Most epidemiologic articles are built around a standard format in which the design, execution, and data from the study are described in some detail, along with statistical analyses to identify “risk factors” (antecedents of adverse health outcomes that remain associated with the outcomes after adjustments for measured potential confounders). With rare exception, these analyses account only for the possible role of random error (“chance”) in producing the results; possible roles for biases and causal mechanisms are dealt with by informal discussion.
This standard format has been criticized for its failure to account adequately for biases and their interactions (apart from adjustments for measured confounders); the alternative offered is sensitivity analysis and its Monte-Carlo and Bayesian extensions.2–8 The standard format has also been attacked for its failure to adequately justify the study within a context of biomedical or social theory.9–11 Some of the latter criticisms have gone so far as to imply that much of epidemiology is pointless and wasteful “black-box” research that yields too many nonsense associations and false alarms.12
Among defenses of “black-box” epidemiology are that it has identified useful interventions and that the absence of a known causal mechanism has no bearing on the validity of the study results.13 Skrabanek,12 however, responded that the latter point was never in dispute; he asserted instead that “there is no logical link between black box epidemiologic studies and science” and that risk-factor epidemiology “cannot contradict pertinent scientific data.” We argue here that such claims are logically erroneous and counterproductive.
One genuine problem of risk-factor epidemiology is overinterpretation of observed associations as causal. We believe that this problem stems from pressure on researchers to demonstrate scientific or policy relevance of their results, or at least to explain their results as arising from something other than error or bias. We argue that it is a mistake to impose such an explanatory burden on empiric research reports, and that the problem of overinterpretation could be addressed by encouraging a descriptive approach to risk-factor studies. We then illustrate our arguments with a case study on the epidemiology of renal cell carcinoma.1
THE VALUE OF DESCRIPTIVE OBSERVATION REPORTS
A theory could be said to explain an observation (or fact) when the observation can be deduced from or is predicted by the theory. According to one popular philosophy of science, the primary role of scientific observation is to supply the facts that require explanation by theories. From this viewpoint, assertions that risk-factor studies could “provide testable hypotheses of causality”12 or are “hypothesis-generating” are logically backward, even though they could reflect some of the psychology of theory creation.14 Furthermore, purely descriptive (atheoretical) approaches to the reporting of single epidemiologic studies (and even metaanalyses) are not only scientific, but beneficial; the publication of mere observations is useful, because such observations supply data for the scientific community to use in tests of theories, including theories not even conceived at the time of publication. This means that the incoherence or implausibility of observations in light of current theory should never be a deterrent to publication; a field that appears to be nothing more than an incoherent jumble of haphazard or implausible observations could be rapidly transformed by the introduction of a new and unifying theory.
One consequence of this philosophy is that (contrary to some teachings) precision and replication are not secondary in importance to validity and novelty in study design. Precision and replication establish the facts that need to be explained by theories. A study that is perfectly valid but so imprecise as to be incapable of determining the direction and size of an association is of little or no use in testing predictions about the association. Precise null results are much more informative than so-called “null” or “nonsignificant” findings that are compatible with a wide range of possibilities (and hence are of little use for testing theories). Thus, precision, not statistical significance, measures the statistical importance of results.15 Although many imprecise studies could be combined to provide enough data to precisely establish an association's size, this phenomenon involves some form of study replication.
Without sufficiently precise data on variation in disease occurrence, there is little that epidemiology can contribute to choosing among proposed social or biologic theories of the disease. With enough description, however, researchers have on hand immediate tests of predictions from proposed theories. A large bank of observations (epidemiologic, laboratory, and clinical) allows rapid winnowing down of theories to promising candidates. The observations can be used regardless of whether they were gathered for the purpose of testing a given theory, although observations collected for that purpose might yield more powerful tests of that theory than would other data sources.
Selecting or formulating theories based on the proportion of observations explained raises the specter of overfitting, that is, proposing a theory so flexible that it can explain not only all that has been observed, but also almost anything likely to be observed. Parsimony (keeping the theory simple) is commonly offered as a safeguard against overfitting.14 Nonetheless, it has been argued that uncritical devotion to parsimony can be harmful when modeling epidemiologic data-generating processes and methodologic problems, because parsimony in statistical models often leads to overconfident epidemiologic inferences.8,16 A more direct safeguard against overfitting is to check whether a proposed theory excludes some plausible possibilities for data that were not used in formulating the theory (such as observation not yet made). This safeguard is the well-known falsifiability or testability criterion for preferring theories.
