Perspectives on Hematology, Health Care, and The Profession of Pharmacy.

Causal Analysis in Observation Studies

May 20, 2018 Uncategorized 1 comment

Criteria for A Confounding Factor

We can summarize thus far with the observation that for a variable to be a confounder, it must have three necessary (but not sufficient or defining) characteristics, which we will discuss in detail. We will then point out some limitations of these characteristics in defining and identifying confounding.

A confounding factor must be extraneous risk factor for the disease.

As mentioned earlier, a potential confounding factor need not be an actual cause of the disease, but if it is not, it must be a surrogate for an actual cause of the disease other than exposure. This condition implies that the association between the potential confounder and the disease must occur within levels of the study exposure. In particular, a potentially confounding factor must be a risk factor within the reference level of the exposure under study. The data may serve as a guide to the relation between the potential confounder and the disease, but it is the actual relation between the potentially confounding factor and disease, not the apparent relation observed in the data, that determines whether confounding can occur. In large studies, which are subject to less sampling error, we expect the data to reflect more closely the underlying relation, but in small studies the data are a less reliable guide, and one must consider other, external evidence (“prior knowledge”) regarding the relation of the factor to the disease.

The following example illustrates the role that prior knowledge can play in evaluating confounding. Suppose that in a cohort study of airborne glass fibers and lung cancer, the data show more smoking and more cancers among the heavily exposed but no relation between smoking and lung cancer within exposure levels. The latter absence of a relation does not mean that an effect of smoking was not confounded (mixed) with the estimated effect of glass fibers: It may be that some or all of the excess cancers in the heavily exposed were produced solely by smoking, and that the lack of a smoking-cancer association in the study cohort was produced by an unmeasured confounder of that association in this cohort, or by random error.

As a converse example, suppose that we conduct a cohort study of sunlight exposure and melanoma. Our best current information indicates that, after controlling for age and geographic area of residence, there is no relation between Social Security number and melanoma occurrence. Thus, we would not consider Social Security number a confounder, regardless of its association with melanoma in the reference exposure cohort, because we think it is not a risk factor for melanoma in this cohort, given age and geographic area (i.e., we think Social Security numbers do not affect melanoma rates and are not markers for some melanoma risk factor other than age and area). Even if control of Social Security number would change the effect estimate, the resulting estimate of effect would be less accurate than one that ignores Social Security number, given our prior information about the lack of real confounding by social security number.

Nevertheless, because external information is usually limited, investigators often rely on their data to infer the relation of potential confounders to the disease. This reliance can be rationalized if one has good reason to suspect that the external information is not very relevant to one’s own study. For example, a cause of disease in one population will be causally unrelated to disease in another population that lacks complementary component causes. A discordance between the data and external information about a suspected or known risk factor may therefore signal an inadequacy in the detail of information about interacting factors rather than an error in the data. Such an explanation may be less credible for variables such as age, sex, and smoking, whose joint relation to disease are often thought to be fairly stable across populations. In a parallel fashion, external information about the absence of an effect for a possible risk factor may be considered inadequate, if the external information is based on studies that had a considerable bias toward the null.

A confounding factor must be associated with the exposure under study in the source population (the population at risk from which the cases are derived).

To produce confounding, the association between a potential confounding factor and the exposure must be in the source population of the study cases. In a cohort study, the source population corresponds to the study cohort and so this proviso implies only that the association between a confounding factor and the exposure exists among subjects that compose the cohort. Thus, in cohort studies, the exposure-confounder association can be determined from the study data alone and does not even theoretically depend on prior knowledge if no measurement error is present.

When the exposure under study has been randomly assigned, it is sometimes mistakenly thought that confounding cannot occur because randomization guarantees exposure will be independent of (unassociated with) other factors. Unfortunately, this independence guarantee is only on average across repetitions of the randomization procedure. In almost any given single randomization (allocation), including those in actual studies, there will be random associations of the exposure with extraneous risk factors. As a consequence, confounding can and does occur in randomized trials. Although this random confounding tends to be small in large randomized trials, it will often be large within small trials and within small subgroups of large trials. Furthermore, heavy non adherence or noncompliance (failure to follow the assigned treatment protocol) or drop-out can result in considerable nonrandom confounding, even in large randomized trials.

