The quality assessment tool for quantitative studies

They are not intended to create a list that you simply tally up to arrive at a summary judgment of quality. Internal validity for cohort studies is the extent to which the results reported in the study can truly be attributed to the exposure being evaluated and not to flaws in the design or conduct of the study—in other words, the ability of the study to draw associative conclusions about the effects of the exposures being studied on outcomes.

Any such flaws can increase the risk of bias. Critical appraisal involves considering the risk of potential for selection bias, information bias, measurement bias, or confounding the mixture of exposures that one cannot tease out from each other. Examples of confounding include co-interventions, differences at baseline in patient characteristics, and other issues throughout the questions above. Thus, the greater the risk of bias, the lower the quality rating of the study. In addition, the more attention in the study design to issues that can help determine whether there is a causal relationship between the exposure and outcome, the higher quality the study.

These include exposures occurring prior to outcomes, evaluation of a dose-response gradient, accuracy of measurement of both exposure and outcome, sufficient timeframe to see an effect, and appropriate control for confounding—all concepts reflected in the tool. Generally, when you evaluate a study, you will not see a "fatal flaw," but you will find some risk of bias.

By focusing on the concepts underlying the questions in the quality assessment tool, you should ask yourself about the potential for bias in the study you are critically appraising. For any box where you check "no" you should ask, "What is the potential risk of bias resulting from this flaw in study design or execution?

The best approach is to think about the questions in the tool and how each one tells you something about the potential for bias in a study. The more you familiarize yourself with the key concepts, the more comfortable you will be with critical appraisal. Examples of studies rated good, fair, and poor are useful, but each study must be assessed on its own based on the details that are reported and consideration of the concepts for minimizing bias.

The guidance document below is organized by question number from the tool for quality assessment of case-control studies.

High quality scientific research explicitly defines a research question. Did the authors describe the group of individuals from which the cases and controls were selected or recruited, while using demographics, location, and time period? If the investigators conducted this study again, would they know exactly who to recruit, from where, and from what time period?

Investigators identify case-control study populations by location, time period, and inclusion criteria for cases individuals with the disease, condition, or problem and controls individuals without the disease, condition, or problem. For example, the population for a study of lung cancer and chemical exposure would be all incident cases of lung cancer diagnosed in patients ages 35 to 79, from January 1, to December 31, , living in Texas during that entire time period, as well as controls without lung cancer recruited from the same population during the same time period.

The population is clearly described as: 1 who men and women ages 35 to 79 with cases and without controls incident lung cancer ; 2 where living in Texas ; and 3 when between January 1, and December 31, Other studies may use disease registries or data from cohort studies to identify cases.

In these cases, the populations are individuals who live in the area covered by the disease registry or included in a cohort study i. For example, a study of the relationship between vitamin D intake and myocardial infarction might use patients identified via the GRACE registry, a database of heart attack patients. NHLBI staff encouraged reviewers to examine prior papers on methods listed in the reference list to make this assessment, if necessary.

In order for a study to truly address the research question, the target population—the population from which the study population is drawn and to which study results are believed to apply—should be carefully defined.

Some authors may compare characteristics of the study cases to characteristics of cases in the target population, either in text or in a table. When study cases are shown to be representative of cases in the appropriate target population, it increases the likelihood that the study was well-designed per the research question.

However, because these statistics are frequently difficult or impossible to measure, publications should not be penalized if case representation is not shown. For most papers, the response to question 3 will be "NR. However, it cannot be determined without considering the response to the first subquestion. For example, if the answer to the first subquestion is "yes," and the second, "CD," then the response for item 3 is "CD.

Did the authors discuss their reasons for selecting or recruiting the number of individuals included? Did they discuss the statistical power of the study and provide a sample size calculation to ensure that the study is adequately powered to detect an association if one exists? An article's methods section usually contains information on sample size and the size needed to detect differences in exposures and on statistical power.

To determine whether cases and controls were recruited from the same population, one can ask hypothetically, "If a control was to develop the outcome of interest the condition that was used to select cases , would that person have been eligible to become a case?

Cases and controls are then evaluated and categorized by their exposure status. For the lung cancer example, cases and controls were recruited from hospitals in a given region. One may reasonably assume that controls in the catchment area for the hospitals, or those already in the hospitals for a different reason, would attend those hospitals if they became a case; therefore, the controls are drawn from the same population as the cases.