A theory formulated to explain laboratory results could have a large body of epidemiologic associations available for testing purposes, provided epidemiologists have been thorough in examining and reporting their data. More generally, a promising biologic theory will provide a link among observations from various research specialties (eg, epidemiologic, experimental, and clinical), as well as links among different risk factors; such a theory will also make predictions that are testable with further feasible observations.
THE EPIDEMIOLOGY OF RENAL CELL CARCINOMA
As an example of these points, the epidemiology of renal cell carcinoma might seem an incoherent body of facts, but for a theory that explains both epidemiologic and laboratory results. That theory posits lipid peroxidation as an intermediate step that leads to a final common pathway shared by numerous observed risk factors such as obesity, hypertension, smoking, oophorectomy, hysterectomy, parity, diabetes, and dietary antioxidants.1 The theory thus explains the epidemiology of renal cell carcinoma; as we discuss, it also explains numerous experimental and clinical findings, and has implications for disease prevention.
We begin by summarizing the literature on risk factors for renal cell carcinoma. This summary is analogous to the data-description portion of a study report; it is a dry description of our perception of published data. For brevity, we rely heavily on reviews of renal cell carcinoma, along with some original reports, and provide only rough summaries of previous findings. Like with data descriptions in study reports, the reviews and our summary are subject to reporting errors and biases (eg, publication bias). Unlike primary data descriptions, however, the reader can check the following summaries against the literature:
1. Age: Renal cell carcinoma rates rise rapidly with age.17
2. Sex: Men have age-specific renal-cell carcinoma rates about double the rates in women.17
The remaining summaries refer to associations adjusted for age, sex, and (in some analyses) other variables:
1. Ethnicity: Renal cell carcinoma rates from U.S. cancer registry data are higher in blacks than in whites.18
2. Obesity: Several case-control and cohort studies have reported obesity associated with renal cell carcinoma.17 For example, body mass index (BMI, defined as kg/m2) over 30 appears associated with 4 times the renal cell carcinoma risk of BMI under 22.19
3. Hypertension: Several case-control and cohort studies have reported hypertension associated with renal cell carcinoma.17 A history of hypertension appears associated with a doubling of renal cell carcinoma risk.19
4. Smoking: Several case-control studies have reported cigarette use associated with renal cell carcinoma.17 For example, smokers appear to have a 40% higher risk of renal cell carcinoma than nonsmokers.20
5. Oophorectomy and hysterectomy: Although earlier case-control studies had reported no association between hysterectomy or oophorectomy and renal cell carcinoma, more recent investigations have observed these procedures associated with a doubling of renal cell carcinoma risk.21–24
6. Parity: Although previous case-control studies have reported no clear relation between parity and renal-cell carcinoma risk, more recent studies have reported positive associations.21,23–26 For example, women with 5 or more births appear to have twice the risk among women with one or 2 births.23–25
7. Antioxidants: In several case-control studies, high measured dietary intake of antioxidants was associated with a 20% to 50% decrease in the risk of renal cell carcinoma relative to low intakes.27,28
8. Diabetes: Although some case-control studies had reported no association between diabetes and renal cell carcinoma, more recent investigations have consistently found positive associations.21 Several large case-control and cohort studies reported diabetes associated with a 30% to 70% elevation in renal cell carcinoma risk.29–31
9. Preeclampsia: There have been case reports of renal cell carcinoma in women with severe preeclampsia or hypertension during pregnancy.32,33
10. Analgesics: Although results are not entirely consistent, several case-control studies have reported chronic use of analgesics (aspirin, other nonsteroidal antiinflammatory drugs, acetaminophen, and phenacetin) associated with an increased renal cell carcinoma risk.21,34
EXPERIMENTAL AND CLINICAL STUDIES OF LIPID PEROXIDATION
What sense can be made of a dozen risk factors ranging from hypertension to hysterectomy? This diversity might suggest that the associations arise from a variety of biologic mechanisms and methodologic problems; the associations might instead be dismissed as the sort of scrap heap that unchecked risk-factor epidemiology produces. A third alternative, however, is that there is a terminal pathway in renal cell carcinoma development affected by all the factors.
