60 T.S. Riall

The goal of the chapter is to address major concepts in data analysis, providing the reader a foundation for analyzing and interpreting data applicable to both basic science and outcomes/health services research. It will provide a frame- work in which surgeons can interpret the literature, evaluate and review scientific articles, and evaluate study protocols, including identification of strengths and weaknesses of the study design and analysis, as well as potential errors. This information can then be used to analyze and interpret your own data or the data of others, communicate the results clearly, and apply the results to patient care.

Sources of Error in Medical Research

All research is susceptible to invalid conclusions resulting from confounding, bias, and chance. A confounder is a vari- able that is associated with both the predictor (or indepen- dent variable) and the outcome of interest (or dependent variable). This variable or risk factor may not be evenly dis- tributed between the control and study groups, producing a spurious association between the predictor and the outcome of interest. Common confounders in epidemiological or out- comes research include gender, age, socioeconomic status, comorbidities, and health behaviors. For example, if you study the relationship between coffee drinking and pancre- atic cancer, you might find a positive association (Fig. 5.1a). However, this association may be entirely explained by smoking status, a known risk factor for pancreatic cancer. If more coffee drinkers than controls are smokers, you will identify an incorrect association between coffee drinking and pancreatic cancer if you do not control for smoking.

Bias is nonrandom, systematic error in the design or con- duct of a study. Bias is unintentional and there are many types. Bias can occur in patient selection (selection bias and membership bias), study performance (information bias), patient follow-up (nonresponder bias),and outcome determi- nation (recall bias,detection bias,and interviewer bias).These types of bias are summarized in Table 5.1. Selection bias is

				Chapter 5.  Analyzing Your Data						61
a
					Smoking
			(unmeasured risk factor)
	Coffee drinking							Pancreatic cancer
(factor being studied)								(outcome)
b
	Active treatment vs.							Survival
	observation for lowand							(outcome)
	intermediated-risk
	prostate cancer
	(factor being studied)
						Patient comorbidities
						(unmeasured risk factor)

FIGURE 5.1  (a) Confounding. In this example, smoking acts as a con- founder. Smoking is associated with both coffee drinking (the factor being studied) and developing pancreatic cancer (outcome). If more coffee drinkers than controls are smokers, you will identify an incor- rect association between coffee drinking and pancreatic cancer if you do not control for smoking. (b) Selection bias. In this example, patient comorbidity is an unmeasured risk factor that is associated with the choice of treatment (the factor being studied). In patients with lowand intermediate-risk prostate cancer,it is difficult to com- pare active treatment versus observation because patient who are healthier are more likely to undergo active therapy and also more likely to live longer

common in observational studies, where treatment is not ran- domly allocated. Patients and their physicians select treat- ment based on a variety of measurable and unmeasurable characteristics and risk factors. For example, in patients with lowand intermediate-risk prostate cancer, it is difficult to compare active treatment versus observation because patient comorbidities are associated with the choice of treatment (Fig. 5.1b). Patients who are healthier are selected to undergo active therapy and are also more likely to live longer.

62	T.S. Riall
TABLE 5.1  Types of bias
Type of bias		Description
Prevalence or		Occurs when a condition is characterized by
incidence bias		early fatalities
Selection bias		Occurs when treatment assignments are made
		on the basis of certain characteristics of the
		patients such that the two groups are not
		similar
Membership bias		Occurs because one or more of the
		characteristics that cause people to belong to
		groups are related to the outcome of interest
Information bias		Occurs because of misclassification of the risk
		factor being assessed and/or misclassification
		of the disease or other outcome itself. It is a
		type of bias that occurs when measurement
		of information (e.g., exposure or outcome)
		differs among study groups
Nonresponder		Occurs when subjects fail to respond to
bias		a survey; responders often have different
		characteristics than nonresponders
Recall bias		Occurs when patients are asked to recall
		certain events; people in a group with an
		adverse outcome are more likely to remember
		certain events
Detection bias		Occurs when a new diagnostic technique
		is introduced that is capable of detecting a
		disease at an earlier stage
Interviewer bias		Occurs when the opinion or prejudice on the
		part of an interviewer is displayed during the
		interview process and affects the outcome of
		the interview

