Types of studies

• Primary  literature: Primary sources are original materials.  It is authored by researchers, contains original research data, and is usually published in a peer-reviewed journal.For Example, a clinical trial done by a pharmaceutical company would be a classic example of a primary literature. Another example. original research results in journals (e.g. NEJM).

• Secondary literature: Secondary literature consists of interpretations and evaluations of primary literature For example, review articles, systematic reviews, meta-analysis, practice guidelines, and  Up To Date ® all are considered as secondary literature.

• Tertiary Literature: Tertiary literature is compiling primary and secondary literature and creating a larger, broader view of clinical information. For example: Textbook on pharmacokinetics and critical care cornerstone book.

• Phase 0 (Pre-clinical): Testing of drug in Vitro (non-human subjects). Gather efficacy, toxicity, and pharmacokinetic information.

• Phase I: Represents the initial introduction of an Investigational New Drug (IND) into humans and typically involves 20–100 healthy volunteers. The goal of this type of study is to gain information on the pharmacokinetic and pharmacodynamics properties of the drug to help design well-controlled and robust phase II drug trials.

• Phase II: Conducted in no more than several hundred patients (100–300 patients with specific diseases). Evaluate the drug’s effectiveness for a particular indication in patients with the disease or condition under investigation, as well as to determine the common short-term adverse effects and risks associated with the drug.

• Phase III: involves the administration of the Investigational New Drug (IND) to several hundred to several thousand patients in different clinical settings to determine its safety, efficacy, and appropriate dosage. Efficacy compared to the gold standard treatment.

• Phase IV: Post marketing surveillance, to gather information on the drug’s effect in various populations and any adverse effects associated with long-term use after Investigational New Drug (IND) has been market.

• It is important to note that the general classification of ’literature review’ has three varieties: Narrative review, qualitative systematic review, and quantitative systematic review (meta-analysis).

• A systematic review is defined as “a review of the evidence on a clearly formulated question that uses systematic and explicit methods/criteria to identify, select and critically appraise relevant primary research, and to extract and analyze data from the studies that are included in the review.” 

• Not all systematic reviews contain meta-analysis. A systematic review may include a meta-analysis (quantitative synthesis) (Optional).

• What is the difference between Systematic review and meta-analysis? A systematic review answers a defined research question by collecting and summarizing all empirical evidence that fits pre-specified eligibility criteria. While meta-analysis is the use of statistical methods to summarize the results of these studies. Meta-analysis is a systematic review that uses quantitative methods to synthesize and summarize the results.

• Meta-analysis is a quantitative, formal, and statistical analysis used to systematically assess previous research studies that combine the results of multiple scientific studies o derive conclusions about that body of research. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. 

• The conclusion from meta-analysis is statistically stronger than the analysis of any single study, due to increased numbers of subjects, greater diversity among subjects, or accumulated effects and results. However, the examination of variability or heterogeneity in study results is also a critical outcome. In addition, If the studies utilized randomized controlled trials (RCT), combining several selected RCT results would be the highest level of evidence on the evidence hierarchy, followed by systematic reviews, which analyze all available studies on a topic.

• Effect size (ES) can be based on means,  proportions, ORs, RRs, HRs. While the weights are based on the amount of information carried out by each study. The p‐value is a poor surrogate for effect size  In primary studies, should report effect size. Meta‐analyses must work with effect size.

There is debate about the best practice for meta-analysis, however. there are five common steps.

1. Step 1: the research question. A clinical research question is identified and a hypothesis is proposed. 

2. Step 2: a systematic review. A systematic review (SR) is specifically designed to address the research question and conducted to identify all studies considered to be both relevant and of sufficiently good quality to warrant inclusion. Following databases are commonly used: MEDLINE, EMBASE, Cochrane L., Psychinfo, and CINAHL.

3. Step 3: data extraction. Once studies are selected for inclusion in the meta-analysis, summary data or outcomes are extracted from each study. 

4. Step 4: standardizations and weighting studies. These measures are usually called Effect Sizes and represent the difference in average scores between intervention and control groups. 

5. Step 5: final estimates of effect. The final estimates from a meta-analysis are often graphically reported in the form of a ‘Forest Plot’.

