective. Successful blinding is not trivial. In a placebo-controlled trial, a placebo must be created to look, smell, and taste just like the intervention.

For example a concern for a trial evaluating the effects of minocycline on cognitive function may be that minocycline can cause a change in skin pigmentation, thus unblinding the intervention.

Blinding can be challenging or impractical in many trials. For example surgical trials often cannot be double-blind for ethical reasons. The effects of the intervention may also be a threat to the blind. For example, an injection site reaction of swelling or itching may indicate an active intervention rather than a sham injection. Researchers could then consider using a sham injection that induces a similar reaction.

In late phase clinical trials, it is common to compare two active interventions. These interventions may have different treatment schedules e. This is often easier than trying to make the two interventions look like each other. Participants are then randomized to receive one active treatment and one placebo but are blinded.

The downside of this approach is that the treatment schedules become more complicated i. When blinding is implemented in a clinical trial, a plan for assessing the effectiveness of the blinding may be arranged. This usually requires two blinding questionnaires, one completed by the trial participant and the other completed by the local investigator or person that conducts the evaluation of the trial participant. Reviews of blinded trials suggest that many trials experience issues that jeopardize the blind.

For example in a study assessing zinc for the treatment of the common cold Prasad et al the blinding failed because the taste and aftertaste of zinc was distinctive. Creative designs can be utilized to help maintain the blind.

GV has staining potential which could jeopardize the blind when the assessors conduct oral examinations after treatment.

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A staining cough drop could be given to study participants prior to evaluation to help maintain the blind. Unplanned unblinding should only be undertaken to protect participant safety i.

Blinding has been poorly reported in the literature. Researchers should explicitly state whether a study was blinded, who was blinded, how blinding was achieved, the reasons for any unplanned unblinding, and state the results of an evaluation of the success of the blinding. One disadvantage to the use of placebos is that sometimes they can be costly to obtain. Although the placebo pill or injection has no activity for the disease being treated, it can provide impressive treatment effects.

This is especially true when the endpoint is subjective e. Evans et. Evans et al reported a significant improvement in pain in the placebo arm of a trial investigating an intervention for the treatment of painful HIV-associated peripheral neuropathy. There can be many logistic and ethical concerns in clinical trials where neither a placebo, nor a sham control can be applied. The inability to use placebos is common in the development of devices.

The control group provides data about what would have happened to participants if they were not treated or had received a different intervention. Without a control group, researchers would be unable to discriminate the effects caused by the investigational intervention from effects due to the natural history of the disease, patient or clinician expectations, or the effects of other interventions. The selection of a control group depends on the research question of interest.

If it is desirable to show any effect, then placebo-controls are the most credible and should be considered as a first option. However placebo controls may not be ethical in some cases and thus active controls may be utilized. If it is desirable to show noninferiority or superiority to other active interventions then active controls may be utilized. Historical controls are obtained from studies that have already been conducted and are often published in the medical literature.

The data for such controls is external to the trial being designed and will be compared with data collected in the trial being designed.

The advantage of using historical controls is that the current trial will require fewer participants and thus use of historical controls provides an attractive option from a cost and efficiency perspective. The drawback of trials that utilize historical controls is that they are non-randomized studies i. Historical controls are rarely used in clinical trials for drug development due to the concerns for bias.

However, when historical data are very reliable, well documented and other disease and treatment conditions have not changed since the historical trial was conducted, then they can be considered. Historical controls have become common in device trials when placebo-controls are not a viable option.

Historical controls can be helpful in interpreting the results from trials for which placebo controls are not ethical e. An active control is an active intervention that has often shown effectiveness to treat the disease under study. Often an active control is selected because it is the standard of care SOC treatment for the disease under study. Active controls are selected for use in noninferiority trials. Active controls and placebo controls can be used simultaneously and provide useful data.

For example, if the new intervention was unable to show superiority to placebo, but an active control group was able to demonstrate superiority to placebo, then this may be evidence that the new intervention is not effective.

However, if the active control with established efficacy did not demonstrate superiority to placebo, then it is possible the trial was flawed or may have been underpowered because of the placebo response or variability being unexpected high. However researchers also select entry criteria to help ensure a high quality trial and to address the specific objectives of the trial. The selection of a population can depend on the trial phase since different phases have different objectives.

Early phase trials tend to select populations that are more homogenous since it is easier to reduce response variation and thus isolate effects.

Later phase trials tend to target more heterogeneous populations since it is desirable to have the results of such trials to be generalizable to the population in which the intervention will be utilized in practice.

It is often desirable for this targeted patient population to be as large as possible to maximize the impact of the intervention.

Thus phase III trials tend to have more relaxed entry criteria that are representative both in demographics and underlying disease status to the patient population for which the intervention is targeted to treat.

When constructing entry criteria, the safety of the study participant is paramount.

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Researcher should consider the appropriateness of recruiting participants with various conditions into the trial. The ability to accrue study participants can also affect the selection of entry criteria. Although strict entry criteria may be scientifically desirable in some cases, studies with strict entry criteria may be difficult to accrue particularly when the disease is rare or alternative interventions or trials are available.

Entry criteria may need to be relaxed so that enrollment can be completed within a reasonable time frame. Researchers should also consider restricting entry criteria to reduce variation and potential for bias. Participants that enroll with confounding indications that could influence treatment outcome could be excluded to reduce potential bias. For example, in a trial evaluating interventions for HIV-associated painful neuropathy, conditions that may confound an evaluation of neuropathy such as diabetes or a B12 deficiency may be considered exclusionary.