Rodent experiments point to lipid peroxidation of the proximal renal tubules as a principal mechanism in renal carcinogenesis.1 Renal tumors induced by ferric nitrilotriacetate in rodents appear to be the counterpart of human renal cell carcinoma.35 Similar to human renal cell carcinoma, rodent renal cell carcinoma incidence is higher in males than females, with histopathologic features marked by clear or granular-type cells.35,36 On the clinical side, lipid peroxidation markers are elevated in human renal cell carcinoma tissue.37,38
Thus, our next step is to summarize experimental and clinical observations that link the dozen epidemiologic risk factors discussed here to the hypothesized shared intermediate steps (lipid peroxidation) and the outcome (renal cell carcinoma):
1. Lipid peroxidation in serum increases with age in humans39 and in renal tissue increases with age in animals.40
2. Men have higher serum and plasma levels of lipid peroxidation than women.41–44 Testosterone treatment or oophorectomy increase ferric nitrilotriacetate-induced lipid peroxidation and renal cell carcinoma incidence in rodents, whereas estradiol treatment or castration decrease ferric nitrilotriacetate-induced lipid peroxidation and renal cell carcinoma incidence.36,45
3. Among patients with diabetes, African-Caribbeans appear to have higher lipid peroxidation levels than whites46 (we are unaware of relevant data for nondiabetics). Black have a higher prevalence of several conditions associated with elevated lipid peroxidation such as hypertension (in both men and women) and obesity (in women).47
4. Obesity/hypertension induces lipid peroxidation and the development of renal damage in rats.48 Obesity has been associated with elevated lipid peroxidation among human subjects, and the elevation appears to be removable by weight reduction.49–54
5. Hypertension has been associated with elevated lipid peroxidation among human subjects, and the elevation appears to be removable by antihypertensive therapy.55–57
6. Oophorectomy in rodents increases the extent of ferric nitrilotriacetate-induced lipid peroxidation of the renal tubules45 and the incidence of renal cell carcinoma.36 Female mice lacking both ovaries show increased lipid peroxide in serum.41 Human oophorectomy is also associated with elevated serum lipid peroxidation41; because human hysterectomy is usually accompanied by oophorectomy, we would expect hysterectomy to be related to lipid peroxidation as well.
7. Dietary antioxidants and antilipidemic agents prevent ferric nitrilotriacetate-induced lipid peroxidation, nephrotoxicity, or renal cancer development in rats,58–60 and they appear to inhibit lipid peroxidation in humans.61–64
8. Relevant to parity, pregnant rats have higher serum lipid peroxidation than nonpregnant rats,65 and pregnant women have higher lipid peroxidation than nonpregnant women.66
9. Tobacco smoking is associated with lipid peroxidation in humans.67,68
10. Diabetes has been associated with elevated lipid peroxidation.69
11. Preeclampsia is associated with increased lipid peroxidation in women.66,70,71
12. Aspirin, naproxen, and diclofenac appear to increase lipid peroxidation in humans and rodents.72–74 Diclofenac-induced nephrotoxicity in mice has been linked to its ability to increase lipid peroxidation.72
Regarding genetic factors, somatic mutations of the VHL gene seem to occur at high frequency in sporadic cases of renal cell carcinoma. It has been shown that renal carcinoma cells lacking a functional VHL gene express hypoxia-inducible genes such as VEGF75–77; furthermore, reintroduction of the wild-type VHL gene into these cells is sufficient to repress VEGF gene expression under normoxic conditions and to restore its normal regulation by hypoxia. It appears that hypoxia can induce lipid peroxidation,78 and also that lipid peroxidation can increase the expression of hypoxia-inducible genes.79
Not every study supports the lipid peroxidation–renal cell carcinoma hypothesis. Isolated normal renal tubular tissues have displayed higher levels of malondialdehyde (MDA; a lipid peroxidation marker) than have renal cell carcinoma tissues.80 4-hydroxy-2-nonenal (HNE) (another lipid peroxidation marker) protein adducts occur in kidneys in both normal and tumor cells, and immunomorphologic analyses suggest less HNE protein adducts in tumor than in normal cells.81 Although most studies have found increased lipid peroxidation in men relative to women,41–44 one study did not.82 In apparent conflict with point 5 above, some studies have suggested an association of diuretics or other antihypertensives with renal cell carcinoma21; others have reported no association.19,83 These results reflect uncertainties in separating the effect of hypertension from that of its medical treatment. One explanation is that antihypertensives increase risk directly while indirectly lowering risk through hypertension reduction. Indeed, one study observed blood pressure reduction associated with reduced renal cell carcinoma risk.84
Many of these associations (such as those with sex, smoking, and antioxidants) are explainable through other mechanisms or are not specific to kidney cancer. Nonetheless, these observations do not conflict with the lipid peroxidation hypothesis because the hypothesis does not preclude other mechanisms, and lipid peroxidation could play a role in other cancers. If and when a truly competing hypothesis is proposed, the challenge for its proponents will be to explain as large a proportion of the available evidence as does the lipid peroxidation hypothesis.
