- Analytic perspective
- Open Access
Regression analysis with categorized regression calibrated exposure: some interesting findings
- Ingvild Dalen^{1}Email author,
- John P Buonaccorsi^{2},
- Petter Laake^{1},
- Anette Hjartåker^{3} and
- Magne Thoresen^{1}
https://doi.org/10.1186/1742-7622-3-6
© Dalen et al; licensee BioMed Central Ltd. 2006
- Received: 06 January 2006
- Accepted: 04 July 2006
- Published: 04 July 2006
Abstract
Background
Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile) scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis.
Methods
We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC).
Results
In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis.
Conclusion
Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a percentile scale. Relating back to the original scale of the exposure solves the problem. The conclusion regards all regression models.
Keywords
- Effect Estimate
- Folate Intake
- Naive Approach
- Replication Pattern
- Regression Calibration
Introduction
Measurement error is recognized as a common problem in epidemiological studies. Many interesting variables are registered with a relatively large degree of uncertainty, often due to low-price and simple measurement methods. The errors could be either random (e.g. due to biological fluctations about a mean), systematic (e.g. due to varying calibrations of measurement instruments), or both, which is most often the case. It is well known that measurement error in predictors biases effect estimates in regression modelling. For this reason, measurement error has been the subject of extensive research over the recent decades, and several methods have been proposed for handling the problem. In linear models the standard reference is [1], while Carroll et al. [2] provide an excellent overview of methods applying to non-linear models.
One of the methods for dealing with measurement error that has gained popularity is the so-called regression calibration method; see for example Chapter 3 of [2]. This is most likely due to its intuitive nature, relative ease of use and general applicability. It has also been shown to have good properties in many situations. Regression calibration was introduced to the epidemiologic community by Rosner et al. [3, 4]. In another formulation of the same method [5], the idea is to predict the unobservable error-prone variable by means of regression, and then to include this predicted variable in the main analysis. The approach involves efforts to somehow relate the observed variable to the underlying "true" variable, either through a sub validation study where the true value is observed directly for some of the individuals, through repeated measurements for some or all of the individuals, or by use of so-called instrumental variables that supply information about the true values relative to the measured values. It is also possible to apply information from external sources. Software for performing regression calibration is available in STATA [6] and in SAS [7, 8].
The most well-known version of regression calibration is the one developed for continuous explanatory variables. However, in epidemiological studies it is also common to categorize the exposure variables according to rank such as quintiles; a selection of newer examples of studies using this approach is [9–13]. Usually an analysis comparing each quintile group to the lowest (reference) group is supplemented with a test for trend for the quintile numbers. Another trend estimator applies the median values of the quintile groups [14, 15]. The reason for categorizing the exposure could be to obtain analyses that require less stringent assumptions and that are more robust to outlying values [16]. Now that regression calibration is becoming more standard in the epidemiologic community, one can easily imagine a situation where this method is applied to a continuous variable, which is subsequently categorized before it is incorporated in the main (regression) analysis. The researcher might then feel confident that he or she has taken the necessary precautions with regard to measurement error.
We study the performance of this approach under 3 different modelling schemes, all applying the same categorization according to quintiles: regression on (A) dummy variables, (B) quintile numbers, and (C) median value within quintile groups, thereby obtaining what one may call an enhanced trend estimator. The corresponding results from analyses with the continuous exposure are included for comparison. Linear regression is used as the framework for our demonstration, but, as will be shown, the results are valid for other regression models as well.
We find that for analysis with dummy variables and for simple trend analysis, in most cases the corrected effect estimates are approximately equal to the ones obtained without making the correction. In some cases they are identical. We argue that categorizing the corrected exposure still retains misclassification similar to the misclassification obtained using the observed exposure. This misclassification induces bias in the effect estimates. When introducing the median value of each category to the analysis, the correction method regains some of its usual advantage over the naive approach. The reason for this will become clear.
We start off defining the models used, and then present analytical and semi-analytical arguments and results for the various settings defined above. The results are illustrated by simulated examples and also by a real-life example, where we have examined the relationship between folate intake and risk of depression in a prospective cohort study of Norwegian women, the Norwegian Women and Cancer study (NOWAC).