Chance alone may lead to invalid conclusions due to type

I and type II errors. Below we will discuss inferential statistics and hypothesis testing. The effects of bias and confounding can be minimized by good study design.Experimental designs minimize bias. Randomization minimizes selection bias and equally distributes potential confounders between exposure groups. Blinding and matching can further decrease bias.

Chapter 5.  Analyzing Your Data

63

Study Design

In order to understand the conclusions that can be drawn from a study, it is critical to understand the study design. In medicine, study designs fall into two broad categories: (1) observational studies in which subjects’ treatment choices are observed and their outcomes documented and (2) experi- mental studies in which researchers randomly allocate the treatment.

There are four types of observational studies: (1) case reports or case series, (2) cross-sectional studies, (3) casecontrol studies, and (4) cohort studies. Case-series studies are simple, descriptive accounts of interesting characteristics in a group of patients.Such studies do not include control patients who do not have the disease or condition being described. These studies often serve as the foundation for future casecontrol and cohort studies. For example, when introducing a new procedure such as single-incision laparoscopic cholecys- tectomy, one might want to report the outcomes of the first group of patients undergoing the procedure to demonstrate safety and feasibility. This research may then lead to casecontrol and cohort studies comparing the new procedure to the current gold standard, in this case, standard four-incision laparoscopic cholecystectomy.

Cross-sectional studies include surveys, polls, and preva- lence studies. They analyze data collected on a group of sub- jects at a single point in time. The intent of a cross-sectional study is to provide a description of what is happening at that single time point. Cross-sectional studies can provide preva- lence of a condition (the number of people with the condition divided by the total population at one point in time). Incidence, or the number of people who develop a condition over a specified period of time, cannot be ascertained in cross-sectional studies.

Case-control and cohort studies are often termed longitudi- nal studies,where subjects are followed over time.The primary difference between the two study types is the direction of the inquiry. Case-control studies are retrospective.The “cases” are

64 T.S. Riall

selected based on the presence of some disease or outcome, while “controls” are individuals without the disease or out- come. For example, you might want to study risk factors for the development of pancreatic fistula after pancreatic resec- tion. In a case-control study, the cases are patients undergoing pancreatic resection who developed a fistula, and the controls are patients undergoing pancreatic resection who did not.You then look back and compare potential risk factors such as pancreatic texture, preoperative diagnosis, anastomotic tech- nique, etc., between the cases and controls. Case-control stud- ies are efficient for unusual conditions or outcomes and are relatively easy to perform, but it can often be difficult to iden- tify an appropriate control. In addition, high-quality medical records are essential. Such studies are especially susceptible to selection and detection bias.The results of case-control studies are often presented as odds ratios (OR).

Traditional cohort studies are prospective. In prospective studies, the direction of inquiry is forward from the cohort inception, and events occur after the study begins. Retrospective cohort studies are studies in which the cohort is identified based on historical medical records, and the fol- low-up period is partly or completely in the past. Cohort studies are optimal for studying the incidence, course, and risk factors for a disease since subjects are followed over time. Using the same example above with a cohort study design, the investigator would define the cohort as patients undergoing pancreatic resection, all of whom are at risk for developing a fistula. All potential risk factors are assessed at the onset of the study (before surgery). Patients are then fol- lowed prospectively to observe the effect of the risk factors on the outcome, in this case, fistula formation. The results of a cohort study are usually presented as relative risk. Prospective cohort studies minimize selection, information, recall, and measurement bias. They often require a long time for completion and are not good for looking at rare outcomes.

In experimental studies, subjects are allocated to specific treatment groups. These studies involve the use of controls that can be concurrent, sequential (cross-over design), or