Heterogeneity

• To what extent the results of studies are inconsistent.  If confidence intervals for the results of individual studies have poor overlap "Generally indicates statistical heterogeneity".  Very high heterogeneity could mean that the studies have nothing in common and that there is no “real” true effect behind our data, meaning that it makes no sense to report the pooled effect at all (Borenstein et al. 2011).   

• Sources of Heterogeneity: Clinical heterogeneity (e.g. Participants, Interventions, Outcome definitions), methodological Heterogeneity (i.e. the way that studies were conducted), and statistical heterogeneity (i.e variation in true treatment effects in magnitude or direction). 

• Assessment of heterogeneity:

1. Visual testing for statistical heterogeneity - using Forest Plot: There should be an overlap between the confidence intervals for each effect estimate on the forest plot. if the overlap is poor, or there are outliers, then statistical heterogeneity may be likely.

2. Statistical testing for statistical heterogeneity: Null hypothesis: trials all have the same treatment effect in the population.  

• Chi-squared (χ2) test with P‐value. It assesses whether observed differences in results are compatible with chance alone.  A low P-value provides evidence of heterogeneity of intervention effects (variation in effect estimates beyond chance).  

• I2 tells us what proportion of the observed dispersion reflects differences in true scores rather than random sampling error. I2 is NOT a measure of absolute heterogeneity. Interpretation: if I2= 0% to 40%: might not be important; if I2= 30% to 60%: may represent moderate heterogeneity;  if I2=50% to 90%: may represent substantial heterogeneity;  if I2= 75% to 100%: considerable heterogeneity.  

Free software for meta-analysis:

• Meta-analysis add-In for Microsoft Excel

• Meta-Essentials

• RevMan

• R

• Open Meta-Analyst

• Forest plot viewer  

•  There are three types of narrative reviews of the literature: editorials, commentaries, and overview articles.

• Narrative reviews also known as unsystematic narrative reviews, have no standard format. They are not systematic and follow no specified protocol. 

• The purpose of the narrative review is to identify a few studies that describe a problem of interest. This type of literature review reports the author’s findings in a condensed format that typically summarizes the contents of each article.

• Narrative overviews are useful educational articles since they pull many pieces of information together into a readable format.

• Narrative reviews have no predetermined research question or specified search strategy, only a topic of interest.

• No clearly specified methods of identifying, selecting, and validating included information.

• Quantitative synthesis is rarely used to integrate the information from multiple studies.

Steps for Conducting a Narrative Literature Review: 

Step 1: Conduct a Search. The first step in writing a narrative overview is to perform a preliminary search of the literature.

Step 2: Identify Keywords.

Step 3: Review Abstracts and Articles.

Step 4: Document Results.

• Interventional in nature.

• Experimental design in which patients are assigned to two or more interventions.

• Intention-to-treat analysis is that study participants should be analyzed according to the groups in which they were randomized, even if they did not receive or comply with treatment. In contrast, "as treated" (or "per protocol") analysis in which subjects are analyzed according to the actual treatment they received.

• Observational, study groups are divided by treatment, followed over time, and the outcomes compared. Exposure to the treatment group must precede the outcome of interest. The exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.

• Cohort studies can be performed prospectively or retrospectively.

• Retrospective cohort studies are NOT the same as case-control studies.

• Helps to determine causality & incidence.

• The outcome measure in cohort studies is usually a risk ratio / relative risk (RR) or odds ratio (OR).

• Advantages: Used for evidence-based medicine; Disadvantages: Expensive, Long follow up, Drop out of cases, Information bias, Analytic bias, Bias in the assessment of the outcome.

• Retrospective in nature. Analytically, case-control studies work backward from outcome to exposure.

• Study participants are identified based on outcome (cases have the outcome; controls do not), and the two groups are compared for the presence of different exposures. This type of study investigates multiple exposures.

• Case–control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have that condition/disease (the "cases") with patients who do not have the condition/disease but are otherwise similar (the "controls"). 

• In case-control studies, data are not available to calculate the incidence rate of the disease being studied, and the actual relative risk cannot be determined. The measure of association between exposure and occurrence of disease in case-control studies is the so-called odds ratio. The main outcome measure in case-control studies is the odds ratio (OR); the ratio of odds of exposure in diseased subjects to the odds of exposure in the non-diseased. 

• Selection of Cases: based on same diagnostic criteria; Controls: based on matching criteria to cases. 

• Definition of a case: This should lead to accurate classification of diseased and non-diseased individuals. Should note, homogeneous disease entity by strict diagnostic criteria.