Methods
In the following we will assume that an exposure variable X is measured with error and in effect is unobservable. The true exposure X is instead observed through a measured value W, and we assume an additive error model such that W = X + U, where U is the measurement error, with expected value E (U) = 0. We also observe a response or disease variable Y and sometimes a covariate Z, both measured without error. Importantly, we assume that the measurement error is non-differential, i.e., F (W|X, Y) = F (W|X). This implies that W contributes no new information about Y apart from what is already in X.
where μ_{ X }, μ_{ W }and μ_{ Z }denote the expected values of X, W and Z, respectively; , and are the variances of X, U and Z; and finally σ_{ XZ }denotes the covariance between X and Z. Since E (U) = 0, μ_{ X }= μ_{ W }. Equation (1) defines the RC predictor for the error-prone exposure X. The parameters in (1) must be estimated from the data, e.g. as described in [2], pages 47–48, or see [6] for a detailed procedure in STATA.
where I_{ r }is 1 if x ∈ and 0 otherwise. F_{ X }denotes the cumulative distribution of X, hence is the r th quintile point in the distribution of X. When evaluating the performance for this method, we mainly look at α_{4}, which is the difference in mean response between the extreme quintile groups for the exposure. The covariate Z is still analyzed on the continuous scale.
where X_{ c }is a number from 0 to 4, according to which quintile group X falls into. Hence, if x ∈ then x_{ c }= r.
where X_{ med }are the median values of the individuals falling into the various categories. Hence, if x ∈ , then x_{ med }is assigned the median value of all the individuals in the r th quintile group.
When comparing the effect estimates obtained from fitting a regression model involving the categorized RC predictor as the exposure to those obtained using a naive predictor ( ) and to the true effect estimates (obtained from X), we categorize the two former according to quintiles in their respective distributions. Hence, the cut-points for the naive predictor and the RC predictor will in general not be the same as the ones for the true exposure. Neither will the median values.
If the response variable Y is instead dichotomuous, e.g. representing a disease variable where the value 1 is assigned to diseased individuals and 0 is assigned to healthy ones, we must replace E (Y) with the logit transform log [E (Y)/(1 - E (Y)]. Similar transforms apply to other regression models.
With respect to standard errors for the RC corrected estimates, these will be underestimated by ordinary methods as they do not take into account the variance in the estimation of X. Since the computation of explicit formulas for the standard error is quite tedious [5], standard errors are typically obtained through bootstrapping [2, 20].
Results
Analytical results
Constant number of replicates
When all individuals are measured an equal number of times (k_{ i }= k), we find that the RC predictor given in Equation (6) is simply a linear transformation of the naive predictor . This transformation represents in essence a weighting between the estimated sample mean and the individual means for each data point. Given a certain error ( ); when k is large and thus relatively close to 1, relatively large confidence is put on the individual means and little correction is made. On the other hand, when k is small, all data points are adjusted closer to the sample mean. In both cases the adjustment is the same for all subjects, resulting in a distribution that is squeezed towards the estimated sample mean, as compared to the distribution of measured values.
which is greater than Var (X) whenever σ_{ U } > 0, that is, when there is measurement error. Notice also that when k → ∞, Var ( ) → Var (X); that is, if we were to have infinitely many replications, we would be able to estimate Var (X) without bias, using the observed values.
Var ( ) = Var (λ' ) = λ'^{2}Var ( ) = λ'Var (X).
Relating this adjusted continuous exposure to a response in a regression analysis results in larger effect estimates as compared to the ones obtained using the measured exposure. For example, in linear regression the effect is decided by the ratio of the covariance of exposure and response to the variance of the response (σ_{ XY }/ ), and even though the covariance between the corrected exposure and the response ( ) underestimates σ_{ XY }due to measurement error, this is counteracted by the decreased variance of , resulting in unbiased effect estimates. Using the observed exposure, we get a so-called attenuated effect estimate, which is underestimating the true effect by a factor λ' [2].
When it comes to using the medians of each quintile as explanatory variable, as proposed in model C, regression calibration regains some of its usual superiority over naive analyses. As explained, RC involves a squeezing of values towards the mean, so the distances between the medians in the distribution of corrected exposure will be smaller than in the naive distribution. Hence, corrected effect estimates will be larger than naive estimates.