In a case-control study, the association of exposure and the potential confounder must be present in the source population that gave rise to the cases. If the control  series is large and there is no selection bias or measurement error, the controls will provide a reasonable  estimate of the association between the potential confounding variable and the exposure in the source population and can be checked with the study data. In general, however, the controls may not adequately estimate the degree of association between the potential confounder and the exposure in the source population that produced the study cases. If information is available on this population association, it can be used to adjust findings from the control series. Unfortunately, reliable external information about the associations among risk factors in the source population is seldom available. Thus, in case-control studies, concerns about the control group will have to be considered in estimating the association between the exposure and the potentially confounding factor, for example, via bias analysis.

Consider a nested case-control study of occupational exposure to airborne glass fibers and the occurrence of lung cancer that randomly sampled cases and controls from cases and persons at risk in an occupational cohort. Suppose that we knew the association of exposure and smoking in the full cohort, as we might if this information were recorded for the entire cohort. We could then use the discrepancy between the true association and the exposure-smoking association observed in the controls as a measure of the extent to which random sampling had failed to produce representative controls. Regardless of the size of this discrepancy, if there were no association between smoking and exposure in the source cohort, smoking would not be a true confounder (even if it appeared to be one in the case-control data), and the the unadjusted estimate would be the best  available estimate. More, generally, we could use any information on the entire cohort to make adjustments to the case-control estimate, in a fashion analogous to two-stage studies.

 

Unfinished, keep updating …

The Summary of Japanese Grammar

May 16, 2018 Japanese, Uncategorized No comments ,

I wrote this thread because I want to intense the study and memorization of Japanese grammar, simply keeping me learning. This thread keeps updating. Hopefully what has been written here could help other Japanese learners.


Grammar Lesson 1

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The function of this grammar structure is to mean “It is”, “I am”, “He is”, etc. The Y is what after the predicate, and the X next to the は is the subject.

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Examples,

じゅうにじはんです (It) is half past twelve.

がくせいです (I) am a student.

にほんごです (My major) is the Japanese language.

山下先生は桜大学の学生でした Mr. Yamashita was a student at Sakura University.

あれは日本の映画じゃなかったです That was not a Japanese movie.

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By adding か after the predicate です, the sentence is transformed to a “yes / no” question form. In addition, by expanding the predicate into the structure of なんxxxですか , the meaning of the sentence become “what is”.

Examples,

りゅうがくせいですか (Are you) an international student?

せんこうはなんですか What is your major?

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の is a particle that connects two nouns. The noun after の expressed the main idea and the one before is the specific characteristic of the main idea.


Grammar Lesson 3

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There are generally three types of verbs and Japanese verbs exist in three forms, including: 1) dictionary forms, 2) the present tense affirmative forms, and 3) the present tense negative forms.

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There are three subtypes of dictionary forms, the “ru-verbs”, the “u-verbs”, and the irregular verbs.

The past tense forms of verbs look like the following.

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Examples,

メアリーさんは九時ごろうさに帰りました Mary returned home at about nine.

私は昨日日本語を勉強しませんでした I did not study Japanese yesterday.

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Nouns used in sentences generally be followed by particles, which indicate the relations that the nouns bear to the verbs.

 The particle を indicates “direct objects,” the kind of things that are directly involved in, or affected by, the event. Note that this particle is pronounced “o”.

Examples,

コーヒーを飲みます I drink coffee.

音楽を聴きます I listen to music.

テレビをます I watch TV.

 The paticle で indicates where the event described by the verb takes place.

Examples,

図書館で本を読みます I will read books in the library.

うちでテレビを見ます I will watch TV at home.

 The particle に has many meanings, but there here we focus on two: 1) the goal toward which things move (location), and 2) the time at which an event takes place.

Examples,

私は今日学校に行きません I will not go to school today.

私はうちに帰ります I will return home.

日曜日に京都に行きます I will go to Kyoto on Sunday.

十一時に寝ます I will go to bed at eleven.

十一時ごろ(に)寝ます I will go to bed at about eleven.

私は今日学校へ行きません I will not go to school today.

私はうちへ帰ります I will return home.