The motivation for every clinical trial begins with a scientific question. The primary objective of the trial is to address the scientific question by collecting appropriate data. The selection of the primary endpoint is made to address the primary objective of the trial. The primary end-point should be clinically relevant, interpretable, sensitive to the effects of intervention, practical and affordable to measure, and ideally can be measured in an unbiased manner.

Endpoints can generally be categorized by their scale of measurement. The three most common types of endpoints in clinical trials are continuous endpoints e. The scale of the primary endpoint impacts the analyses, trial power, and thus costs. In many situations, more than one efficacy endpoints are used to address the primary objective.

This creates a multiplicity issue since multiple tests will be conducted.

Decisions regarding how the statistical error rates e. Endpoints can be classified as being objective or subjective. Objective endpoints are those that can be measured without prejudice or favor. Death is an objective endpoint in trials of stroke. Subjective endpoints are more susceptible to individual interpretation. For example, neuropathy trials employ pain as a subjective endpoint.

Other examples of subjective endpoints include depression, anxiety, or sleep quality. Objective endpoints are generally preferred to subjective endpoints since they are less subject to bias. Composite endpoints An intervention can have effects on several important endpoints. Composite endpoints combine a number of endpoints into a single measure.

The advantages of composite endpoints are that they may result in a more completed characterization of intervention effects as there may be interest in a variety of outcomes. Composite endpoints may also result in higher power and resulting smaller sample sizes in event-driven trials since more events will be observed assuming that the effect size is unchanged.

Composite endpoints may also reduce the bias due to competing risks and informative censoring. This is because one event can censor other events and if data were only analyzed on a single component then informative censoring can occur.

Composite endpoints may also help avoid the multiplicity issue of evaluating many endpoints individually.

Composite endpoints have several limitations. Firstly, significance of the composite does not necessarily imply significance of the components nor does significance of the components necessarily imply significance of the composite.

For example one intervention could be better on one component but worse on another and thus result in a non-significant composite.

Another concern with composite endpoints is that the interpretation can be challenging particularly when the relative importance of the components differs and the intervention effects on the components also differ. For example, how do we interpret a study in which the overall event rate in one arm is lower but the types of events occurring in that arm are more serious? Higher event rates and larger effects for less important components could lead to a misinterpretation of intervention impact.

It is also possible that intervention effects for different components can go in different directions. Power can be reduced if there is little effect on some of the components i. When designing trials with composite endpoints, it is advisable to consider including events that are more severe e.

It is also advisable to collect data and evaluate each of the components as secondary analyses. This means that study participants should continue to be followed for other components after experiencing a component event. When utilizing a composite endpoint, there are several considerations including: i whether the components are of similar importance, ii whether the components occur with similar frequency, and iii whether the treatment effect is similar across the components.

Surrogate Endpoints In the treatment of some diseases, it may take a very long time to observe the definitive endpoint e. A surrogate endpoint is a measure that is predictive of the clinical event but takes a shorter time to observe.

The definitive endpoint often measures clinical benefit whereas the surrogate endpoint tracks the progress or extent of disease.

Surrogate endpoints could also be used when the clinical end-point is too expensive or difficult to measure, or not ethical to measure. An example of a surrogate endpoint is blood pressure for hemorrhagic stroke. Surrogate markers must be validated. Ideally evaluation of the surrogate endpoint would result in the same conclusions if the definitive endpoint had been used.

The criteria for a surrogate marker are: 1 the marker is predictive of the clinical event, and 2 the intervention effect on the clinical outcome manifests itself entirely through its effect on the marker. It is important to note that significant correlation does not necessarily imply that a marker will be an acceptable surrogate.

Missing data can create biased estimates of treatment effects. DeMets, PhD is currently the Max Halperin Professor of Biostatistics and former Chair of the Department of Biostatistics and Medical Informatics at the University of Wisconsin — Madison He has co-authored numerous papers on statistical methods and four texts on clinical trials, two specifically on data monitoring. He has served on many NIH and industry-sponsored data monitoring committees for clinical trials in diverse disciplines.

In , he was elected as a member of the Institute of Medicine.

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Christopher B. Granger is Professor of Medicine at Duke University, where he is an active clinical cardiologist and a clinical trialist at the Duke Clinical Research Institute. He has had Steering Committee, academic leadership, and operational responsibilities for many clinical trials in cardiology.

He has been on numerous Data Monitoring Committees. Food and Drug Administration and Duke aiming to increase the quality and efficiency of clinical trials.

David M. Reboussin has served on the Data and Safety Monitoring Boards for many National Institutes of Health trials within areas including cardiology, diabetes, nephrology, pulmonology, liver disease, psychiatry, pediatrics, weight loss and smoking cessation.

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His work in statistical methodology has included techniques and software for sequential monitoring of clinical trials.Variation in these diagnoses can be minimized with clear definitions and consistent evaluations.

An active control is an active intervention that has often shown effectiveness to treat the disease under study. Theodore rated it did not like it Nov 29, Other Editions 9. Researchers need to be careful about influencing participant adherence since the goal of the trial may be to evaluate the strategy of how the interventions will work in practice which may not include incentives to motivate patients similar to that used in the trial.

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For example, when evaluating a new intervention, an investigator may consider using a smaller Type I error e. If it is desirable to show noninferiority or superiority to other active interventions then active controls may be utilized.

Arrangement is in five parts, each focusing on a different phase of the r.

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