We illustrate this using linear regression. If X ~ N (0, ) and U ~ N (0, ), and we have k replicates, then ~ N (0, /λ') and ~ N (0, λ' ). Hence, for any percentile point q we have that and . Hence, for variables consisting of median points in quintile groups we have that Var ( _{ med }) = Var (X_{ med })/λ' and Var ( _{ med }) = λ' Var (X_{ med }).
Regarding the covariances, we have that given that the error in the exposure is independent of the response Y (nondifferential measurement error), Cov ( , Y) = Cov (X, Y). Thus, the covariance between the response and the variable given by medians in quintile groups of the naive exposure is
Cov ( _{ med }, Y) = Cov (X_{ med }, Y).
Cov ( _{ med },Y)= λ' Cov (X_{ med }, Y),
Hence, since in this case Cov ( _{ med }, Y)/Var ( _{ med }) = Cov (X_{ med }, Y)/Var (X_{ med }), the regression calibrated effect estimate is asymptotically correct. The naive estimates are on the other hand attenuated by the same factor Λ as when analyzing the exposure on continuous scale.
Varying numbers of replicates
When the number of replicates varies between individuals, we have in addition a kind of confusion effect, in that some data points are adjusted to a larger extent than others. However, the main effect of the transformation is the mentioned adjustment towards the sample mean. At least, we propose that classification of the corrected predictor according to quintiles leads to much the same classification pattern as classification of the naive predictor .
Misclassification. Percentages of equal classifications between X_{ c }and _{ c }, X_{ c }and _{ c }and _{ c }and _{ c }for various replication patterns, where X_{ c }is the categorized true exposure, _{ c }is the categorized mean measured exposure, and _{ c }is the categorized RC corrected exposure, all of them categorized according to quintiles in the individual distributions.
Pattern | applying to | x_{ c }= _{ c }(%) | x_{ c }= _{ c }(%) | _{ c }= _{ c }(%) |
---|---|---|---|---|
1 | total sample | 44.3 | 44.1 | 89.1 |
20% with 5 reps | 58.3 | 58.1 | 72.7 | |
80% with 1 rep | 40.8 | 40.6 | 93.2 | |
2 | total sample | 55.6 | 55.2 | 89.1 |
80% with 5 reps | 59.4 | 59.3 | 93.2 | |
20% with 1 rep | 40.2 | 38.8 | 72.7 | |
3 | total sample | 50.3 | 49.7 | 83.0 |
50% with 5 reps | 59.5 | 58.9 | 83.0 | |
50% with 1 rep | 41.0 | 40.5 | 83.0 |
At the same time, we see that the percentages of cases that are correctly classified (that is, in accordance with the classification of the true X), are very similar for the naive and the corrected predictors. Hence, categorizing using the corrected exposure still retains misclassification, and the magnitude of this is very similar to the misclassification obtained with the naive approach. Hence, the estimates relating to categorical exposure in models A and B, will be very similar for the naive and the RC approach. However, in model C, regression calibration still benefits from the mentioned squeezing of values towards the mean.
Illustration with simulated data
We simulated a variety of situations to obtain numerical results regarding the biases of the naive and the corrected effect estimates. These simulations were conducted using the software program R version 2.2.1 [21], in which the base integrated routine for general linear models was applied to generated datasets of size n = 100000.
The true exposure X and the response Y were both generated from standard normal distributions. The error U was normally distributed with mean zero and variance decided by various fixed levels of the reliability ratio . The covariate Z was omitted.
We studied cases where the correlation ρ_{ XY }between the response and the true continuous exposure, and hence the effect β_{1}, was either 0.7 or 0.2, see Equation (2). These cases correspond to true mean differences α_{4} of 1.96 and 0.56 between the extreme quintiles in model A (Equation (3)), naive trends γ_{1} of 0.47 and 0.13 (model B, Equation (4)), and effects ψ_{1} of 0.76 and 0.22 using medians in groups as explanatory variables (model C, see Equation (5)).
Results were produced for three levels of the reliability ratio λ: 0.2 (which corresponds to a rather large measurement error), 0.5, and 0.8 (modest measurement error situation). Standard errors for the corrected effect estimates are obtained via resampling pairs bootstrapping with 200 bootstrap samples [20].
