Clinical Research Lecture 3 PDF

# Clinical Research Dr Engy Ahmed Wahsh Lecturer of clinical pharmacy, Faculty of pharmacy, October 6 university ## Formulating Clinical Questions - A useful approach to formatting a clinical (or research) question is using the **Patient Intervention Comparison Outcome (PICO)** framework - The qu...

# Clinical Research Dr Engy Ahmed Wahsh Lecturer of clinical pharmacy, Faculty of pharmacy, October 6 university ## Formulating Clinical Questions - A useful approach to formatting a clinical (or research) question is using the **Patient Intervention Comparison Outcome (PICO)** framework - The questions is divided into four key components: 1. **Patient/Population:** Which patients or population group of patients are you interested in? Is it necessary to consider any subgroups? 2. **Intervention (exposure):** Which intervention/treatment is being evaluated? 3. **Comparison/Control:** What is/are the main alternative/s compared to the intervention? 4. **Outcome:** What is the most important outcome for the patient? Outcomes can include short- or long-term measures, intervention complications, social functioning or quality of life, morbidity, mortality or costs. ## Formulating Clinical Research Question **PI(E)CO = Population + Exposure/Intervention + Comparator + outcome** **key elements of the question** ## Research Question Structure **Observational** - Indicator or risk factor - Outcome - Population **Interventional (PICO)** - Population - Intervention - Comparator - Outcome **Diagnostic** - New Test - Reference Test (index) - Population ## Choosing A Sample - The basic principle of statistics is simple: Using limited amounts of data (your 'sample'), we wish to make the strongest possible conclusions about the wider population. - For these conclusions to be valid, we must consider the precision and accuracy of the analyses. ## Accuracy - The study sample is accurate if it is representative of the population from which it was chosen. - This can be achieved if: - Each individual of the population has an equal chance of being selected (random sampling). - The selection is completely independent of individual characteristics such as age, sex, or ethnic origin. - If samples were not randomly selected (systematic bias), on average, any sample estimate will differ from the population statistic. - As a result, the study sample will be inaccurate. ## Precision - The amount of variation between the sample estimates determines the precision of the study sample. - If there is little variability between the sample estimates, i.e. if the sample estimates themselves are similar to each other, the study sample statistics are more precise. - When choosing between accurate and precise study samples, it is more important to be accurate because, on average, the study sample estimates will be closer to the true population value. ## Extrapolating from 'Sample' to 'Population' The image shows a diagram which depicts how random sampling might be applied, with a large group of people, or a target population on the left. On the right, a smaller group of people, or a selected sample. The selected sample is used to extrapolate from the larger target population using a statistical test ## Types of Variables - Independent Variables - Dependent Variables - Confounding Variables - Extraneous Variables ## Independent Variables - Independent variables are manipulated or controlled by the researcher. - They represent the cause or predictor variable in a study. - Example: In a study investigating the effect of different study techniques on exam scores, the independent variable is the study technique (e.g., reading, highlighting, summarizing). ## Dependent Variables - Dependent variables are observed or measured to assess the effect of the independent variable. - They represent the outcome or response variable in a study. - Example: In the same study mentioned earlier, the dependent variable is the exam score achieved by students after using the different study techniques. ## Independent vs Dependent Variable - There can be many variables in an experiment, but the two key variables that are always present are the independent and dependent variable. - The independent variable is the one that the researcher intentionally changes or controls. - The dependent variable is the factor that the research measures. It changes in response to the independent variable or depends upon it. ## Remembering Variables With DRYMIX - When results are plotted in graphs, the convention is to use the independent variable as the x-axis and the dependent variable as the y-axis. - The DRY MIX acronym can help keep the variables straight: - **D** is the dependent variable. - **R** is the responding variable. - **Y** is the axis on which the dependent or responding variable is graphed (the vertical axis). - **M** is the manipulated variable or the one that is changed in an experiment. - **I** is the independent variable. - **X** is the axis on which the independent or manipulated variable is graphed (the horizontal axis). ## Confounding Variables - A variable that affects the independent or dependent variable, altering the ability to determine the true effect on the measured outcome. These factors may hide or exaggerate a true association. - To minimize the potential for missing a confounding variable, all relevant information should be collected a evaluated. During the design, subjects should be randomized or matched. - Example: In a study examining the relationship between coffee consumption and heart disease risk, age could be a confounding variable because older individuals might drink more coffee and are also at a higher risk of heart disease. ## Extraneous Variables - Extraneous variables are variables other than the independent variable that may affect the dependent variable. - They need to be controlled or accounted for to minimize their impact on the study results. - Example: In a study examining the effect of caffeine consumption on cognitive performance, the time of day when the cognitive tests are administered could be an extraneous variable. If participants take the tests in the morning when they are typically more alert and attentive, this extraneous variable may influence their performance on the cognitive tasks independent of caffeine consumption. - Controlling for extraneous