Evidence In Practice PDF

Evidence in Practice: A New Series for Clinicians Understanding a research article requires some familiarity with both the language and the meth- ods used in the research world The aim of the “Evidence in Practice” series is to help practicing physical therapists build expertise in understanding res...

Evidence in Practice: A New Series for Clinicians Understanding a research article requires some familiarity with both the language and the meth- ods used in the research world The aim of the “Evidence in Practice” series is to help practicing physical therapists build expertise in understanding research. Asking a Question: Linking Evidence With Practice Sensitivity and specificity are relevant when defining your question. if your question is too specific, you will not find any evidence that meets all your criteria. Broad questions lead to sensitive searches that identify relevant evidence, but also retrieve lots of irrelevant information. include cointerventions along with the treatment of interest critical to define what the treatment of interest should be compared important to understand which outcome is most important for your patient. outcome(s) of most interest should be specified in question All the same considerations apply to prognosis questions, but the section is not relevant for diagnosis questions. Start with a sensitive (broad) search, gradually increasing specificity until you are left with a manageable number of articles boolean operators * OR” and “AND” are Boolean operators; they connect search terms and make your search more sensitive AND means that the search will find articles that include any of the patient words and any of the intervention words OR will find more articles; adding parts of the question to each other using AND will find fewer. - Even if all parts of the original question are not included in your final search, they still all need to be defined. -This is because all parts of the question are relevant when you judge how to apply the results of the articles identified in your search to your patients. Fundamentals of Measurement: Linking Evidence to Practice From the researcher’s point of view, the reason for measuring various demographic, personal, and clinical factors is self-evident: scores on the measures answer the research question. information guides further assessment and management. construct is what you are interested in measuring. A measure (sometimes called a tool or an instrument) is how the construct is measured. Screening measures are designed to estimate how likely it is that a healthy person will have a certain condition in the future, and whether further investigations (eg, screening for cardio vascular risk) should be pursued. Diagnostic measures (tests) are designed to determine whether someone does or does not have a certain condition Prognostic tools are designed to help predict whether or when a patient will recover Treatment-based classification tools are designed to direct a patient toward a certain type of treatment Outcome measures are designed to track the level or presence of a symptom, function, or disease marker pain intensity measured on a numeric rating scale may form part of a diagnostic test, inform likely prognosis, and be tracked over time as an outcome measure Subjectivity refers to the extent of personal judgment involved in taking a measure, and the personal judgment could be on behalf of the patient or the observer Patient-reported measures: ratings provided by the patient -commonly used to rate symptom severity Observer-rated measures: involve observations made by the clinician -measures like: strength or range of motion, movement quality, or the ability to perform particular tasks Scans, images, tests, and monitoring devices: screening, diagnosis, or measurement of constructs like habitual physical activity. Administrative data: most commonly used in research, and include metrics such as hospital attendance, work absence, insurance claim data, and death. patient relevance-involves judgment as to how important the outcome construct is to your patient Qualitative Research: Linking Evidence to Practice Studies typically involve analyzing the language that participants use to describe their experiences and perceptions. estimates the likelihood of false positive qualitative study- describes the experience of receiving a false positive diagnosis. Qualitative research typically aligns more with one of two broad approaches I Interpretivist: everything is filtered through socially mediated influence -never completely unbiased. 2 Positivist: study findings represent truth like quantitative research. -words or phrases are counted to calculate relationships between different concepts. Thematic analysis: seek to determine the importance of phrases and recognize the influence of the researcher’s own views on the study conclusions -involve counting the number of times a word or phrase appeared in the data and generating themes to record the most common barriers Methods using words as the object include conversation, performance, or narrative analyses. Philosophical Approach: what is valued and how data are analyzed. Participants: characteristics of a study sample rather than obtaining a representative sample sample size requirement for qualitative research studies is typically much smaller than for quantitative research studies. Explicit acknowledgment of biases is called reflexivity data collection structured or semistructured Knowing how information was recorded, who was present, interview location, and how the interview was structured helps describe the participants’ context. researchers must spell out why they stopped collecting data -helps readers judge whether there are sufficient data to answer the research question analysis involve reading interview transcripts then grouping similar codes together to create meaning Reliability and Validity: Linking Evidence to Practice It is no overstatement to say that if a measure is not both sufficiently reliable and valid, then it is not fit for purpose reliability is the extent to which a measurement is free from error implications of unreliable measures are serious. If an unreliable diagnostic test (measure) was applied to a patient several times, then the same patient might be diagnosed as both having and not having the condition on different occasions or by different people data collected from unreliable measures do not provide useful information; a measure that is not reliable cannot be valid. Intrarater reliability refers to the situation where the same rater takes the measure on one patient on several occasions, and reliability is the extent to which the scores from the successive measurements are the same Interrater reliability is relevant when multiple raters use the same measure on a single person, and reliability is the extent to which scores from the different raters are the same. Validity is the extent to which the score on a measure truly reflects the construct it is supposed to measure we collect indirect measures, such as self-reported experiences and behaviors, or recall of beliefs and emotions, and assume that these reflect the construct- when info unobservable or “latent” construct validity of a measure, are ideally able to compare their measure to a “gold standard.