Results from simulations without covariates. Naive and regression calibrated effect estimates in linear regression with error-prone exposure X, analysing (A) dummy variables, comparing 5th vs. 1st quintile, (B) quintile numbers, and (C) median values within quintile groups. Results from analysis with continuous exposure is included for comparison. We have X and Y ~ N (0,1) and the error U ~ N (0, ), where is chosen such that the reliability ratio λ is either 0.8, 0.5 or 0.2. The results are obtained via simulation, where the correlation ρ_{ XY }between continuous X and Y is set to either 0.7 or 0.2. The true effects are indicated. For the cases marked 'k constant', each individual is measured twice. For the cases marked 'k not constant', the replication pattern is 5 measurements on a random 20% subset of individuals and 1 measurement on the rest. Standard errors for the corrected cases are bootstrapped.
k constant | k not constant | ||||||
---|---|---|---|---|---|---|---|
ρ _{ XY } | model | true coef | λ | naive coef (SE) | RC coef (SE) | naive coef (SE) | RC coef (SE) |
0.8 | 0.62 (0.003) | 0.70 (0.002) | 0.58 (0.003) | 0.70 (0.003) | |||
cont | 0.70 | 0.5 | 0.47 (0.003) | 0.70 (0.004) | 0.38 (0.002) | 0.70 (0.005) | |
0.2 | 0.23 (0.002) | 0.69 (0.010) | 0.16 (0.002) | 0.69 (0.011) | |||
0.8 | 1.85 (0.010) | 1.85 (0.008) | 1.77 (0.010) | 1.77 (0.008) | |||
A | 1.96 | 0.5 | 1.61 (0.010) | 1.61 (0.009) | 1.45 (0.010) | 1.47 (0.009) | |
0.2 | 1.14 (0.010) | 1.14 (0.008) | 0.93 (0.010) | 1.00 (0.010) | |||
0.7 | |||||||
0.8 | 0.44 (0.002) | 0.44 (0.002) | 0.42 (0.002) | 0.42 (0.002) | |||
B | 0.47 | 0.5 | 0.38 (0.002) | 0.38 (0.002) | 0.35 (0.002) | 0.35 (0.002) | |
0.2 | 0.27 (0.002) | 0.27 (0.002) | 0.23 (0.002) | 0.24 (0.002) | |||
0.8 | 0.68 (0.003) | 0.76 (0.003) | 0.62 (0.003) | 0.76 (0.003) | |||
C | 0.76 | 0.5 | 0.51 (0.003) | 0.76 (0.005) | 0.42 (0.003) | 0.76 (0.006) | |
0.2 | 0.25 (0.002) | 0.75 (0.011) | 0.18 (0.002) | 0.77 (0.013) | |||
0.8 | 0.18 (0.003) | 0.20 (0.003) | 0.17 (0.003) | 0.21 (0.003) | |||
cont | 0.20 | 0.5 | 0.13 (0.003) | 0.20 (0.004) | 0.11 (0.002) | 0.20 (0.004) | |
0.2 | 0.07 (0.002) | 0.20 (0.006) | 0.04 (0.002) | 0.19 (0.006) | |||
0.8 | 0.52 (0.010) | 0.52 (0.010) | 0.53 (0.010) | 0.52 (0.010) | |||
A | 0.56 | 0.5 | 0.45 (0.010) | 0.45 (0.010) | 0.41 (0.010) | 0.42 (0.010) | |
0.2 | 0.32 (0.010) | 0.32 (0.010) | 0.25 (0.010) | 0.27 (0.009) | |||
0.2 | |||||||
0.8 | 0.13 (0.002) | 0.13 (0.002) | 0.12 (0.002) | 0.12 (0.002) | |||
B | 0.13 | 0.5 | 0.11 (0.002) | 0.11 (0.002) | 0.10 (0.002) | 0.10 (0.002) | |
0.2 | 0.08 (0.002) | 0.08 (0.002) | 0.06 (0.002) | 0.06 (0.002) | |||
0.8 | 0.19 (0.003) | 0.22 (0.004) | 0.18 (0.003) | 0.22 (0.004) | |||
C | 0.22 | 0.5 | 0.14 (0.003) | 0.21 (0.005) | 0.12 (0.003) | 0.22 (0.005) | |
0.2 | 0.07 (0.002) | 0.22 (0.007) | 0.05 (0.002) | 0.21 (0.007) |
We see that in situations with a constant number of replicates, regression calibration estimates are equal to the ones obtained from the naive approach, unless the original scale of measurement is somehow incorporated. None of the methods performed very poorly as long as the measurement error was not too large, however the effects were attenuated by a factor of almost 0.6 in both models A and B in the most severe measurement error situation studied (λ = 0.2). When λ = 0.5, the attenuation factor for these models was just above 0.8. Hence, the effect estimates differ considerably from the true effects in many cases. Moreover, a decrease in the reliability ratio is associated with increased bias, as was to be expected.