variables is crucial to ensure the internal validity of the study and the reliability of the findings. ## Extraneous and confounding variables - The image shows a diagram which depicts the effects of confounding vs extraneous variables. They both impact the dependent variable, but confounding variables originate from the independent variable. ## Differentiating between Extraneous and Potential Confounding variables - **EEE -** Extraneous variables are usually identified Early in the study and are thus Eradicated before they can have a major impact on the DV. Most Environmental factors/variables can be classified as extraneous variables. E.g. outside noises in a test room or low lighting in a visual acuity test. - **(Potential) Confounding Variables** are usually not identified early on and are thus not controlled for or eradicated. Because of this, they have the 'potential' to cause unwanted changes and thus 'confound' the DV. A 'confounded' DV makes it difficult for us to determine whether it was the IV itself that caused the change in the DV or whether it was the confounding variable that caused this change. ## Research - **1RY RESEARCH -** Original data - **2RY RESEARCH -** Data from 1ry resources ## 1ry research - Information appears for the first time. - Data are not published before. ## 2ry research - Data were published before, and the investigators are retrieving these data to summarize them, add them, reanalyze them, or synthesize new evidence from them. - Secondary research includes a summary, collection, and/or synthesis of existing research. ## Secondary Clinical Study Designs - **Reviews** - Traditional narrative literature reviews - Systematic review - Meta-analysis - **Secondary Analyses** - Secondary data analyses - Pooled/post-hoc analysis of multiple RCTs ## Observational Study * No intervention is implemented by the investigator. * Assesses for biomedical or health outcomes. * As the National Cancer Institute explains it, participants are observed or certain outcomes are measured, but no attempt is made to affect the outcome. Which is to say, no treatment is given. * One benefit to this approach is the fact that a large population can be studied, over a long period of time, if needed. * An observational study could even be a survey in which the investigator is sampling a particular group of people and asking them questions over time. ## Interventional Study * Participants are assigned to receive one or more interventions (or no intervention). * This intervention might be a surgical operation, a drug, or any other form of treatment. * The investigators manipulate this risk factor to examine its effect on this population. * The assignments are determined by the study protocol. ## Primary Clinical Study Designs - **Observational** - Descriptive - Case report or Case series - Cross-sectional - **Diagnostic** - Analytical - Cross-sectional - cohort - **Interventional** - RCT - Case control - NON RCT ## Study Purpose: Descriptive versus Analytical - **Time Orientation:** Prospective versus retrospective design - **Prospective:** Begin in the present and progress forward, collecting data from subjects whose outcomes lie in the future. - **Retrospective:** Begin and end in the present; however, this design involves a major backward look to collect information about events that occurred in the past. - **Investigator Orientation:** - Experimental trials - Quasi-experimental trials - (c) Observational trials ## Hierarchy of Study Designs - **Relative Strength of Evidence** - Hierarchy of Study Designs: This hierarchy holds assuming that all of the study designs are performed using the best possible techniques (e.g., a poorly conducted RCT is not necessarily higher on the hierarchy) than a well-done cohort study). - **Systematic Reviews and Meta-Analysis** - **RCT** - **Cohort** - **Case-Control** - **Cross-Sectional** - **Case Series** - **Case Reports** - **Ideas, opinions, and reviews** ## LOE (Level of Evidence) | Estimate of Certainty for Treatment Effect | Extent of Populations Studied | Types of Trials Utilized | |---|---|---| | Level A | Multiple populations evaluated | Data derived from multiple trials/meta-analyses | | Level B | Limited populations evaluate | Data derived from a single trial or nonrandomized study | | Level C | Very limited populations evaluated | Consensus opinion of experts or case reports | ## CLASS (Strength) of Recommendation | CLASS | Benefit > Risk | Suggested Phrases for Writing Recommendations | |---|---|---| | CLASS I (STRONG) | Benefit >>> Risk | Is recommended; Is indicated/useful/effective/beneficial; Should be performed/administered/other; Treatment/strategy A is recommended/indicated in preference to treatment B; Treatment A should be chosen over treatment B | | CLASS IIa (MODERATE) | Benefit >> Risk | Is reasonable; Can be useful/effective/beneficial; Treatment/strategy A is probably recommended/indicated in preference to treatment B; It is reasonable to choose treatment A over treatment B | | CLASS IIb (WEAK) | Benefit > Risk | May/might be reasonable; May/might be considered; Usefulness/effectiveness is unknown/unclear/uncertain; or not well established | | CLASS III: No Benefit (MODERATE) | Benefit = Risk | Is not recommended; Is not indicated/useful/effective/beneficial; Should not be performed/administered/other | | CLASS III: Harm (STRONG) | Risk > Benefit | Is not recommended; Causes harm; Associated with excess morbidity/mortality; Should not be performed/administered/other | ## LEVEL (Quality) of Evidence | LEVEL | Description | |---|---| | LEVEL A | High-quality evidence from more than 1 RCT; Meta-analyses of high-quality RCTs; One or more RCTs corroborated by high-quality registry studies | | LEVEL B-R (Randomized) | Moderate-quality evidence from 1 or more RCTs; Meta-analyses of moderate-quality RCTs | | LEVEL B-NR (Nonrandomized) | Moderate-quality evidence from 1 or more well-designed, well-executed nonrandomized studies, observational studies, or registry studies; Meta-analyses of such studies | | LEVEL C-LD (Limited Data) | Randomized or nonrandomized observational or registry studies with limitations of design or execution; Meta-analyses of such studies; Physiological or mechanistic studies in human subjects | | LEVEL C-EO (Expert Opinion) | Consensus of expert opinion based on clinical experience |