Background
There have been a number of studies that have reported a strong relationship of forced expiratory volume in one second (FEV1) to risk of lung cancer (e.g.
Methods
Inclusion and exclusion criteria
Attention was restricted to epidemiological studies of cohort design involving a follow-up period of at least three years, in which FEV1 was recorded at baseline, and which presented the results of analyses relating FEV1 (or related measures) to subsequent risk of lung cancer.
The following exclusion criteria were applied:
Patients
Studies of patients who had undergone, or were selected for, surgery; of patients with cancer or serious diseases other than COPD; publications describing case reports or reviews concerning treatment for cancer or surgical procedures.
Not cohort
Clinical studies; studies of cross-sectional design; studies involving a follow-up period shorter than three years.
Not lung cancer
Lung cancer not an endpoint; no lung cancer cases seen during follow-up.
Reviews not of interest
Review papers where the relationship of FEV1 to lung cancer was not considered, the papers typically only describing the relationship of an exposure (e.g. smoking) with FEV1 and separately with lung cancer.
Note that the four sets of exclusion criteria were applied in turn, and once one criterion was satisfied no attempt was made to consider the others.
Literature searching
A Medline search was first carried out using the search term (“Forced expiratory volume” [Mesh Terms] OR FEV1 [All fields] OR “Forced expiratory volume” [All Fields]) AND Lung cancer) with no limits. An Embase search was then carried out using the same search terms. Reviews of interest, including the earlier systematic review of Wasswa-Kintu
Identification of studies
Relevant papers were allocated to studies, noting multiple papers on the same study, and papers reporting on multiple studies. Each study was given a unique reference code (REF) of up to six characters (e.g. MANNIN or MRFIT), usually based on the principal author’s name. Possible overlaps between study populations were considered.
Data recorded
Relevant information was entered onto a study database and a linked relative risk (RR) database. The study database contained a record for each study describing the following aspects: relevant publications; study title; study design; sexes considered; age range; details of the population studied; location; timing; length of follow-up; definition of lung cancer, and whether mortality or incidence. It also contains details of the individual components making up the Newcastle-Ottawa study quality score
Quality. DOC file which describes the components of the Newcastle-Ottawa study quality scoring system, shows the scores allocated to each study, and for some scores gives the reason the study scored as negative. Scores relate to eight items - 1: “
Click here for file
The RR database holds the detailed results, typically containing multiple records for each study. Each record is linked to the relevant study and refers to a specific RR, recording the comparison made and the results. This record includes the following: sex; age range; race; smoking status; adjustment factors; type of lung cancer; source publication and length of follow-up. For studies which provided a block of results by level of FEV1%P (or by an associated measure, such as FEV1/FVC, FEV1 unnormalised or SDs of FEV1/height3 below average), the record also included the measure reported, the range (or mean if provided) of values for the comparison group, and for each level the range (or mean) of values, and the reported or estimated RR and 95% confidence interval (CI) relative to the comparison group. Also recorded was an estimate of the ratio of the number at risk in the comparison group to the overall number at risk, and the ratio of the number at risk to the number of lung cancer cases for the block, and information to distinguish between multiple blocks within the same study (e.g. for different sexes or smoking groups). For studies which only provided summary statistics for a block (such as the RR for a 1% decrease in the measure), the record contained details of the summary statistic and also the information to distinguish between multiple blocks. Although our main analyses are restricted to the most relevant estimates recorded in the RR database (e.g. data for FEV1%P if available, direct estimates of β rather than estimates derived from RRs by level, data for longest follow-up, or whole population data rather than data for small subsets of the population), all data were entered as available. However, most studies did not allow any choice.
Statistical methods
The basic model
The underlying model is that proposed by Berlin
where α and β are constants. This implies that the relative risk RR(d2,d1) comparing dose d2 to dose d1 is given by
where diff is the difference in dose. This model implies that a fixed difference in dose increases risk by a fixed multiplicative factor.
When applying this model the dose, d, is the estimated mean level of FEV1%P, and the difference in doses, diff, is taken to be the reduction in FEV1%P compared to the highest level studied. As RRs tend to increase with decreasing level of FEV1%P, expressing diff in terms of reductions in FEV1%P ensures that estimates of β tend to be positive. Note that no attempt is made to estimate absolute risks or the parameter α, only the slope parameter, β, being estimated.