• Controls are a sample of the population that gave rise to the cases. Controls must be identified independently of exposure status. In which, members of the control group who gets the disease “would” end up as a case in the study.

• The most optimum case-to-control ratio is 1:1. It has been suggested that we can increase the number of controls to increase statistical power (if we have a limited number of cases) of the study. It has been argued that the increase in statistical power may be limited with greater than four control (4:1).

Advantage: 

1. Efficient for rare diseases.

2. Efficient for diseases with long induction and latent periods.

3. Can evaluate multiple exposures in relation to a disease.

4. Relatively quick and inexpensive

Disadvantages: 

1. Recall Bias, Selection bias, Interviewer bias (information bias), and Confounding. Confounding can be prevented or mitigated by matching & statistical techniques.

2. Can investigate only one disease outcome.

3. Inefficient for rare exposures.

4. Cannot directly compute incidence rates of disease in exposed and unexposed.

When to use case-control design:

• Little is known about the disease.

• Exposure data are difficult or expensive to obtain.

• Rare diseases.

• Disease with long induction and latent period.

• Dynamic underlying population.

• Groups are divided pre- and post implementation of some practice. Often used for evaluating the impact of a protocol.

• A cross-sectional study is defined as a type of observational study (Descriptive) that analyzes data of variables collected at one given point in time across a sample population or a pre-defined subset. Both exposure and outcomes are assessed simultaneously (at one point in time). 

• Often referred to as a snapshot study or prevalence study (e.g. survey).

• Give a picture of the prevalence of different diseases at a moment in time (Single point in time).

• Sampling: Procedure by which some members of a given population are selected as representatives of the entire population. Representativeness (Validity) of the sample is essential to generalize.

• In question formulation of the survey, each one question should measure one thing, only (e.g. Do you complain of fever and headache?).

• Advantages: Inexpensive, quick, simple; Disadvantages: Don't establish causality, recall bias, survival bias, and confounders.

A case study, also known as a case report, is an in depth or intensive study of a single individual or specific group, while a case series is a grouping of similar case studies / case reports together. They are generally descriptive studies based on qualitative data e.g. observations, interviews, questionnaires, diaries, personal notes or clinical notes.

There are many classifications for case series: (El-Gilany AH. What is case series?. Asp Biomed Clin Case Rep. 2018 Aug 17;1(1):10-15.)

A. Informal vs. Formal Case Series: 

• Informal case series: Cases are selected for specific reasons: best case, worse case, significant variations. The format of this kind of case series is: introduction; case 1, case 2, case 3, etc. (each case is presented as a short case description); discussion (cases will be compared to one another, related cases to the current literature, implications of the findings, teaching points and what changes in clinical practice this might engender). 

• Formal case series: Include all cases of a specific type, or with specific selection criteria, presented more like a cohort study than a single case report and its format is introduction methods, results, and discussion/conclusions. 

B. Consecutive vs. Non-Consecutive Case Series: 

• Consecutive case series: Includes all eligible patients identified by the researchers during the study period. The patients are treated in the order in which they are identified. Consecutiveness increases the quality of the case series. 

• Non-consecutive case series: Includes some, but not all, of the eligible patients identified by the researchers during the study period. 

C. Exposure or Outcome-Based Sampling: 

• Exposure-based sampling: Include all patients treated and have specific outcomes or adverse events. Sampling is based on both a specific outcome and the presence of specific exposure. 

• Outcome-based sampling: Includes patients with the specific outcome regardless of exposure. Thus neither absolute risk nor relative risk can be calculated. Selection is based only on a specific outcome, and data are collected on previous exposures. 

Questions/elements to be included in the presentation of the cases (Abdel-Hady El-Gilany.2018):

• What: The diagnosis or case definition showed be clear and applied equally to all individuals in the series. The case definition should mention the inclusion and exclusion criteria, which should be based on widely used validated definitions. If authors use their own criteria, definition and justification are necessary to enable readers to compare the studied population with their own patients.

• When: The date when the disease or death occurred (time).

• Where: The place where the person lived, worked etc (place).

• Who: The characteristics of the population (person). Noting the socio-demographic characteristics of a series of cases, as well as the temporal and spatial distributions can sometimes provide a clue to risk factors and hence help generate a hypothesis. This can be tested.