If the controls were recruited or selected from a different region e. The following example further explores selection of controls. In a study, eligible cases were men and women, ages 18 to 39, who were diagnosed with atherosclerosis at hospitals in Perth, Australia, between July 1, and December 31, Appropriate controls for these cases might be sampled using voter registration information for men and women ages 18 to 39, living in Perth population-based controls ; they also could be sampled from patients without atherosclerosis at the same hospitals hospital-based controls.

As long as the controls are individuals who would have been eligible to be included in the study as cases if they had been diagnosed with atherosclerosis , then the controls were selected appropriately from the same source population as cases. In a prospective case-control study, investigators may enroll individuals as cases at the time they are found to have the outcome of interest; the number of cases usually increases as time progresses.

At this same time, they may recruit or select controls from the population without the outcome of interest. One way to identify or recruit cases is through a surveillance system.

In turn, investigators can select controls from the population covered by that system. This is an example of population-based controls. Investigators also may identify and select cases from a cohort study population and identify controls from outcome-free individuals in the same cohort study. This is known as a nested case-control study.

Were the same underlying criteria used for all of the groups involved? The investigators should have used the same selection criteria, except for study participants who had the disease or condition, which would be different for cases and controls by definition. Therefore, the investigators use the same age or age range , gender, race, and other characteristics to select cases and controls. Information on this topic is usually found in a paper's section on the description of the study population.

For this question, reviewers looked for descriptions of the validity of case and control definitions and processes or tools used to identify study participants as such. Was a specific description of "case" and "control" provided? Is there a discussion of the validity of the case and control definitions and the processes or tools used to identify study participants as such? They determined if the tools or methods were accurate, reliable, and objective.

For example, cases might be identified as "adult patients admitted to a VA hospital from January 1, to December 31, , with an ICD-9 discharge diagnosis code of acute myocardial infarction and at least one of the two confirmatory findings in their medical records: at least 2mm of ST elevation changes in two or more ECG leads and an elevated troponin level. All cases should be identified using the same methods. Unless the distinction between cases and controls is accurate and reliable, investigators cannot use study results to draw valid conclusions.

When it is possible to identify the source population fairly explicitly e. When investigators used consecutive sampling, which is frequently done for cases in prospective studies, then study participants are not considered randomly selected. In this case, the reviewers would answer "no" to Question 8. However, this would not be considered a fatal flaw. If investigators included all eligible cases and controls as study participants, then reviewers marked "NA" in the tool.

If percent of cases were included e. If this cannot be determined, the appropriate response is "CD. A concurrent control is a control selected at the time another person became a case, usually on the same day.

This means that one or more controls are recruited or selected from the population without the outcome of interest at the time a case is diagnosed. Investigators can use this method in both prospective case-control studies and retrospective case-control studies. For example, in a retrospective study of adenocarcinoma of the colon using data from hospital records, if hospital records indicate that Person A was diagnosed with adenocarcinoma of the colon on June 22, , then investigators would select one or more controls from the population of patients without adenocarcinoma of the colon on that same day.

This assumes they conducted the study retrospectively, using data from hospital records. The investigators could have also conducted this study using patient records from a cohort study, in which case it would be a nested case-control study.

Investigators can use concurrent controls in the presence or absence of matching and vice versa. A study that uses matching does not necessarily mean that concurrent controls were used. Investigators first determine case or control status based on presence or absence of outcome of interest , and then assess exposure history of the case or control; therefore, reviewers ascertained that the exposure preceded the outcome. For example, if the investigators used tissue samples to determine exposure, did they collect them from patients prior to their diagnosis?

If hospital records were used, did investigators verify that the date a patient was exposed e. For an association between an exposure and an outcome to be considered causal, the exposure must have occurred prior to the outcome. This is important, as it influences confidence in the reported exposures. Equally important is whether the exposures were assessed in the same manner within groups and between groups.

This question pertains to bias resulting from exposure misclassification i. For example, a retrospective self-report of dietary salt intake is not as valid and reliable as prospectively using a standardized dietary log plus testing participants' urine for sodium content because participants' retrospective recall of dietary salt intake may be inaccurate and result in misclassification of exposure status.

Similarly, BP results from practices that use an established protocol for measuring BP would be considered more valid and reliable than results from practices that did not use standard protocols. A protocol may include using trained BP assessors, standardized equipment e.

Blinding or masking means that outcome assessors did not know whether participants were exposed or unexposed. To answer this question, reviewers examined articles for evidence that the outcome assessor s was masked to the exposure status of the research participants.