You do not use the particle に with 1) time expressions defined relative to the present moment, such as “today,” and “tomorrow,” 2) expressions describing regular intervals, such as “every day,” and 3) the word for “when.”

Examples,

明日きます I will come tomorrow.

毎晩テレビを見ます I watch TV every evening.

いつ行きますか When will you go?

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You can use ませんか (= the present tense negative verb, plus the question particle) to extend an invitation. It should be noted that its affirmative counterpart, ますか, cannot be so used.

昼ご飯を食べませんか What do you say to having lunch with me?

テニスをしませんか Will you play tennis with me?

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Japanese sentences are fairly flexible in the arrangement of elements that appear in them. Generally, sentences are made up of several noun-particle sequences followed by a verb or an adjective, which in turn is often followed by a sentence-final particle such as か, ね, orよ. Among the noun-particle sequences, their relative orders are to a large extent free. A typical sentence, therefore, looks like the following, but several other arrangements of non-particle sequences are also possible.

私は今日図書館で日本語を勉強します I will study Japanese in the library today.

私はよく七時ごろうちへ帰ります I often go back home at around seven.

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You can add a frequency adverb such as 毎日, よく, ときどき to a sentence to describe how often you do something.

Examples,

私はときどき喫茶店に行きます I sometimes go to a coffee shop.

私はぜんぜんテレビを観ません I do not watch TV at all.

たけしさんはあまり勉強しません Takeshi does not study much.

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The particle は presents the topic of one’s utterance. It puts forward the item that you want to talk about and comment on. A topic phrase, however, need not be the subject of a sentence. We see three sentences in the dialogue of this lesson where non subject phrases are made topics with the help of the particle は.

メアリーさん、週末はたいて何をしますか Mary, what do you usually do on the weekend?

今日は京都に行きます I’m going to Kyoto today.

In the above two examples, は promotes time expressions as the topic of each sentence. Its effects can be paraphrased like this: “Let’s talk about weekends; what do you do on weekends?” “Let me say what I will do today; I will go to Kyoto.”


Grammar Lesson 4

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Xがあります means “there is/are X (nonliving thing).” The particle が introduces, or presents, the item X. There are some rules for this verb. First, it calls for the particle に for the place description. Second, place description usually comes at the beginning of the sentence. Third, the thing description is usually followed by the particle が.

You can also use あります to say that you have or own something. Besides, you can use あります when you want to say that an event will take place.

Examples,

時間があります (I) have time.

時間がありますか (Do you) have time?

時間がありません (I don’t) have time.

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The Japanese version of “X is in front of Y” looks like

XはYの前です

Examples,

あのデパートの前です It’s in front of that department store.

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銀行は図書館のとなりです The bank is next to the library.

かさはテーブルの下です The umbrella is under the table.

レストランはデパート病院の間です The restaurant is between the department store and the hospital.

One can use any of the above location words together with a verb to describe an event that occur in the place.

私はモスバーガーの前でメアリーさんを待ちました I waited for Mary in front of the Mom Burger place.

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The duration of an activity is expressed with a bare noun, like 一時間. Such a noun stands alone (that is, not followed by an particle).

Examples,

メアリーさんはそこでたけしさんを一時間待ちました Mary waited for Takeshi there for an hour.

私は昨日日本語を三時間くらい勉強しました I studies Japanese for about three hours yesterday.

昨日7時間半寝ました (I) slept for seven and a half hours last night.

Type I and Type II Error in Statistics

March 21, 2018 Clinical Research, Evidence-Based Medicine, Medical Statistics, Research No comments , , , , ,

We often use inferential statistics to make decisions or judgements about the value of a parameter, such as a population mean. For example, we might need to decide whether the mean weight, 𝜇, of all bags of pretzels packaged by a particular company differs from the advertised weight of 454 grams, or we might want to determine whether the mean age, 𝜇, of all cars in use has increased from the year 2000 mean of 9.0 years. One of the most commonly used methods for making such decisions or judgments is to perform a hypothesis test. A hypothesis is a statement that something is true. For example, the statement “the mean weight of all bags of pretzels packaged differs from the advertised weight of 454 g” is a hypothesis.