(most common) there are no gold standards for many constructs in which we are interested -In these cases, construct validity is tested against a “reference standard,” which is a sort of imperfect gold standard. statistics used to describe agreement depend on whether the measures are dichotomous (eg, kappa, sensitivity/specificity) or continuous (eg, intraclass correlation) Sampling: Linking Evidence to Practice Random and Consecutive Sampling most common approaches to recruiting a representative sample are random sampling and consecutive sampling. Random sampling requires that contact details of everyone in the population are available, so that each individual in the population has an equal chance of being selected for the sample. Consecutive sampling (common in clinical research): everyone who meets the study inclusion criteria at a certain place during a defined period is invited into the study until the necessary sample size is reached, the person recruiting participants into the trial cannot choose who is recruited. Selection bias occurs when certain members of the population are preferentially recruited into the study Different studies may recruit from the same population in different ways. task for clinicians reading a study is to judge whether differences between the patient standing in front of them and the patients included in the study sample are likely to influence the applicability of the study results to their patient. often researchers depend on clinicians to help recruit participants Choosing to Participate Individuals usually have a choice as to whether or not they wish to participate in a study, and can drop out of a study at any time. researchers rely on participants' goodwill and desire to contribute to improving clinical care at the cost of their convenience. task of the evidence-based clinician is to assess the extent to which any differences influence the way the results from a particular study can be applied to an individual patient. Types of Research Questions: Descriptive, Predictive, or Causal Research questions fall into 1 of 3 mutually exclusive types: descriptive, predictive, or causal descriptive q’s seek to describe the “landscape,”to provide an overview of the situation. provide a quantitative summary of certain features of the world predictive q’s help readers form expectations about what is likely to happen in the future aim is to learn something about the future using information from the present, which requires a longitudinal study design. variables that appear in prediction models are not necessarily treatment targets, even when the variables are “modifiable. Causal Questions aim to find treatment targets, identify factors that increase the risk of getting a condition or injury, or estimate what will happen to people who receive one treatment compared to another. nearly always require longitudinal designs. misconception that only experimental studies (randomized controlled trials) can address causal questions. This is not true. Nonrandomized study designs such as longitudinal observational cohorts, case-control studies, and natural experiments can also be used to address causal questions. A Common Problem With Observational Studies Many studies state an aim of “exploring associations” but do not specify whether this is for the purposes of description, prediction, or causation. Sample Size: Linking Evidence to Practice when sample sizes are too large, the risk is that statistically significant findings are clinically irrelevant. small studies are at higher risk of bias. priori power calculations help researchers balance the risks of sample sizes that are too small or too large by identifying a sample size that will give a high probability of identifying an important effect priori power calculations reported with the study can help readers assess whether a study might be underpowered or overpowered. A small study carries more risk that the researchers’ conclusions are inaccurate Sample size directly impacts the precision of effect estimates and measures of statistical significance The smaller the sample, the wider the confidence interval, and the less certain you are about the true treatment effect. Confidence intervals from a small sample often span large and negligible effects, which means it is uncertain whether a treatment is useful or not. Limitations of this approach not with standing,2 a small sample will result in a larger P value regardless of how effective the treatment is When sample sizes are small, P values can be especially unstable Larger sample sizes help minimize sampling variability smaller samples are less likely to be representative and generalizable to the population as a whole study inflation: Underpowered studies that find a statistically significant effect are more likely to report an inflated effect size. Studies that report associations between predictive variables and outcome are complex publication bias: Studies that show an effect or a significant association are more likely to be published than studies that do not. Pilot studies are not designed to test or estimate the effectiveness of a treatment, nor should you interpret the results as such. -designed to prepare for a future definitive study that addresses the research question. Objectives consistent with pilot studies include testing the feasibility and acceptability of the data - collection processes, estimating recruitment rates, and checking adherence to the intervention(s). Pilot studies are not suitable for answering questions about treatment effectiveness. Readers should have lower confidence in results from small studies because of sampling variability, study inflation effects, poor precision, and low power. Small studies are at risk of publication bias and often incorrectly labelled as pilot or feasibility