Classification and Bias of Clinical Research

Rick Chappell, Ph.D.

Professor,

Department of Biostatistics and Medical Informatics

University of Wisconsin Medical School

Good Ethics is Good Science:

“If a research study is so methodologically flawed that little or no reliable information will result, it is unethical to put subjects at risk or even to inconvenience them through participation in such a study. … Clearly, if it is not good science, it is not ethical.”

- U.S. Dept. of Health and Human Services, Policy for Protection of Human Subjects (45 CFR 46, 1/1/92 ed.)

Types of Studies Classified by Temporal Point of View

I. Instantaneous Studies - Surveys

II. Longitudinal Studies A. Retrospective Studies

Historical Observational CohortCase - Control

B. Prospective StudiesProspective Observational CohortClinical Trial

C. Hybrid Designs

A Schematic for Temporal Classification

Now

ProspectiveRetrospective

Observational Cohort

Clinical Trial

Randomization

Observational Cohort

Case - Control

Instantaneous: Survey

I. Instantaneous:Population-Based Studies

Synonyms Survey Population-Correlation Study Ecological Study

Two or more populations are instantaneously compared through the prevalences of both exposure and disease.

As summarized units get smaller (country region neighborhood individual), a survey approaches a historical observational cohort study.

Population-Based Studies

Advantages

Instantaneous.

Easy access to a large and varied population.

Good for hypothesis generation.

Disadvantages

Intervention is usually not feasible.

Very little information on causality: IARC standards require individual-based evidence.

II. Longitudinal:Individual-Based Studies

A longitudinal study observes exposures and events for individuals over a period of time.

There are two types, depending on whether one is looking forwards (prospective) or backwards (retrospective) from the present.

Longitudinal Studies:A. Retrospective

Historical Observational Cohort Synonyms - survey, retrospective cohort study. Examines outcomes among patients with past exposures. E.g., track down 1950s asbestos miners & determine current

status.

Case - Control (Breslow and Day, 1980) Synonyms - case referent, retrospective study. Examines past exposures among a group of patients with

current outcomes. E.g., interview mesothelioma patients & determine past

exposures.

Historical Observational Cohort Studies

Advantages

Quick results - no wait.

Easy to get large samples by ‘mining’ databases.

Yields wide range of sequelae.

Useful for investigating rare treatments or exposures.

Disadvantages

No opportunity to customize data collection.

No possibility for blinding.

Many possible biases: Confounding Selection Information

Case - Control Studies

Advantages

Cheap, quick - record searching can be automated.

Useful for pilot studies.

Useful for investigating rare disorders.

Disadvantages

Gives narrow picture of risks due to treatment or exposure.

Biases: Confounding Selection Recall

Yields only estimates of relative, not absolute risk.

Hypothetical Historical Cohort Study

Exposed Group 100 Patients 10 Events Rate = .1

Odds Ratio 2

Control Group 100 Patients 5 Events Rate = .05

Hypothetical Case-Control Study

Event Group 100 Patients 10 Exposures

Event Rate per Exposure = ?

(Not 100/200).

Non-Event (Control) Group 100 Patients 5 Exposures

Odds Ratio 2

Longitudinal Studies:B. Prospective

General Advantages Can collect detailed exposure, treatment, disease, and

demographic information. Blinding is possible. Recall and information bias may be eliminated. Useful for investigating rare treatments or exposures.

Classification depends on the presence of intervention.

Prospective Studies

Prospective Observational Cohort Synonyms - prospective trial, ‘clinical trial’. No intervention.

Randomized Controlled Clinical Trial Synonyms - prospective interventional cohort study,

experiment, prospective trial, clinical trial. Experimenters directly intervene in patient treatment,

usually on a randomized basis with controls.

Prospective Observational Cohort Study

Additional

Advantage

Passive observation; no need to dictate treatment.

Disadvantages

May take a long time to accrue cases and wait for results.

Potential confounding bias due to lack of randomization and suitable controls.

Clinical Trials

Additional Advantages

“The most definitive tool for evaluation of the applicability of clinical research” - 1979 NIH release.

Biases may be eliminated.

Good design may make analysis simple.

Disadvantages

As above, may take a long time.

Must be ethically and laboriously conducted.

Requires treatment on basis (in part) of scientific rather than medical factors. Patients may make some sacrifice (Meier, 1982).

Phases of a Clinical Trial

Biochemical and pharmacological research.

Animal Studies (Gart, 1986 & Schneiderman, 1967).

Phase I (Storer, 1989) - estimate toxicity rates using few (~ 10 - 40) healthy or sick subjects.