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Typically, a hypothesis test involves two hypotheses: the null hypothesis and the alternative hypothesis (or research hypothesis), which we define as follows. For instance, in the pretzel packaging example, the null hypothesis might be “the mean weight of all bags of pretzels packaged equals the advertised weight of 454 g,” and the alternative hypothesis might be “the mean weight of all bags of pretzels packaged differs from the advertised weight of 454 g.”

The first step in setting up a hypothesis test is to decide on the null hypothesis and the alternative hypothesis. Generally, the null hypothesis for a hypothesis test concerning a population mean, 𝜇, alway specifies a single value for that parameter. Hence, we can express the null hypothesis as

H0: 𝜇 = 𝜇0

The choice of the alternative hypothesis depends on and should reflect the purpose of the hypothesis test. Three choices are possible for the alternative hypothesis.

  • If the primary concern is deciding whether a population mean, 𝜇, is different from a specific value 𝜇0, we express the alternative hypothesis as, Ha ≠ 𝜇0. A hypothesis test whose alternative hypothesis has this form is called a two-tailed test.
  • If the primary concern is deciding whether a population mean, 𝜇, is less than a specific value 𝜇0, we express the alternative hypothesis as, Ha < 𝜇0. A hypothesis test whose alternative hypothesis has this form is called a left-tailed test.
  • If the primary concern is deciding whether a population mean, 𝜇, is greater than a specified value 𝜇0, we express the alternative hypothesis as, Ha > 𝜇0. A hypothesis test whose alternative hypothesis has this form is called a right-tailed test.

PS: A hypothesis test is called a one-tailed test if it is either left tailed or right tailed.

Screen Shot 2018 03 21 at 6 09 44 PMAfter we have chosen the null and alternative hypotheses, we must decide whether to reject the null hypothesis in favor of the alternative hypothesis. The procedure for deciding is roughly as follows. In practice, of course, we must have a precise criterion for deciding whether to reject the null hypothesis, which involves a test statistic, that is, a statistic calculated from the data that is used as a basis for deciding whether the null hypothesis should be rejected.

Type I and Type II Errors

In statistics, type I error is to reject the null hypothesis when it is in fact true; whereas type II error is not to reject the null hypothesis when it is in fact false. The probabilities of both type I and type II errors are useful (and essential) to evaluating the effectiveness of a hypothesis test, which involves analyzing the chances of making an incorrect decision. A type I error occurs if a true null hypothesis is rejected. The probability of that happening, the type I error probability, commonly called the significance level of the hypothesis test, is denote 𝛼. A type II error occurs if a false null hypothesis is not rejected. The probability of that happening, the type II error probability, is denote 𝛽.

Screen Shot 2018 03 21 at 6 29 58 PMIdeally, both type I and Type II errors should have small probabilities. Then the chance of making an incorrect decision would be small, regardless of whether the null hypothesis is true or false. We can design a hypothesis test to have any specified significance level. So, for instance, of not rejecting a true null hypothesis is important, we should specify a small value for 𝛼. However, in making our choice for 𝛼, we must keep Key Fact 9.1 in mind. Consequently, we must always assess the risks involved in committing both types of errors and use that assessment as a method for balancing the type I and type II error probabilities.

The significance level, 𝛼, is the probability of making type I error, that is, of rejecting a true null hypothesis. Therefore, if the hypothesis test is conducted at a small significance level (e.g., 𝛼 = 0.05), the chance of rejecting a true null hypothesis will be small. Thus, if we do reject the null hypothesis, we can be reasonably confident that the null hypothesis is false. In other words, if we do reject the null hypothesis, we conclude that the data provide sufficient evidence to support the alternative hypothesis.

However, we usually do not know the probability, 𝛽, of making a type II error, that is, of not rejecting a false null hypothesis. Consequently, if we do not reject the null hypothesis, we simply reserve judgement about which hypothesis is true. In other words, if we do not reject the null hypothesis, we conclude only that the data do not provide sufficient evidence to support the alternative hypothesis; we do not conclude that the data provide sufficient evidence to support the null hypothesis. In short, it might be true that there is a true difference but the power of the statistic procedure is not high enough to detect it.