Using the median values in model C, we see that the regression calibration approach gives unbiased effect estimates. This is in contrast to the naive approach, which in the most severe cases (λ = 0.2) indicates effects that are about 1/3 of the true effects.
When the number of replicates varies, we see again that the regression calibration fails to improve significantly the effect estimates relative to the naive approach, except for with model C. In these results we see some small, though not substantial, differences between the two approaches for models A and B, due to the confusion effect mentioned previously. We also see that, in contrast to what could be expected from Table 1, it is the regression calibrated estimates that are slightly better off. Although the naive approach gives a higher percentage of correctly classified cases, the mean squared distance between the true and the observed category is actually larger than for the RC approach (1.23 vs. 1.20), explaining this apparent inconsistency. Notice also that the results are generally worse with this replication pattern than when all individuals were measured twice.
Including a covariate
Regression calibration uses the information of covariates in the correction procedure, see Equation (1). Thus, including a variable correlated to X in the analysis will probably give RC an advantage relative to the naive approach, especially when the correlation is strong.
Results from simulations including a covariate Z, weakly correlated to X. Naive and regression calibrated effect estimates in linear regression with error-prone exposure X and a covariate Z weakly correlated to X (ρ_{ XZ }= 0.2), analysing (A) dummy variables, comparing 5th vs. 1st quintile, (B) quintile numbers, and (C) median values within quintile groups. Results from analysis with continuous exposure is included for comparison. We have X, Z and Y ~ N (0,1) and the error U ~ N (0, ), where is chosen such that the reliability ratio λ is either 0.8, 0.5 or 0.2. The results are obtained via simulation, where the correlations ρ_{ XY }= ρ_{ ZY }are set to either 0.7 or 0.2. The true effects of X and Z are indicated for the various models. All individuals are measured twice. Standard errors for the corrected cases are bootstrapped.
ρ_{ XY }= ρ_{ ZY } | model | true coef (X) | λ | naive coef (X) (SE) | RC coef (X) (SE) | true coef (Z) | naive coef (Z) (SE) | RC coef (Z) (SE) |
---|---|---|---|---|---|---|---|---|
0.8 | 0.52 (0.001) | 0.58 (0.002) | 0.60 (0.002) | 0.58 (0.001) | ||||
cont | 0.58 | 0.5 | 0.38 (0.001) | 0.58 (0.003) | 058 | 0.62 (0.002) | 0.58 (0.002) | |
0.2 | 0.19 (0.001) | 0.59 (0.009) | 0.66 (0.002) | 0.58 (0.003) | ||||
0.8 | 1.53 (0.010) | 1.53 (0.006) | 0.61 (0.003) | 0.60 (0.002) | ||||
A | 1.63 | 0.5 | 1.32 (0.007) | 1.34 (0.010) | 0.60 | 0.63 (0.003) | 0.60 (0.002) | |
0.2 | 0.92 (0.010) | 0.95 (0.008) | 0.67 (0.003) | 0.60 (0.003) | ||||
0.7 | ||||||||
0.8 | 0.36 (0.002) | 0.36 (0.001) | 0.61 (0.003) | 0.60 (0.002) | ||||
B | 0.39 | 0.5 | 0.31 (0.002) | 0.32 (0.001) | 0.60 | 0.63 (0.003) | 0.60 (0.002) | |
0.2 | 0.22 (0.002) | 0.23 (0.002) | 0.67 (0.003) | 0.60 (0.003) | ||||
0.8 | 0.56 (0.003) | 0.63 (0.002) | 0.61 (0.003) | 0.60 (0.002) | ||||