Phase II (Thall & Simon, 1995) - determines whether a therapy has potential using a few very sick patients.

Phases of a Clinical Trial (cont.)

Phase III - large randomized controlled, possibly blinded, experiments

Phase IV - a controlled trial of an approved treatment with long-term followup of safety and efficacy.

Longitudinal Studies:C. Hybrid Designs

Prospective Treatment, Historical Controls Currently treated series of patients is compared with

a previous series. See Gehan & Freireich (1974), Gehan (1984). Advantages

Doesn’t assign treatments.No need to recruit controls.

Longitudinal Studies:C. Hybrid Designs (cont.)

Prospective Treatment, Historical Controls Disadvantages

Same as in Historical Observational Cohort except that characteristics of treated patients (only) can be collected.

Selection bias likely because of time lag between groups.

Hybrid Designs

Prospective Treatment with Both Prospective and Historical Controls Uses both types of controls to maximize efficiency

and minimize bias See Pocock (1976a and 1976b).

Bias in Clinical Studies

Definition: Bias is a systematic error in estimation which is not reduced by increasing the study sample size (as opposed to random variation).

See Sacket (1979) and other articles in the same issue; Rose (1982); and Lachin (1988).

Classification is based on whether bias occurs at the time of patient Selection; or at the time of Information collection; or at the time of Publication.

They are all variants of Confounding, in which a third variable is related to both treatment and outcome.

I. Selection Bias

Prevalence - Incidence Bias Prevalence (observed occurrence) of a trait

Incidence (rate of onset). Cause: gap between exposure, selection of subjects. Not a problem with irreversible events such as

mortality, if detectable. E.g., hypertension may disappear with onset of CV

disease and can be overlooked as a risk factor. See Neyman, 1955. (Any retrospective study, especially case-control.)

Selection Bias

Admission Rate Bias Patients may differ from noninstitutionalized

subjects in size or direction of effects. E.g., systemic weakness vs. arthritis:

Negative relation among inpatients;Positive relation among outpatients.

See Berkson, 1946. (Any nonrandomized study with a mix of patient

sources, especially case-control.)

Selection Bias

Nonrespondant (Volunteer) Bias Nonparticipation may be related to the subject of

investigation. E.g., smokers ignore surveys more often than do non-

smokers (Seltzer, 1974). For general methods to analyze data with ‘nonignorable

nonresponse’ see Little and Rubin (1987) and Rubin (1987).

(Case-control, though drop-outs can effect any study not analyzed ‘intent to treat.)

Example: Where to add armor to fighter planes?

In World War II, the U.S. Air Force conducted an investigation into where armor could most effectively be added to fighter planes.

Researchers examined returning aircraft, mapped the locations of bullet holes, and recommended that the most commonly pierced areas be reinforced.

Their recommendation neglected the most vital part of the aircraft, which was intact in all returning aircraft: the area surrounding the pilot’s head!

II. Information Bias

Detection Signal (Diagnostic Suspicion) Bias In unblinded studies, an exposure may be

considered a risk factor for an endpoint, and such patients preferentially observed.

In blinded studies, an exposure may make an endpoint more detectable.

E.g., estrogen causes bleeding from uterine cancer to be more easily detectable.

(Any unblinded study except case-control; also clinical trials with sensitive endpoints.)

Reports of Original Studies JAVMA 191, 12/1/87“High-rise syndrome in cats”

Wayne O. Whitney, DVM & Cheryl J. Mehlhaff, DVM

Selection and/or detection bias

Information Bias

Exposure Suspicion Bias An outcome may cause the investigator to look for a

particular exposure. The temporal reverse of detection signal bias. E.g., arthritis and knuckle-cracking. (Case-control studies.)

Information Bias

Recall (family information) Bias Similar to exposure suspicion bias, but errors

originate with the subject or his/her family. E.g., in a study of prescription use among women

with fetal malformation, 28% reported unverifiable exposure vs. 20% of the controls (Klemetti & Saxen, 1967).

(Case-control studies.)

III. Publication (Reporting) Bias

Even a perfect study leads to bias if dissemination depends on the direction of its result.

Causes: Commercial reasons; Researchers’ personal motivations; Editorial Policy !

Vickers, et al. (1998) show that the problem is widespread: in some countries, 100% of publications show treatment effects.

Publication (Reporting) Bias

A version of the multiple comparisons problem (Miller, 1985), or ‘testing to a foregone conclusion’.

E.g., ORG-2766 protected nerves from cytotoxic injury in 55 women with ovarian cancer - NEJM lead article (van der Hoop, et al., 1990); a subsequent negative study of 133 women - ASCO Proceedings abstract (Neijt, et al., 1994).