Missing or Poor Quality Data in Clinical Trials

March 20, 2018 Clinical Trials, Research No comments ,

In most trials, participants have data missing for a variety of reasons. Perhaps they were not able to keep their scheduled clinic visits or were unable to perform or undergo the particular procedures or assessments. In some cases, follow-up of the participant was not completed as outlined in the protocol. The challenge is how to deal with missing data or data of such poor quality that they are in essence missing. One approach is to withdraw participants who have poor data completely from the analysis. However, the remaining subset may no longer be representative of the population randomized and there is no guarantee that the validity of the randomization has been maintained in this process.

Many methods to deal with this issue assume that the data are missing at random; that is, the probability of a measurement not being observed does not depend on what its value would have been. In some contexts, this may be a reasonable assumption, but for clinical trials, and clinical research in general, it would be difficult to confirm. It is, in fact, probably not a vlid assumption, as the reason the data are missing is often associated with the health status of the participant. Thus, during trial design and conduct, every effort must be made to minimize missing data. If the amount of missing data is relatively small, then the available analytic methods will probably be helpful. If the amount of missing data is substantial, there may be no method capable of rescuing the trial. Here, we discuss some of the issues that must be kept in mind when analyzing a trial with missing data.

Rubin provided a definition of missing data mechanisms. If data are missing for reasons unrelated to the measurement that would have been observed and unrelated to covariates, then the data are “missing completely at random.” Statistical analyses based on likelihood inference are valid when the data are missing at random or missing completely at random. If a measure or index allows a researcher to estimate the probability of having missing data, say in a participant with poor adherence to the protocol, then using methods proposed by Rubin and others might allow some adjustment to reduce bias. However, adherence, as indicated earlier, is often associated with a participant’s outcome and attempts to adjust for adherence can lead to misleading results.

If participants do not adhere to the intervention and also do not return for follow-up visits, the primary outcome measured may not be obtained unless it is survival or some easily ascertained event. In this situation, an intention-to-treat analysis is not feasible and no analysis is fully satisfactory. Because withdrawal of participants from the analysis is known to be problematic, one approach is to “impute” or fill in the missing data such that standard analyses can be conducted. This is appealing if the imputation process can be done without introducing bias. There are many procedures for imputation. Those based on multiple imputations are more robust than single imputation.

A commonly used single imputation method is to carry the last observed value forward. This method, also known as an endpoint analysis, requires the very strong and unverifiable assumption that all future observations, if they were available, would remain constant. Although commonly used, the last observation carried forward method is not generally recommended. Using the average value for all participants with available data, or using a regression model to predict the missing value are alternatives, but in either case, the requirement that the data be missing at random is necessary for proper inference.

A more complex approach is to conduct multiple imputations, typically using regression methods, and then perform a standard analysis for each imputation. The final analysis should take into consideration the variability across the imputations. As with single imputation, the inference based on multiple imputation depends on the assumption that the data are missing at random. Other technical approaches are not described here.

If the number of participants lost to follow-up differs in the study groups, the analysis of the data could be biased. For example, participants who are taking a new drug that has adverse effect may, as a consequence, miss scheduled clinic visits. Events may occur but be unobserved. These losses to follow-up would probably not be the same in the control group. In this situation, there may be a bias favoring the new drug. Even if the number lost to follow-up is the same in each study group, the possibility of bias still exists because the participants who are lost in one group may have quite different prognoses and outcomes than those in the other group.

An outlier is an extreme value significantly different from the remaining values. The concern is whether extreme values in the sample should be included in the analysis. This question may apply to a laboratory result, to the data from one of several areas in a hospital or from a clinic in a multi center trial. Removing outliers is not recommended unless the data can be clearly shown to be erroneous. Even though a value may be an outlier, it could be correct, indicating that on occasions an extreme result is possible. This fact could be very important and should not be ignored.

Systematic Review – Defining the Question

March 18, 2018 Evidence-Based Medicine, Research No comments , , , ,

Eligibility Criteria

The acronym PICO helps to serve as a reminder of the essential components of review question. One of the features that distinguish a systematic review from a narrative review is the pre-specification of criteria for including and excluding studies in the review (eligibility criteria). Eligibility criteria are  a combination of aspects of the clinical question plus specification of the types of studies that have addressed these questions. The participants, interventions and comparisons in the clinical question usually translate directly into eligibility criteria for the review. Outcomes usually are not part of the criteria for including studies: a Cochrane review would typically seek all rigorous studies of a particular comparison of interventions in a particular population of participants, irrespective of the outcomes measured or reported. However, some reviews do legitimately restrict eligibly to specific outcomes.

