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Year : 2008  |  Volume : 52  |  Issue : 5  |  Page : 596 Table of Contents     

Analyzing Retrospective Data

Senior Prof. & Head, Department of Anaesthesiology, R.N.T.Medical College, Udaipur (Raj.), India

Date of Web Publication19-Mar-2010

Correspondence Address:
Pramila Bajaj
25, Polo Ground, Udaipur (Raj.)
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Source of Support: None, Conflict of Interest: None

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How to cite this article:
Bajaj P. Analyzing Retrospective Data. Indian J Anaesth 2008;52:596

How to cite this URL:
Bajaj P. Analyzing Retrospective Data. Indian J Anaesth [serial online] 2008 [cited 2020 Oct 27];52:596. Available from: https://www.ijaweb.org/text.asp?2008/52/5/596/60684

The decision to perform a clinical intervention is dependent upon the clinician's prior experience, clini­cal findings and reading (or interpretation) of the litera­ture, and the assessment of whether the probability of disease reaches some threshold.

To interpret the literature and apply the principles of evidence-based medicine, it is important to under­stand the strengths and limitations of the different study designs and their applicability to a given clinical situa­tion. The gold standard for evidence of causation and justification for action is the prospective randomized clinical trial (RCT). RCTs have defined inclusion and exclusion criteria, treatment protocols, and out- comes of interest. They are usually either single of double-blind (both patient and physician) and are designed to test the effect of a drug or intervention.

Randomized clinical trials derive their strength from an evidence-based perspective because of their high degree of internal validity i.e., the randomization scheme and use of placebo (or accepted alternative treatments) provide strong evidence that the results are related to the intervention. If performed properly and with a suf­ficient sample size, the randomization should ensure that all important variables are distributed evenly, even uni­dentified confounders. Importantly, these trials have a lower degree of external validity because the interven­tion may not behave in the same manner as when it is diffused into a more heterogeneous population in whom treatment is not defined. Therefore, it is important to determine if the results of the study can be applied to the specific clinical situation of interest.

In many instances, there is insufficient evidence to justify a randomized clinical trial or it is important to determine how an intervention works in a different popu­lation than previously studied in an RCT. In these situ­ations, cohort studies can be utilized to study the ques­tion of interest. The analysis of retrospective studies, the approach can also be applied to any cohort study, These types of studies are frequently "hypothesis gen­erating" rather than designed to prove or disprove a hypothesis (the goal of the RCT). Prospective cohort studies involve the identification of a group of subjects who are followed over time for the occurrence of an outcome. The goal is to determine those patients who develop the outcome, and those factors which are as­sociated with the development of morbidity or mortal­ity can be discerned. An example of a prospective co­hort study identifying factors associated with perioperative cardiac morbidity and mortality is that of Goldman and colleagues, which led to the develop­ment of the cardiac risk index [1] .

Another example of a prospective cohort study is one in which patients with a known disease are studied for the development of predefined outcomes. Such stud­ies provide the natural history of patients with the dis­ease. An example would be studies of patients who have sustained a myocardial infarction, the importance of which is that the optimal time between the infarct and surgery can be determined [2],[3],[4] .

An important strength of any prospectively col­lected data is that the method of surveillance for an outcome of interest can be defined a priori, whereas this may not be true in retrospective studies. For ex­ample, studies which focus on the incidence of perioperative myocardial infarction are dependent both on the definition of an event and the frequency with which surveillance laboratories are obtained to detect that event.

Although prospective cohort studies have great value in identifying risk factors for the outcome of in­terest, there are significant limitations. The selection of the cohort of interest can significantly impact the results obtained. The larger the cohort, the more the results can be generalized. A second bias is that many patients may be lost to follow-up. In peri operative studies, this may not be an important issue for short-term outcomes. Finally, the importance of a risk factor depends upon the completeness of the data. For example, if the pres­ence of severe angina was not included in the data­base, then it could not be a risk factor and other fac­tors may appear to be more important [1] .

