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Evaluating the Literature. How do you decide if an article you have found is a good one?. What question is the study answering?. Does it relate to your patient? Does it apply to your setting? Look for studies that answer questions relevant to your practice.
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Evaluating the Literature How do you decide if an article you have found is a good one?
What question is the study answering? • Does it relate to your patient? • Does it apply to your setting? • Look for studies that answer questions relevant to your practice. • If so, move on with further article analysis
Who is being studied? Look for patients similar to your patients, or patients that are easily compared to your patients. Example: A study shows that women with stage 3 breast cancer who participate in breast cancer support groups have a better prognosis than those who do not. This study is done on a group of insured women; the authors report that 97% are white, and their average age is 61. Before recommending support groups for all your patients with breast cancer as a life-prolonging measure, you have to consider whether these women are comparable to your patients. Look at the patients’ ages, socioeconomic status, insurance status. Look at the stage of cancer in this study and make sure it applies to your patient’s care.
Where is the study being done? Look for a setting similar to your setting Example: A study examining the use of meditation in decreasing tension-type headaches provided the intervention in a special, soundproofed meditation room. The special setting may influence the outcomes of the study. You have to decide whether you think you could get similar results in a busy family doctor’s office.
What is being investigated? What is the intervention? Look for medications that are available to you FDA approved On your formularies or otherwise obtainable Special treatments (example: seed radiation or brachytherapy for prostate cancer) are done at your site or are accessible to you Patients must be able to reasonably access an intervention Must be covered by insurance or cheap
Is it unduly burdensome? Look for studies where the intervention is available to you and not worse than the disease. Example: The Alexander technique is effective for decrease symptoms of low back pain. However, there are very few Alexander technique practitioners outside of large cities. Example: Intensive psychotherapy reduces relapse in patients with bulimia. However, most insurance plans will not pay for more than 24 psychotherapy sessions per year.
Is there a control or placebo group? You need to make sure that the intervention is not getting results by chance, and a control or placebo group helps determine that. Example: A stem cell therapy is applied to nerve cells, and the cells grow an average of 5 mm. However, when a placebo is applied to the nerve cells under similar conditions, the cells grow an average of 5 mm. If the intervention is being tested against “usual care”, make sure the “usual care” makes sense with your usual care. Example: A study finds that New Drug (ND) is superior to Old Drug (ODD) in the treatment of heart failure. However, you never use ODD because you do not think it is effective, preferring to use Intermediate Drug (ID) instead. You want to find a study comparing ND to ID instead of OD.
Control or Placebo Group Cont. Are the patients being randomly assigned to intervention or control groups? How are patients and investigators being blinded? Look for studies where the investigators and the patients do not know whether they are getting a placebo or an active treatment. This helps eliminate a source of bias in the study. Are the groups equivalent to begin with? The patients in both groups should be the same at the start, otherwise it can be hard to draw conclusions about whether the treatment worked or whether the results are due to those initial differences.
Follow Up How long are patients in the study being followed up? Does this make sense for the disease or condition and the intervention being studied? Example: a study examines the effect of New Drug (ND) compared with placebo. The investigators want to know if ND can slow the progression of coronary artery disease in patients with high cholesterol. The study tracks patients for 4 weeks after beginning the trial, and then concludes that ND is not any better or worse than placebo. In this case, the follow-up time does not make sense with the endpoint being studied, since CAD usually takes more than 4 weeks to arise. Example: New Drug (ND) is being studied to determine if it improves symptoms of depression compared with placebo. The participants are tracked for 8 weeks after beginning the study, and ND decreases depression symptoms more than placebo. However, participants are not followed after 8 weeks, so long-term conclusions can’t be drawn.
Outcomes and Endpoints What are the outcomes or endpoints being studied? Do they make sense for the disease and the intervention being studied? Look for outcomes or endpoints that are measurable: scales of symptoms or quality of life, functional abilities, patient perceptions using a standardized questionnaire. Example: a study examining the effect of a nice lice treatment considers a patient “cured” if they are no longer symptomatic. This outcome may not be good enough, since the patient cannot return to school until he or she is free from lice and nits. Lack of itch is a benefit, but not as useful an outcome to study.
Measurements Are the measurements reliable? • If you did this study again with the same measurements on the same people, would you get the same results? • Reasons for low reliability might be: -Different investigators get different responses, or rate responses in different ways Example: two physicians are judging range of motion of knees after a new therapy treatment. Doctor A consistently under- estimates the degree of flexion. This study would have poor reliability. -Patients change their answers if asked the same questions again
Are these outcomes patient-centered? Patient-centered outcomes are those that involve things that actually matter to patients, such as quality of life, functional measurements, incidence or risk of disease, or pain. Disease-oriented outcomes are those we measure that do not necessarily affect patients, such as hemoglobin levels, blood pressure, or cholesterol levels. Disease-oriented outcomes are not as useful as patient-oriented outcomes, because there is frequently not a clear, direct connection between the levels measured and what happens to patients. Example: a study of the use of erythropoietin in patients on hemodialysis for end-stage kidney disease (ESRD) found that patients whose anemia was corrected to a ‘normal’ level (mean 13.7mg/dl) actually had more adverse events such as stroke, CHF, and MI, than patients whose anemia was corrected to a mean of 11.3 mg/dl. Quality of life indicators were similar in both groups. The disease-oriented outcome is the hemoglobin level; it seemed reasonable that higher would be better. However, when the patient-oriented outcomes (adverse events and quality of life) were measured, the lower hemoglobin levels were found to be better. Ref: Singh, Ajay K., Szczech, Lynda, Tang, Kezhen L., Barnhart, Huiman, Sapp, Shelly, Wolfson, Marsha, Reddan, Donal, the CHOIR Investigators, Correction of Anemia with Epoetin Alfa in Chronic Kidney DiseaseN Engl J Med 2006 355: 2085-2098
Results What does the study show? What are the results? What are the adverse events associated with the intervention? Look for results that are clearly stated and clearly explained. Make sure you understand the headings and columns of all the tables. If you are looking at figures to help draw conclusions, make sure you understand which data are being included in the figure, the axes of the figure, and the type of figure it is.
Are the Results Meaningful? How meaningful are the results? Are they statistically significant? Clinically significant? Example: a questionnaire is measuring quality of life in patients with low back pain. The intervention group receives posture training, and their quality of life scores improve by 6 points on a 100 point scale. The control group receives nothing, and their scores improve by 2 points on the same scale. The results may be statistically significant, but clinically the significance of a 4-point increase in a 100 point scale may be nothing. Or the significance may be outweighed by the burden of the posture training.
Can you think of any sources of bias that the authors did not address or avoid? Are the people in the study the same as people not in the study (can the results from the people in the study be generalized to all people, or is there something special or specific about the people in the study)? Is the setting generalizable to your setting (is a study done in German emergency rooms applicable to your Dallas, Texas, family medicine office? How about one done in a small private practice in rural Sparta, Wisconsin, compared with your large medical group practice in Chicago?) Could any of the people in the control group have gotten the intervention in another setting? How do you know if the people in the invention group were truly doing the intervention (taking the meds, participating in therapies, etc)? How did the investigators deal with people who did not finish the study (dropped out or lost to follow-up)? To minimize bias, these people should be included in the analysis. Look for “intention to treat” analyses.
Could the funding of the study affect its outcomes? Example: Drug Company (DC) is making a new probiotic to prevent colds in children. The probiotic seems effective, but you note that all of the investigators are employed by DC. Can you trust that the investigators are free from bias? Look for studies that declare who had access to the data collected. Investigators without any ties to the company should have full control over the analysis and publication of the data.