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Population

The criteria for considering types of people included in studies in a review should be sufficiently broad to encompass the likely diversity of studies, but sufficiently narrow to ensure that a meaningful answer can be obtained when studies are considered in aggregate. It is often helpful to consider the types of people that are of interest in two steps. First, the diseases or conditions of interest should be defined using explicit criteria for establishing their presence or not. Criteria that will force unnecessary exclusion of studies should be avoided. For example, diagnostic criteria that were developed more recently – which may be viewed as the current gold standard for diagnosing the condition of interest – will not have been used in earlier studies. Expensive or recent diagnostic tests may not be available in many countries or settings.

Second, the broad population and setting of interest should be defined. This involves deciding whether a special population group is of interest, determined by factors such as age, sex, race, educational status or the presence of a particular condition such as angina or shortness of breath. Interest may focus on a particular settings such as a community, hospital, nursing home, chronic care institution, or outpatient setting.

The types of participants of interest usually determine directly the participant-related eligibility criteria for including studies. However, pre-specification of rules for dealing with studies that only partially address the population of interest can be challenging.

Any restrictions with respect to specific population characteristics or settings should be based on a sound rationale. Focusing a review on a particular subgroup of people on the basis of their age, sex or ethnicity simply because of personal interests when there is no underlying biologic or sociological justification for doing so should be avoided.

Interventions

The second key component of a well-formulated question is to specify the interventions of interest and the interventions against which these will be compared (comparisons). In particular, are the interventions to be compared with an inactive control intervention, or with an active control intervention? When specifying drug interventions, factors such as the drug preparation, route of administration, dose, duration, and frequency should be considered. For more complex interventions (such as educational or behavioral interventions), the common or core features of the interventions will need to be defined. In general, it is useful to consider exactly what is delivered, at what intensity, how often it is delivered, who delivers it, and whether people involved in delivery of the intervention need to be trained. Review authors should also consider whether variation in the intervention (i.e., based on dosage/intensity, mode of delivery, frequency, duration etc) is so great that it would have substantially different effects on the participants and outcomes of interest, and hence may be important to restrict.

Outcomes

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Although reporting of outcomes should rarely determine eligibility of studies for a review, the third key component of a well-formulated question is the delineation of particular outcomes that are of interest. In general, Cochrane reviews should include all outcomes that are likely to be meaningful to clinicians, patients, the general public, administrators and policy makers, but should not include outcomes reported in included studies if they are trivial or meaningless to decision makers. Outcomes considered to be meaningful and therefore addressed in a review will not necessarily have been reported in individual studies. For example, quality of life is an important outcome, perhaps the most important outcome, for people considering whether or not to use chemotherapy for advanced cancer, even if the available studies are found to report only survival. Including all important outcomes in a review will highlight gaps in the primary research and encourage researchers to address these gaps in future studies.

Outcomes may include survival (mortality), clinical events (e.g., strokes or myocardial infarction), patient-reported outcomes (e.g., symptoms, quality of life), adverse events, burdens (e.g., demands on caregivers, frequency of tests, restrictions on lifestyle) and economic outcomes (e.g., cost and resource use). It is critical that outcomes used to assess adverse effects as well as outcomes used to assess beneficial effects are among those addressed by a review. If combinations of outcomes will be considered, these need to be specified. For example, if a study fails to make a distinction between non-fatal and fatal strokes, will these data be included in a meta-analysis if the question specifically related to stroke death?

Review authors should consider how outcomes may be measured, both in terms of the type of scale likely to be used and the timing of measurement. Outcomes may be measured objectively (e.g., blood pressure, number of strokes) or subjectively as rated by a clinical, patient, or carer (e.g., disability scales). It may be important to specify whether measurement scales have been published or validated. When defining the timing of outcome measurement, authors may consider whether all time frames or only selected time-points will be included in the review. One strategy is to group time-points into pre-specified intervals to represent “short-term”, “medium-term” and “long-term” outcomes and to take no more than one of each from each study for any particular outcome. It is important to give the timing of outcome measure considerable thought as it can influence the results of the review.