Evidence gathered from retrospective trials is con­sidered weaker than prospective studies, but may of­fer an excellent means of further generating hypothesis without collecting new data [5] . Unlike prospective stud­ies, retrospective studies are also totally dependent on the data collected in the medical record or billing (dis­charge) data. In many instances, identification of the outcome of interest was not performed in a systematic method. For example, the frequency of obtaining elec­trocardiograms and serial biomarkers was not consis­tent in a study with peri operative myocardial infarction as an outcome.

The major issue in evaluating retrospective data is the potential influence of confounders. Bias in retro­spective studies is a serious threat to the validity of the findings. Bias can come in a myriad of forms, and it may be impossible for statistical analysis to detect and eliminate bias. The traditional method to statistically adjust for bias is the use of multivariate analysis.

Multivariate analysis provides an estimate of the relationship between the risk factor (or intervention) and the outcome after adjusting for the differences of known or suspected confounders between the risk fac­tor (or treatment) groups. Multivariate analysis can in­volve three different types of analysis: multiple linear regression for continuous outcomes (e.g., total minutes of anaesthesia, total cost), multiple logistic regression for categorical or dichotomous outcomes (e.g., myo­cardial infarction or death), and proportional hazards regression for time to event outcomes (e.g., time to death). In a multiple regression analysis, a regression equation is developed that relates the outcome of in­terest all of the explanatory variables in the study.

Recently, propensity scores are increasingly be­ing used to help determine whether the outcome differ­ences seen are true effects of the treatment or just a sign that the risk factors for the outcome were not evenly distributed between the groups [6] . Propensity scores are different from regression analyses because they take into account the variable's influence on the likelihood for the subject to receive treatment, the variable's im­pact on outcome, and the variable's impact on the re­lationship between treatment (or non treatment) and outcome. Such analyses have increased importance in studies where randomization is impossible or impracti­cal.

In effect, propensity score analysis attempts to reconstruct a situation similar to randomization. The propensity score is calculated by building a regression model with the treatment as the dependent variable. In cohort studies, the chance of receiving different treat­ments is frequently a function of different baseline char­acteristics such as age and comorbidities. Therefore, the different treatment groups may have differing baseline characteristics. As demonstrated above, multivariate analysis evaluates the influence of these characteristics on the outcome. In propensity analysis, patients with a similar chance of undergoing the treatments are com­pared.

Finally, the propensity score can actually be uti­lized a regression model. In such a manner, the propen­sity score can be the only confounding variable. In in­terpreting the literature, the reader of a propensity score analysis should ask two questions:

1. Matching on the propensity is intended to bal­ance observed prognostic variables. Did it? The au­thors should include a table showing it did.

2. Propensity score only balance the prognostic variables used to construct the score-the variables in the table in Ref. 1. Did the authors fail to measure some important variable? Are there important variables not in the table?

If these conditions for question 1 are met and there is little likelihood that important variable were not mea­sured, then it is likely that the propensity analysis did prove good evidence of a strong association. Some authors have attempted to use sensitivity analysis to determine if some unmeasured variable would change the results.

   References Top

1.Goldman L, Caldera DL, Nussbaum SR. Multifactorial index of cardiac risk in non cardiac surgical procedures. N Engl J Med 1977;297:845-50.  Back to cited text no. 1      
2.Tarhan S, Moffitt EA, Taylor WF, Giuliani ER. Myocar­dial infarction after general anesthesia. JAMA 1972;220:1451-4.  Back to cited text no. 2  [PUBMED]    
3.Rao TLK, Jacobs KH, El-Etr AA. Reinfarction following anesthesia in patients with myocardial infarction. An­esthesiology 1983;59:499-505.  Back to cited text no. 3      
4.Shah KB, Kleinman BS, Sami H, et al. Reevaluation of perioperative myocardial infarction in patients with prior myocardial infarction undergoing noncardiac opera­tions. Anesth Analg 1990;71:231-5.  Back to cited text no. 4  [PUBMED]  [FULLTEXT]  
5.Ochroch EA, Fleisher LA. Retrospective analysis: look­ing backward to point the way forward. Anesthesiology 2006;105:643-4.  Back to cited text no. 5  [PUBMED]  [FULLTEXT]  
6.Joffe MM, Rosenbaum PR. Invited commentary: pro­pensity scores. Am J Epidemiol 1999;150:327-33.  Back to cited text no. 6  [PUBMED]  [FULLTEXT]  


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