An outcome assessor, for example, may examine medical records to determine the outcomes that occurred in the exposed and comparison groups.

In this case, the outcome assessor would most likely not be blinded to exposure status. A reviewer would note such a finding in the comments section of the assessment tool. One way to ensure good blinding of exposure assessment is to have a separate committee, whose members have no information about the study participants' status as cases or controls, review research participants' records. To help answer the question above, reviewers determined if it was likely that the outcome assessor knew whether the study participant was a case or control.

If it was unlikely, then the reviewers marked "no" to Question Outcome assessors who used medical records to assess exposure should not have been directly involved in the study participants' care, since they probably would have known about their patients' conditions. If blinding was not possible, which sometimes happens, the reviewers marked "NA" in the assessment tool and explained the potential for bias. Investigators often use logistic regression or other regression methods to account for the influence of variables not of interest.

This is a key issue in case-controlled studies; statistical analyses need to control for potential confounders, in contrast to RCTs in which the randomization process controls for potential confounders. In the analysis, investigators need to control for all key factors that may be associated with both the exposure of interest and the outcome and are not of interest to the research question. A study of the relationship between smoking and CVD events illustrates this point.

Such a study needs to control for age, gender, and body weight; all are associated with smoking and CVD events. Well-done case-control studies control for multiple potential confounders. Matching is a technique used to improve study efficiency and control for known confounders. For example, in the study of smoking and CVD events, an investigator might identify cases that have had a heart attack or stroke and then select controls of similar age, gender, and body weight to the cases.

For case-control studies, it is important that if matching was performed during the selection or recruitment process, the variables used as matching criteria e.

NHLBI designed the questions in the assessment tool to help reviewers focus on the key concepts for evaluating a study's internal validity, not to use as a list from which to add up items to judge a study's quality. Internal validity for case-control studies is the extent to which the associations between disease and exposure reported in the study can truly be attributed to the exposure being evaluated rather than to flaws in the design or conduct of the study.

In other words, what is ability of the study to draw associative conclusions about the effects of the exposures on outcomes? In critical appraising a study, the following factors need to be considered: risk of potential for selection bias, information bias, measurement bias, or confounding the mixture of exposures that one cannot tease out from each other.

High risk of bias translates to a poor quality rating; low risk of bias translates to a good quality rating. Again, the greater the risk of bias, the lower the quality rating of the study. In addition, the more attention in the study design to issues that can help determine whether there is a causal relationship between the outcome and the exposure, the higher the quality of the study.

If a study has a "fatal flaw," then risk of bias is significant; therefore, the study is deemed to be of poor quality. An example of a fatal flaw in case-control studies is a lack of a consistent standard process used to identify cases and controls. Generally, when reviewers evaluated a study, they did not see a "fatal flaw," but instead found some risk of bias. By focusing on the concepts underlying the questions in the quality assessment tool, reviewers examined the potential for bias in the study.

For any box checked "no," reviewers asked, "What is the potential risk of bias resulting from this flaw in study design or execution? By examining questions in the assessment tool, reviewers were best able to assess the potential for bias in a study.

Specific rules were not useful, as each study had specific nuances. In addition, being familiar with the key concepts helped reviewers assess the studies. Examples of studies rated good, fair, and poor were useful, yet each study had to be assessed on its own.

Did the authors describe the eligibility criteria applied to the individuals from whom the study participants were selected or recruited? In other words, if the investigators were to conduct this study again, would they know whom to recruit, from where, and from what time period? Here is a sample description of a study population: men over age 40 with type 2 diabetes, who began seeking medical care at Phoenix Good Samaritan Hospital, between January 1, and December 31, The population is clearly described as: 1 who men over age 40 with type 2 diabetes ; 2 where Phoenix Good Samaritan Hospital ; and 3 when between January 1, and December 31, Another sample description is women who were in the nursing profession, who were ages 34 to 59 in , had no known CHD, stroke, cancer, hypercholesterolemia, or diabetes, and were recruited from the 11 most populous States, with contact information obtained from State nursing boards.

To assess this question, reviewers examined prior papers on study methods listed in reference list when necessary. Question 3. Study participants representative of clinical populations of interest.

The participants in the study should be generally representative of the population in which the intervention will be broadly applied. Studies on small demographic subgroups may raise concerns about how the intervention will affect broader populations of interest. For example, interventions that focus on very young or very old individuals may affect middle-aged adults differently. Similarly, researchers may not be able to extrapolate study results from patients with severe chronic diseases to healthy populations.