(All Studies.)

A type of reporting bias: Multiple Comparisons (“Data Dredging”)

A “p-value” is interpreted as the probability of attaining a result as extreme that observed given that the result is false (under the null hypothesis); it can be viewed as the false positive rate under the null hypothesis.

This assumes that only a single test is conducted. If many tests are performed, it is possible to “sample to a foregone conclusion” and produce a falsely low p-value.

For example, if twenty-five independent tests are conducted, the probability of at least one p-value being less than .01 is .22.

Often only the significant result is reported, and the 24 others ignored.

IV. Confounding (General)

Caused by any situation in which: A third variable exists which isn’t known or at least isn’t

accounted for; It is associated with the “cause”and It is also associated with the “effect”.

Then:

The supposed cause-effect relation will be confounded by the third variable.

(Any nonrandomized study)

Do Storks Bring Babies?

Population of Oldenburg, Germany, 1930-1936(Ornithologische Monatsberichte 44, Jahrgang, 1936, Berlin)

Storks (1000s)

Humans(1000s)

References

Berkson, J. Limitations of the application of fourfold table analysis to hospiital data (1946). Biometrics Bulletin 2, 47-53.

Breslow, N.E. and Day, N.E. (1980). Statistical Methods in Cancer Research 1: The Analysis of Case-Control Ctudies. Oxford: Oxford University Press.

Dorr, Robert T. (1997). Personal communication.

Gart, J.J. et al. (1986). Statistical Methods in Cancer Research 3: The Design and Analysis of Long-Term Animal Experiments. Oxford: Oxford University Press.

Gehan, Edmund A. The evaluation of therapies: Historical control studies, with discussion (1984). Statistics in Medicine 3, 315-324.

Gehan, Edmund A. and Freireich, Emil (1974). The New England Journal of Medicine, 198-203.

IARC. Monographs on the Evaluation of Carcinogenic Risk of Chemicals to Humans. Lyon: IARC.

Klemetti, A. and Saxen, L. Prospective vs. retrospective approach in the search for environmental causes of malformations. American Journal of Public Health 57, 2071-2075.

Lachin, J. Statistical properties of randomization in clinical trials (1988). Controlled Clinical Trials 9, 289-311.

Little, R.J.A. and Rubin, D.B. (1987). Statistical analysis with Missing Data. New York: Wiley.

Neyman, J. Statistics - servant of all sciences (1955). Science 122, 401.

Meier, Paul. Current research in statistical methodology for clinical trials (1982). Proceedings of Current Topics in Biostatistics and Epidemiology: A Memorial Symposium in Honor of Jerome Cornfield. Pages 141- 150. Biometrics.

Miller, R. Publication bias (1985). Entry in The Encyclopedia of Statistical Sciences, Volume 5. S. Kotz and N.L. Johnson, eds., pp. 679-689. New York: Wiley.

National Institutes of Health, Division of Research Grants, Research Analysis and Evaluation Branch, Bethesda, MD (1979). NIH inventory of clinical trials: fiscal year 1979, Volume I.

Neijt, et al. (1994). Proceedings of the American Society for Clinical Oncology.

Pocock, S.J. The combination of randomized and historical controls in clinical trials (1976a). Journal of Chronic Diseases 29, 175-188.

Pocock, S.J. Randomized versus historical controls: A compromise solution (1976b). Proceedings of the International Biometric Conference 9/1, 245-260.

Rose, G. Bias (1982). British Journal of Clinical Pharmacology 13, 157-162.

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.

Sacket, D.L. Bias in analytic research (1979). Journal of Chronic Diseases 32, 51-63.

Schneiderman, M.A. Mouse to man: statistical problems in bringing a drug to clinical trial (1967). Proceedings of the 5th Berkeley Symposium in Mathematical Statistics and Probability, Volume IV. L.M. LeCam and J. Neyman, eds. Berkeley.

Seltzer, C.C. et al. Mail response by smoking status (1974). American Journal of Epidemiology 100, 453-477.

Storer, B.E. Design and analysis of phase I clinical trials (1989). Biometrics 46, 33-38.

Thall, Peter F. and Simon, Richard M. Recent developments in the design of phase II clinical trials (1995). In Recent Advances in Clinical Trial Design and Analysis. Peter Thall, ed. , pp. 49-72. New York: Kluwer.

Unger, D.L. Does knuckle cracking lead to arthritis of the fingers? [letter]. Arthritis & Rheumatism 41. 949-50, 1998.

van der Hoop, et al. (1990). New England Journal of Medicine 322, 89-84.

Vickers, et al. (1998). Controlled Clinical Trials 19, 159-166.