While all important outcomes should be included in Cochrane reviews, trivial outcomes should not be included. Authors need to avoid overwhelming and potentially misleading readers with data that are of little or no importance. In addition, indirect or surrogate outcome measures, such as laboratory results or radiologic results, are potentially misleading and should be avoided or interpreted with caution because they may not predict clinically important outcomes accurately. Surrogate outcomes may provide information on how a treatment might work but not whether it actually does work. Many interventions reduce the risk for a surrogate outcome but have no effect or have harmful effects on clinically relevant outcomes, and some interventions have no effect on surrogate measures but improve clinical outcomes.Screen Shot 2018 03 18 at 6 49 15 PM

Main Outcomes

Once a full list of relevant outcomes has been complied for the review, authors should prioritize the outcomes and select the main outcomes of relevance to the review question. The main outcomes are the essential outcomes for decision-making, and are those that would form the basis of a “Summary of findings” table. “Summary of findings” tables provide key information about the amount of evidence for important comparisons and outcomes, the quality of the evidence and the magnitude of effect. There should be no more than seven main outcomes, which should generally not include surrogate or interim outcomes. They should not be chosen on the basis of any anticipated or observed magnitude of effect, or because they are likely to have been addressed in the studies to be reviewed.

Primary Outcomes

Primary outcomes for the review should be identified from among the main outcomes. Primary outcomes are the outcomes that would be expected to be analyzed should the review identify relevant studies, and conclusions about the effects of the interventions under review will be based largely on these outcomes. There should in general be no more than three primary outcomes and they should include at least one desirable and at least one undesirable outcome (to assess beneficial and adverse effects respectively).

Secondary Outcomes

Main outcomes not selected as primary outcomes would be expected to be listed as secondary outcomes. In addition, secondary outcomes may include a limited number of additional outcomes the review intends to address. These may be specific to only some comparisons in the review. For example, laboratory tests and other surrogate measures may not be considered as main outcomes as they are less important than clinical endpoints in informing decisions, but they may be helpful in explaining effect or determining intervention integrity.

Types of Study

Certain study designs are more appropriate than others for answering particular questions. Authors should consider a priori what study designs are likely to provide reliable data with which to address the objectives of their review.

Because Cochrane reviews address questions about the effects of health care, they focus primarily on randomized trials. Randomization is the only way to prevent systematic differences between baseline characteristics of participants in different intervention groups in terms of both known and unknown (or unmeasured) confounders. For clinical interventions, deciding who receives an intervention and who does not is influenced by many factors, including prognostic factors. Empirical evidence suggests that, on average, non-randomized studies produce effect estimates that indicate more extreme benefits of the effects of health care than randomized trials. However, the extent, and even the direction, of the bias is difficult to predict.

Specific aspects of study design and conduct should also be considered when defining eligibility criteria, even if the review is restricted to randomized trials. For example, decisions over whether cluster-randomized trials and cross-over trials are eligible should be made, as should thresholds for eligibility based on aspects such as use of a placebo comparison group, evaluation of outcomes blinded to allocation, or a minimum period of follow-up. There will always be a trade-off between restrictive study design criteria (which might result in the inclusion of studies with low risk of bias, but which are very small in number) and more liberal design criteria (which might result in the inclusion of more studies, but which are at a higher risk of bias). Furthermore, excessively broad criteria might result in the inclusion of misleading evidence. If, for example, interest focuses on whether a therapy improves survival in patients with a chronic condition, it might be inappropriate to look at studies of very short duration, except to make explicit the point that they cannot address the question of interest.

Scope of Review Question

The questions addressed by a review may be broad or narrow in scope. For example, a review might address a broad question regarding whether anti platelet agents in general are effective in preventing all thrombotic events in humans. Alternatively, a review might address whether a particular anti platelet agent, such as aspirin, is effective in decreasing the risk of a particular thrombotic event, stroke, in elderly persons with a previous history of stroke.

Determining the scope of a review question is a decision dependent upon multiple factors including perspectives regarding a question’s relevance and potential impact; supporting theoretical, biologic and epidemiological information; the potential generalizability and validity of answers to the questions; and available resources.