To use this method it was required to estimate β, and its standard error (SE β), for each block to be analysed. Three main situations were found in the blocks examined:
a) Some studies actually presented estimates of β together with its SE or 95% CI that could be used directly. Others presented estimates in a form that could readily be converted, e.g. increase in risk per 1% decrease in FEV1%P.
b) Other studies presented data by grouped values of FEV1%P either directly as RRs and 95% CIs or in other ways that allowed RRs and 95% CIs to be calculated using standard methods
c) A final group of studies had RRs that were not expressed in terms of FEV1%P, but in terms of an associated measure, such as uncorrected FEV or FEV1/FVC. To ensure consistency in the estimation process for β, we converted values of the associated measure into values in terms of FEV1%P. To do this we made use of the publicly available data in the NHANES III study.
The NHANES III dataset
The National Health and Nutrition Examination Surveys (NHANES) were conducted on nationwide probability samples of approximately 32,000 persons 1–74 years of age. The NHANES III survey
Based on the NHANES data, Hankinson
where the coefficients: b0, b1, and b2, vary by sex, race and age, as shown in Table
For each result not expressed in terms of FEV1%P, we selected those NHANES III subjects who had the range of characteristics relevant to that result. These characteristics included the range of the lung function measure provided, age and sex (and in some cases smoking habit or an additional lung function specification). We then applied the FEV1 prediction equations to each of the selected subjects and thus estimated the mean value of FEV1%P. For example, one study
One study
Only one publication
Predictions and goodness-of-fit of the fitted model
For data presented by grouped levels of FEV1%P (or associated measures) the estimate of β was used to calculate predicted RRs and numbers of lung cancer cases at each level corresponding to the observed RRs and numbers. The observed (O) and predicted (P) numbers were then used to derive a chisquared test of goodness-of-fit by summing (O-P)2/P, taking the degrees of freedom (d.f) as one less than the number of levels. For defined values of d (0, 0.01-10, 10.01-20, 20.01-30, 30.01-40, >40) O and P were summed over block to similarly derive an overall goodness-of-fit chisquared statistic on 5 d.f. Blocks involving only two levels were ignored for the chisquared tests as providing no useful information on goodness-of-fit.
Meta-analysis and meta-regression
Individual study estimates of β and SE β were combined to give overall estimates using inverse-variance weighted regression analysis, equivalent to fixed-effect meta-analysis. Random-effects meta-analyses were also conducted, but are not reported here as the results were virtually identical. Heterogeneity was investigated by testing for significant variation in β, considering the following factors: sex (male, female, combined), publication year (<1990, 1990–1994, 1995+), age at baseline (<50, 50–59, 60+ years), Newcastle-Ottawa quality score (5–7, 8–9), continent (North America, other), mortality or incidence (deaths, incidence, both), population type (general population, other), exposed population (exposed to known lung carcinogens, other), length of follow up (≤15, 16–23, 24+ years), smoking adjustment (yes, no), measure of FEV1 reported (FEV1%P, other), effect as originally reported (regression coefficient, RR and CI, SMR/SIR) and inverse-variance weight of β (<1000, 1000–2999, 3000+). Simple one factor at a time regressions were carried out first, with the significance of each factor tested by a likelihood-ratio test compared to the null model. A stepwise multiple regression analysis was then carried out to determine which of the factors predicted risk independently.
Forest plots
Exp(β) is an estimate of the RR associated with a decrease of 1% in FEV1%P. For each such RR included, referenced by the study REF and associated block details such as sex, the RR is shown as a rectangle, the area of which is proportional to its weight. The CI is indicated by a horizontal line. The RRs and CIs are plotted on a logarithmic scale so that the RR is centred in the CI. Also shown are the values of each RR and CI and the weight as a percentage of the total. Results from the meta-analysis are shown at the bottom of the plot. The combined estimate is presented as a diamond, with the width corresponding to the CI and the RR as the centre of the diamond.