Did the authors present their reasons for selecting or recruiting the number of individuals included or analyzed? Did they note or discuss the statistical power of the study? This question addresses whether there was a sufficient sample size to detect an association, if one did exist. An article's methods section may provide information on the sample size needed to detect a hypothesized difference in outcomes and a discussion on statistical power such as, the study had 85 percent power to detect a 20 percent increase in the rate of an outcome of interest, with a 2-sided alpha of 0.

In any case, if the reviewers determined that the power was sufficient to detect the effects of interest, then they would answer "yes" to Question 5. Another pertinent question regarding interventions is: Was the intervention clearly defined in detail in the study?

Did the authors indicate that the intervention was consistently applied to the subjects? Did the research participants have a high level of adherence to the requirements of the intervention? Or did a large percentage of participants end up not taking the specific dose of Drug A indicated in the study protocol? Reviewers ascertained that changes in study outcomes could be attributed to study interventions.

If participants received interventions that were not part of the study protocol and could affect the outcomes being assessed, the results could be biased.

This question is important because the answer influences confidence in the validity of study results. But even with a measure as objective as death, differences can exist in the accuracy and reliability of how investigators assessed death.

For example, did they base it on an autopsy report, death certificate, death registry, or report from a family member? Another example of a valid study is one whose objective is to determine if dietary fat intake affects blood cholesterol level cholesterol level being the outcome and in which the cholesterol level is measured from fasting blood samples that are all sent to the same laboratory.

An example of a "no" would be self-report by subjects that they had a heart attack, or self-report of how much they weight if body weight is the outcome of interest. Blinding or masking means that the outcome assessors did not know whether the participants received the intervention or were exposed to the factor under study. To answer the question above, the reviewers examined articles for evidence that the person s assessing the outcome s was masked to the participants' intervention or exposure status.

Sometimes the person applying the intervention or measuring the exposure is the same person conducting the outcome assessment.

In this case, the outcome assessor would not likely be blinded to the intervention or exposure status. In assessing this criterion, the reviewers determined whether it was likely that the person s conducting the outcome assessment knew the exposure status of the study participants. If not, then blinding was adequate.

An example of adequate blinding of the outcome assessors is to create a separate committee whose members were not involved in the care of the patient and had no information about the study participants' exposure status.

Using a study protocol, committee members would review copies of participants' medical records, which would be stripped of any potential exposure information or personally identifiable information, for prespecified outcomes. Higher overall followup rates are always desirable to lower followup rates, although higher rates are expected in shorter studies, and lower overall followup rates are often seen in longer studies. Usually an acceptable overall followup rate is considered 80 percent or more of participants whose interventions or exposures were measured at baseline.

However, this is a general guideline. In accounting for those lost to followup, in the analysis, investigators may have imputed values of the outcome for those lost to followup or used other methods.

For example, they may carry forward the baseline value or the last observed value of the outcome measure and use these as imputed values for the final outcome measure for research participants lost to followup. Were formal statistical tests used to assess the significance of the changes in the outcome measures between the before and after time periods? The reported study results should present values for statistical tests, such as p values, to document the statistical significance or lack thereof for the changes in the outcome measures found in the study.

Were the outcome measures for each person measured more than once during the course of the before and after study periods? Multiple measurements with the same result increase confidence that the outcomes were accurately measured. Group-level interventions are usually not relevant for clinical interventions such as bariatric surgery, in which the interventions are applied at the individual patient level. In those cases, the questions were coded as "NA" in the assessment tool.

The questions in the quality assessment tool were designed to help reviewers focus on the key concepts for evaluating the internal validity of a study. They are not intended to create a list from which to add up items to judge a study's quality. Internal validity is the extent to which the outcome results reported in the study can truly be attributed to the intervention or exposure being evaluated, and not to biases, measurement errors, or other confounding factors that may result from flaws in the design or conduct of the study.

In other words, what is the ability of the study to draw associative conclusions about the effects of the interventions or exposures on outcomes?

Critical appraisal of a study involves considering the risk of potential for selection bias, information bias, measurement bias, or confounding the mixture of exposures that one cannot tease out from each other. High risk of bias translates to a rating of poor quality; low risk of bias translates to a rating of good quality. In addition, the more attention in the study design to issues that can help determine if there is a causal relationship between the exposure and outcome, the higher quality the study.

