Is alcohol consumption a risk factor for prostate cancer? A systematic review and meta–analysis
Strategy for data analysis
Publication bias was assessed through visual inspection of the funnel plot of log–RR of morbidity or mortality of prostate cancer due to alcohol consumption against the inverse standard error of log–RR [56] and Egger’s linear regression method [58]. We plotted a forest graph to examine how the RR estimate for any drinking in one study is different from others [56]. We also assessed between–study heterogeneity of RRs overall and by drinking groups using Cochran’s Q [59] and the I2 statistic [60]. As no heterogeneity was detected, fixed effects models were used to obtain the summarized RR estimates [56]. We also conducted sensitivity tests using random effects models, but patterns of results were very similar and are not reported here.
We used the fixed effects models to estimate the weighted RRs of prostate cancer for any alcohol use and by drinking groups while adjusting for the potential effects of study–level covariates [56, 61–63]. Drinking level in each study group was examined in terms of pre–defined specific consumption levels. Drinking categories were defined and reclassified as: (1) lifetime occasional drinkers (0.02–0.33 g/day); (2) former drinkers now completely abstaining; (3) current occasional drinkers, up to one drink per week (1.30 g per day); (4) low volume drinkers, up to 2 drinks or 1.30–24 g per day; (5) medium volume, up to 4 drinks or 25–44g per day; (6) high volume drinkers, up to 6 drinks or 25–64g per day; and (7) higher volume drinkers, 6 drinks or 65g or more per day. All studies had an open–ended heavier drinking group, i.e., with no upper limit of quantity consumed per day for responses accepted as valid. We investigated the dose–response relationship between the RR and alcohol consumption for those who drank one drink or more per week using the midpoint of each exposure category using t-test in multivariate linear regression analysis [56].
We investigated the potential modification and confounding effects of study–level covariates using bivariate analysis of RR of prostate cancer morbidity or mortality and any alcohol consumption [64]. According to the availability of the data from 27 included studies, the following study characteristics were investigated: (1) study designs which included cohort study, population–based case–control study and hospital–based case–control study; (2) outcomes, i.e., morbidity or mortality of prostate cancer; (3) adequacy of drinking measurement method defined as whether both quantity and frequency of total alcohol consumption was assessed for at least one week; (4) mean or median age of individual study populations at baseline; (5) year at baseline, if recruited over a number of years then take midpoint; (6) whether subjects with a history of cancer were excluded at baseline or prior to randomization (yes, no or unknown); (7) presence of misclassification errors, i.e., including both former and occasional drinkers, only former drinkers, only occasional drinkers or neither former nor occasional drinkers in the abstaining reference group; (8) whether or not the study and control for social status (yes or no) using income or occupation measures; (9) whether or not a study controlled for racial identity or country of origin (yes or no); (10) whether or not a study control for smoking status (yes or no); (11) whether or not a study was conducted in US. We made stratified RR estimates for studies with different values for these characteristics and also examined the differences in the RR estimates between these same subgroups of studies [64].
The covariates above were selected for control in multivariate regression analyses on empirical grounds based on the P–value of bivariate tests of the log–RR of each covariate, and correlations with other covariates. Using all 27 studies, any variable whose bivariate test had a P–value 0.10 was considered as a candidate for the multivariate regression analyses of the log–RR of prostate cancer morbidity or mortality [65, 66]. If two or more covariates were moderately to highly correlated (coefficient 0.30), the one with lowest P–value from the bivariate test was included in the multivariate regression analyses. Abstainer bias was the main interest of the present study and thus its potential confounding effect was adjusted for in the pooled analysis (Table 3) and further examined in the stratified analysis (Table 4). On the basis of these criteria, two other covariates were included in the analyses: (i) whether or not the study was conducted in the US and (ii) whether smoking was controlled in the individual studies (Tables 3 and 4). Although the study design variable was not selected as a controlled covariate in the final models using bivariate analysis, the study design was a concern as these were unevenly distributed across the studies with different abstainer biases and the RR estimates were slightly different in case-control studies from cohort studies [23]. We still examined the potential effect of the design variable by performing a sensitivity analysis by including and excluding it in multivariate regression analyses (Tables 3 and 4). However, the estimates remained unchanged. We also conducted a correlation analysis of the study design variable and other selected covariates. The design variable was highly correlated with the abstainer bias variable (the coefficient = 0.48 and P 0.001) and it was not included in the final models.
In multivariate regression analysis, the dependent variable was the natural log of the RR estimated using the rate ratio, hazard ratio or odds ratio of each drinking group in relation to the abstainer category. All analyses were weighted by the inverse of the estimated variance of the natural log RR. Variance was estimated from reported standard errors or confidence intervals. The weights for each individual study were created using the inverse variance weight scheme used in fixed regression analysis in order to obtain maximum precision for the main results of the meta–analysis [56] and such analyses may adjust for confounding among the characteristics [63].
Studies with large or small estimates and/or variance can be highly influential. Univariate analysis [56, 67, 68] was performed to identify outliers. If a particular RR was more than twice the standard deviation of the RR estimates by drinking groups it was considered to be an outlier; five risk estimates were identified as outliers among 126 risk estimates. Sensitivity analyses were run after excluding outliers but no substantial changes in the risk estimates resulted [56]. A sensitivity analysis was also run after excluding one study by Putnam et al. [41] with markedly higher risk estimates but, again, the estimates remained unchanged. There was also no substantial effect on the RR estimates when each of other studies were excluded or included.
All significance tests assumed two–tailed P values or 95% CIs. All statistical analyses were performed using SAS 9.3 and the SAS PROC MIXED procedure was used to model the log–transformed RR [69].