Publication bias
Publication bias was investigated using Egger’s test
Software
All data entry and most statistical analyses were carried out using ROELEE version 3.1 (available from P.N.Lee Statistics and Computing Ltd, 17 Cedar Road, Sutton, Surrey SM2 5DA, UK). Some analyses were conducted using SAS or Excel 2003.
Results
Publications and studies identified
Thirty-three publications
Others. DOC file summarizes the results for the four studies which satisfied the inclusion/exclusion criteria but were later rejected as estimates of β could not be derived.
Click here for file
The remaining 26 publications were then subdivided into 22 distinct studies, some details of which are summarized in Table
Fitted β estimates and goodness-of-fit
Table
Table
Fit. DOC file giving, for each of the blocks considered in Table
Click here for file
Where only two levels of FEV1%P were available, the fitted numbers of cases necessarily equalled the numbers observed. Where there were more than two levels being compared, the goodness-of-fit to the model was generally satisfactory. The significant (p<0.05) misfits to the model were for: block 5 (CARTA), where there was almost a 4-fold difference in risk between the highest and middle groups (90+ and 80 to <90 FEV1/FVC) but virtually the same estimated FEV1%P; block 13 (NOMURA) and block 29 (VANDEN female former smokers), where the pattern of increasing risk with declining FEV1%P was non-monotonic; and block 14 (PETO), block 17 (RENFRE females) and block 30 (VANDEN female current smokers), where the increase in risk was similar but marked in all the groups with reduced FEV1%P. Only for block 13 (NOMURA) was the p value for the fit <0.01. Table
Meta-analysis and meta-regressions
Exp(β) is the RR associated with a decrease in FEV1%P by one unit, and Figure
Flow diagram for literature searching
Flow diagram for literature searching. The diagram gives details of the four stages of the search; the Medline search, the Embase search, the search based on reviews of interest, and the search based on secondary references. The four criteria for rejecting papers during these four stages are described further in the Methods section under the headings “patients”, “not cohort”, “not lung cancer” and “reviews not of interest”. Note that one of the three papers accepted from the search based on secondary references cited a paper that was also examined but provided no lung cancer results. The four stages produced a total of 33 accepted papers (22 Medline, 5 Embase, 3 reviews of interest, 3 secondary references). Subsequently 7 of these were rejected for reasons described in the first paragraph of the Results section.
Funnel plot
Forest plot of the 32 estimates of exp(β). Estimates of β and SE(β) are presented in Table 3 for results presented originally as regression coefficients and in Table 4 for results presented by grouped level of FEV1 or associated measures. For each of the 32 estimates Figure 2 shows the associated values of exp(β) with their 95%CIs. These estimates are shown both numerically and also graphically on a logarithmic scale. The studies are sorted in order of block number, and are referenced by study reference (REF). Multiple blocks within the same study are distinguished by the following codes (M = males, F = females, N = never smokers, X = ex smokers, C = current smokers, LO = FEV1/FVC ≥ 0.70, and HI = FEV1/FVC < 0.70). In the graphical representation individual RRs are indicated by a solid square, with the area of the square proportional to the weight (inverse- variance of log RR).
Table
Publication bias
Based on the 32 estimates of β there was no evidence of publication bias using Egger’s test. This is consistent with the funnel plot shown as Figure
Discussion
Based on 32 independent data sets from 22 studies we estimate β as 0.018 (95%CI 0.016-0.021). This relationship is highly significant (p<0.001) and is equivalent to saying that, compared to someone with an average FEV1%P of 100%, someone with an FEV1%P of 90% would have a 20% increase in lung cancer risk, and someone with an FEV1%P of 50% would have a 151% increase.
There is little evidence of heterogeneity over study (I2 = 29.6%), or that estimates vary by specific factors including sex, study location, length of follow-up, adjustment for smoking, the measure of FEV1 reported, or how the results were originally reported. Nor was there any evidence of publication bias. There was, however, some evidence that estimates varied by age of the population at baseline, but even then clear reductions were seen in all three age groups studied, with β varying only between 0.015 and 0.024. We discuss below various aspects of our methods, which might attract criticism.