A promising theory will not only make predictions testable with further observations; it will also point to new observations that will make past observations useful in testing the theory. For example, antilipidemic agents appear to prevent lipid peroxidation in humans.62–64 In isolation, this observation says nothing about the lipid peroxidation hypothesis, but it can form part of another test of the theory when combined with data on the relation of antilipidemic agents to renal cell carcinoma. The limited available data support an inverse association of statins with cancers (including renal cell carcinoma),85 and further data could be easily collected in case-control studies of renal cell carcinoma. Lipid-lowering agents could exert beneficial effects in patients with different types of renal disease86 such as end-stage renal disease,87,88 a condition itself associated with an increased renal cell carcinoma risk89; thus, we would expect that further epidemiologic renal cell carcinoma data will support the lipid peroxidation hypothesis.
In summary, it appears that most available evidence can be explained by the lipid peroxidation hypothesis, and the amount of contradicting evidence is no more than what one might expect given the many sources of error and bias in typical studies. Therefore, we think that the lipid peroxidation hypothesis is well-supported at this time.
Research articles can be useful even if they only report how the study was done and what associations were observed without attempting to interpret or explain these observations in terms of methodologic, biologic, or social theories. Readers can weave the observations into their own explanatory theory, one that incorporates their knowledge of related literature. This is not to discourage the study authors from proposing their own explanations. In particular, the study authors are usually most familiar with the study methods and therefore often best equipped to offer methodologic explanations of their results (ie, explanations of those results based on the particulars of the study design and conduct rather than in terms of the biologic or social mechanisms of ultimate interest). In particular, they could be well-positioned to propose realistic models for sources of bias in their study. These bias models can then be used to examine the sensitivity of the results to plausible bias combinations, and (more ambitiously) can be used to quantify uncertainty about the observed associations in a manner less misleading than ordinary statistical methods.2–8
We suggest that removing pressure for study reports to delve into general theory could be constructive in allowing the writers more time and the report more space to describe the methods, analyses, and data. The saved space can help ensure that “null” associations are reported in as much detail (with their interval estimates) as other associations, and so reduce publication bias in the general literature. The added detail can facilitate formulation of explanations by readers. A descriptive orientation could also encourage deferral of general explanations to more thorough and comprehensive reviews. When forced to cram a minireview into a study report, study authors can end up biased toward overweighting the evidence that they know well (eg, their own studies) and must inevitably sacrifice thoroughness.
Our suggestion is analogous to suggestions that authors should be under no pressure to offer public health implications of their results. With regard to the latter, it has been argued that policy implications should be reserved for separate articles that synthesize evidence in a balanced fashion, along with costs and benefits of proposed actions.90 Whether considering scientific explanations or policy formulation, rarely if ever does a single study have any implications in isolation from the broader interdisciplinary whole. Yet, for most topics, even an outline of that whole would be too lengthy to include in every research report, and such inclusion would be wastefully redundant.
Our suggestion would specialize the use of contextual theory (biology, sociology, and so on) in epidemiologic papers into 3 major categories. In study reports, contextual theory could be used to justify the study and the choice of analytic models (including bias models), but the theory need not be reviewed in detail; the primary mission would be accurate presentation of methods and data. Reviews would then bear the task of evaluating contextual theories against all lines of evidence and against each other. Finally, policy analyses would integrate review information about likely effect sizes, along with cost considerations, to compare the likely costs and benefits of various actions (including inaction). Some epidemiologists could find such article specialization in conflict with their conception of public health research. Most researchers accept, however, that personal specialization has become necessary, because one “renaissance scientist” can no longer master all the fields that contribute to understanding and control of complex diseases like cancer (pathology, toxicology, genetics, animal experimentation, epidemiology, sociology, statistics, risk assessment, policy analysis, and so on). Article specialization is an analogous necessity when description, analysis, and interpretation of every relevant study cannot be handled by one “renaissance paper.”
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On Miasmas and Astrology
“For want of knowing any other cause, epidemics were attributed by the ancients to the atmosphere, without any evidence; just as political and social events were believed to be occasioned by the stars.” - JOHN SNOW
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