These issues include exposures occurring prior to outcomes, evaluation of a dose-response gradient, accuracy of measurement of both exposure and outcome, and sufficient timeframe to see an effect. Generally, when reviewers evaluate a study, they will not see a "fatal flaw," but instead will find some risk of bias.

By focusing on the concepts underlying the questions in the quality assessment tool, reviewers should ask themselves about the potential for bias in the study they are critically appraising. For any box checked "no" reviewers should ask, "What is the potential risk of bias resulting from this flaw in study design or execution? The best approach is to think about the questions in the assessment tool and how each one reveals something about the potential for bias in a study.

Specific rules are not useful, as each study has specific nuances. In addition, being familiar with the key concepts will help reviewers be more comfortable with critical appraisal. Examples of studies rated good, fair, and poor are useful, but each study must be assessed on its own.

Learn more about the development and use of Study Quality Assessment Tools. Study Quality Assessment Tools. Quality Assessment of Controlled Intervention Studies. Was the study described as randomized, a randomized trial, a randomized clinical trial, or an RCT? Was the method of randomization adequate i. Was the treatment allocation concealed so that assignments could not be predicted? Were study participants and providers blinded to treatment group assignment? Were the people assessing the outcomes blinded to the participants' group assignments?

Were the groups similar at baseline on important characteristics that could affect outcomes e. Was the differential drop-out rate between treatment groups at endpoint 15 percentage points or lower? Was there high adherence to the intervention protocols for each treatment group? Were other interventions avoided or similar in the groups e. Were outcomes assessed using valid and reliable measures, implemented consistently across all study participants?

Were outcomes reported or subgroups analyzed prespecified i. Were all randomized participants analyzed in the group to which they were originally assigned, i. Question 1. Described as randomized Was the study described as randomized? A study does not satisfy quality criteria as randomized simply because the authors call it randomized; however, it is a first step in determining if a study is randomized Questions 2 and 3.

Treatment allocation—two interrelated pieces Adequate randomization: Randomization is adequate if it occurred according to the play of chance e. Blinding Blinding means that one does not know to which group—intervention or control—the participant is assigned. Question 6. Similarity of groups at baseline This question relates to whether the intervention and control groups have similar baseline characteristics on average especially those characteristics that may affect the intervention or outcomes.

Questions 7 and 8. Dropout "Dropouts" in a clinical trial are individuals for whom there are no end point measurements, often because they dropped out of the study and were lost to followup.

Question 9. Adherence Did participants in each treatment group adhere to the protocols for assigned interventions? Question Avoid other interventions Changes that occur in the study outcomes being assessed should be attributable to the interventions being compared in the study. Outcome measures assessment What tools or methods were used to measure the outcomes in the study? Power calculation Generally, a study's methods section will address the sample size needed to detect differences in primary outcomes.

Prespecified outcomes Investigators should prespecify outcomes reported in a study for hypothesis testing—which is the reason for conducting an RCT. Intention-to-treat analysis Intention-to-treat ITT means everybody who was randomized is analyzed according to the original group to which they are assigned. General Guidance for Determining the Overall Quality Rating of Controlled Intervention Studies The questions on the assessment tool were designed to help reviewers focus on the key concepts for evaluating a study's internal validity.

Is the review based on a focused question that is adequately formulated and described? Were eligibility criteria for included and excluded studies predefined and specified? Did the literature search strategy use a comprehensive, systematic approach?

Were titles, abstracts, and full-text articles dually and independently reviewed for inclusion and exclusion to minimize bias? Was the quality of each included study rated independently by two or more reviewers using a standard method to appraise its internal validity? Were the included studies listed along with important characteristics and results of each study? Was publication bias assessed? Was heterogeneity assessed? This question applies only to meta-analyses. Focused question The review should be based on a question that is clearly stated and well-formulated.

Question 2. Eligibility criteria The eligibility criteria used to determine whether studies were included or excluded should be clearly specified and predefined. Literature search The search strategy should employ a comprehensive, systematic approach in order to capture all of the evidence possible that pertains to the question of interest.

Manual searches of references found in articles and textbooks should supplement the electronic searches. Additional search strategies that may be used to improve the yield include the following: Studies published in other countries Studies published in languages other than English Identification by experts in the field of studies and articles that may have been missed Search of grey literature, including technical reports and other papers from government agencies or scientific groups or committees; presentations and posters from scientific meetings, conference proceedings, unpublished manuscripts; and others.