One is the use of the data from NHANES III which, though nationally representative of the USA, would not be representative of the populations involved in the 22 studies we considered. We used NHANES III for two reasons. First, we needed to have mean FEV1%P values corresponding to the groups used, only one study actually reported such means, and NHANES III was a large and available database. Our feeling is that any errors for non open-ended intervals are likely to be minor, and that even for open-ended intervals any errors are unlikely to have affected our main conclusions. In this we are fortified by the general consistency of the estimates of β and also by the observation that for the one study (STAVEM) that did supply means, the estimates reported (121.9, 106.6, 95.3 and 75.7) were similar to those that could be estimated from NHANES III (122.1, 106.2, 94.8 and 71.9). The other reason was that we needed some method of incorporating studies reporting results, not by FEV1%P directly, but by associated measures. Had we restricted attention to results reported by FEV1%P we would have reduced the number of available blocks from 32 to 20, and we wished to avoid such loss of power. Here it is reassuring that the overall estimate for the 12 blocks where β was estimated using data for associated measures of 0.019 (0.014-0.024) was very close to that for the other 20 blocks of 0.018 (0.015-0.021).
We should also comment on the fact that the method of estimation of β required pseudo-numbers of cases and numbers at risk for each level of FEV1%P corresponding to the adjusted RRs, as using simple numbers would have removed the effects of adjustment. We used the method of Hamling
Funnel plot
Funnel plot. Funnel plot of the 32 estimates of β against their precision (1/SE). The dotted vertical line indicates the meta-analysis estimate. Estimates based on data originally presented as FEV1%P are distinguished from other estimates by different symbols.
Another issue is the use of a simple model in which the logarithm of the RR is linearly related to the difference in FEV1%P. As always, one could postulate more complex relationships, but have found that the model fits the data quite well, as judged by the goodness-of-fit tests conducted. We have not explored whether more complex models fit materially better, nor attempted to estimate risks for a given level of FEV1%P, but note that a simple model has advantages in expressing the relationship to the reader. Clearly our model may not fit perfectly at the extremes (e.g. comparing someone with a value of FEV1%P of 150 and one of 30) but data here are limited. One would really need individual person data to get a more precise answer, but we have not attempted to obtain such data, particularly as many of the studies were conducted many years ago.
Based on those studies where we could estimate β we found no evidence of publication bias. However, we should point out that we had to reject seven publications, describing four studies, as the data were not presented in a way that allowed estimation of β. These studies, which each involved less than 40 lung cancer cases, were consistent in demonstrating a positive association of reduced FEV1 with increased lung cancer risk, and it seems unlikely that this omission has caused material bias.
While our β estimates were quite consistent over study, we did observe somewhat higher values in younger populations. This may reflect variations in the rate of FEV1 decline associated with susceptibility to smoking
In their review Wasswa-Kintu
Conclusions
Our review confirms the strong association between reduced FEV1 and increased risk of lung cancer. The strength of the association is very consistent, with our 32 estimates of β showing remarkably little variation, given the variety of ways in which the source papers presented their results. Based on our results, we estimate that each 10% decrease in FEV1%P is associated with a 20% (95% CI 17%-23%) increase in lung cancer risk.
Abbreviations
CI: Confidence Interval; d.f.: Degrees of Freedom; FEV1: Forced Expiratory Volume in 1 second; FEV1%P: FEV1 expressed as a percentage of predicted; FVC: Forced Vital Capacity; NHANES: National Health and Nutrition Examination Surveys; REF: 6 character Reference code used to identify a study; RR: Relative Risk; SE: Standard error.
Competing interests
PNL, founder of P.N.Lee Statistics and Computing Ltd., is an independent consultant in statistics and an advisor in the fields of epidemiology and toxicology to a number of tobacco, pharmaceutical and chemical companies. This includes Philip Morris Products S.A., the sponsor of this study. JSF and JSH are employees of P.N.Lee Statistics and Computing Ltd.
Authors’ contributions
JSF and PNL were responsible for planning the study. Literature searches were carried out by PNL and KJC. Data entry was carried out by JSH and checked by PNL or JSF. The statistical analyses were conducted by JSF along lines discussed and agreed with PNL. PNL drafted the paper, which was then critically reviewed by JSF and JSH. All authors read and approved the final manuscript.