Searching the grey literature is important whenever feasible because sometimes only positive studies with significant findings are published in the peer-reviewed literature, which can bias the results of a review. Dual review for determining which studies to include and exclude Titles, abstracts, and full-text articles when indicated should be reviewed by two independent reviewers to determine which studies to include and exclude in the review.

Question 5. Quality appraisal for internal validity Each included study should be appraised for internal validity study quality assessment using a standardized approach for rating the quality of the individual studies. List and describe included studies All included studies were listed in the review, along with descriptions of their key characteristics. Question 7. Publication bias Publication bias is a term used when studies with positive results have a higher likelihood of being published, being published rapidly, being published in higher impact journals, being published in English, being published more than once, or being cited by others.

Reviewers assessed and clearly described the likelihood of publication bias. Question 8. Heterogeneity Heterogeneity is used to describe important differences in studies included in a meta-analysis that may make it inappropriate to combine the studies.

For example: Should a study evaluating the effects of an intervention on CVD risk that involves elderly male smokers with hypertension be combined with a study that involves healthy adults ages 18 to 40?

Clinical Heterogeneity Should a study that uses a randomized controlled trial RCT design be combined with a study that uses a case-control study design? Methodological Heterogeneity Statistical heterogeneity describes the degree of variation in the effect estimates from a set of studies; it is assessed quantitatively. Was the research question or objective in this paper clearly stated?

Was the study population clearly specified and defined? Were all the subjects selected or recruited from the same or similar populations including the same time period? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants? Was a sample size justification, power description, or variance and effect estimates provided? For the analyses in this paper, were the exposure s of interest measured prior to the outcome s being measured?

Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome e. Were the exposure measures independent variables clearly defined, valid, reliable, and implemented consistently across all study participants?

Was the exposure s assessed more than once over time? Were the outcome measures dependent variables clearly defined, valid, reliable, and implemented consistently across all study participants?

Were the outcome assessors blinded to the exposure status of participants? Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure s and outcome s? Research question Did the authors describe their goal in conducting this research? Questions 2 and 3. Study population Did the authors describe the group of people from which the study participants were selected or recruited, using demographics, location, and time period?

Groups recruited from the same population and uniform eligibility criteria Were the inclusion and exclusion criteria developed prior to recruitment or selection of the study population? Sample size justification Did the authors present their reasons for selecting or recruiting the number of people included or analyzed?

Exposure assessed prior to outcome measurement This question is important because, in order to determine whether an exposure causes an outcome, the exposure must come before the outcome. Sufficient timeframe to see an effect Did the study allow enough time for a sufficient number of outcomes to occur or be observed, or enough time for an exposure to have a biological effect on an outcome?

Different levels of the exposure of interest If the exposure can be defined as a range examples: drug dosage, amount of physical activity, amount of sodium consumed , were multiple categories of that exposure assessed?

Exposure measures and assessment Were the exposure measures defined in detail? Repeated exposure assessment Was the exposure for each person measured more than once during the course of the study period? Outcome measures Were the outcomes defined in detail? Blinding of outcome assessors Blinding means that outcome assessors did not know whether the participant was exposed or unexposed.

Followup rate Higher overall followup rates are always better than lower followup rates, even though higher rates are expected in shorter studies, whereas lower overall followup rates are often seen in studies of longer duration. Statistical analyses Were key potential confounding variables measured and adjusted for, such as by statistical adjustment for baseline differences? Some general guidance for determining the overall quality rating of observational cohort and cross-sectional studies The questions on the form are designed to help you focus on the key concepts for evaluating the internal validity of a study.

Quality Assessment of Case-Control Studies. Was the research question or objective in this paper clearly stated and appropriate? Did the authors include a sample size justification? This critical appraisal tool can be used when doing knowledge synthesis. It provides a standardized approach to assessing overall study quality based on eight categories and to developing recommendations for study findings. The manual walks users through the tool and provides guidance on how to rate methodological quality for each of the questions in the eight sections of the tool:.

Once all questions have been answered, users rate the overall methodological quality of the research article. Thomas B. Worldviews Evidence Based Nursing , 1 3 , These summaries are written by the NCCMT to condense and to provide an overview of the resources listed in the Registry of Methods and Tools and to give suggestions for their use in a public health context.

We have provided the resources and links as a convenience and for informational purposes only; they do not constitute an endorsement or an approval by McMaster University of any of the products, services or opinions of the external organizations, nor have the external organizations endorsed their resources and links as provided by McMaster University. McMaster University bears no responsibility for the accuracy, legality or content of the external sites.

Briefing note: Decisions, rationale and key findings summary.