C | 0.63 | 0.5 | 0.42 (0.003) | 0.63 (0.003) | 0.60 | 0.63 (0.003) | 0.60 (0.002) | |
0.2 | 0.21 (0.002) | 0.63 (0.009) | 0.67 (0.003) | 0.60 (0.003) | ||||
0.8 | 0.15 (0.003) | 0.17 (0.003) | 0.17 (0.003) | 0.17 (0.003) | ||||
cont | 0.17 | 0.5 | 0.11 (0.003) | 0.16 (0.004) | 0.17 | 0.18 (0.003) | 0.17 (0.003) | |
0.2 | 0.05 (0.002) | 0.17 (0.006) | 0.19 (0.003) | 0.17 (0.003) | ||||
0.8 | 0.44 (0.010) | 0.45 (0.010) | 0.18 (0.003) | 0.17 (0.003) | ||||
A | 0.47 | 0.5 | 0.38 (0.010) | 0.38 (0.009) | 0.17 | 0.18 (0.003) | 0.17 (0.003) | |
0.2 | 0.27 (0.010) | 0.29 (0.010) | 0.19 (0.003) | 0.17 (0.003) | ||||
0.2 | ||||||||
0.8 | 0.11 (0.002) | 0.11 (0.002) | 0.18 (0.003) | 0.17 (0.003) | ||||
B | 0.11 | 0.5 | 0.09 (0.002) | 0.09 (0.002) | 0.17 | 0.18 (0.003) | 0.17 (0.003) | |
0.2 | 0.06 (0.002) | 0.07 (0.002) | 0.19 (0.003) | 0.17 (0.003) | ||||
0.8 | 0.16 (0.003) | 0.18 (0.004) | 0.18 (0.003) | 0.17 (0.003) | ||||
C | 0.18 | 0.5 | 0.12 (0.003) | 0.18 (0.004) | 0.17 | 0.18 (0.003) | 0.17 (0.003) | |
0.2 | 0.06 (0.002) | 0.18 (0.007) | 0.19 (0.003) | 0.17 (0.003) |
Due to the introduction of Z, the true effects that we are trying to estimate are somewhat smaller than when X is the only independent variable in the models. Nevertheless, we see that when the correlation between X and Z is small (Table 3), the pattern from Table 2 is repeated, in that the naive and RC corrected estimates of the effects of X are very similar for models A and B, while for continuous exposure and for model C, RC is much better. In fact, the attenuation factors are quite similar to the ones obtained in Table 2 (for constant k).
Regarding the effects estimates for the covariate Z, we see that both methods are quite good, though while the RC approach gives unbiased estimates, the naive approach tends to overestimate as the measurement error increases. This is a well-known effect for covariates positively correlated to error-prone explanatory variables.
Results from simulations including a covariate Z, strongly correlated to X. Naive and regression calibrated effect estimates in linear regression with error-prone exposure X and a covariate Z strongly correlated to X (ρ_{ XZ }= 0.7), analysing (A) dummy variables, comparing 5th vs. 1st quintile, (B) quintile numbers, and (C) median values within quintile groups. Results from analysis with continuous exposure is included for comparison. We have X, Z and Y ~ N (0,1) and the error U ~ N (0, ), where is chosen such that the reliability ratio λ is either 0.8, 0.5 or 0.2. The results are obtained via simulation, where the correlations ρ_{ XY }= ρ_{ ZY }are set to either 0.7 or 0.2. The true effects of X and Z are indicated for the various models. All individuals are measured twice. Standard errors for the corrected cases are bootstrapped.
ρ_{ XY }= ρ_{ ZY } | model | true coef (X) | λ | naive coef (X) (SE) | RC coef (X) (SE) | true coef (Z) | naive coef (Z) (SE) | RC coe (Z) (SE) |
---|---|---|---|---|---|---|---|---|
0.8 | 0.33 (0.003) | 0.41 (0.003) | 0.47 (0.003) | 0.41 (0.003) | ||||
cont | 0.41 | 0.5 | 0.21 (0.002) | 0.40 (0.005) | 0.41 | 0.55 (0.003) | 0.41 (0.004) | |
0.2 | 0.09 (0.001) | 0.42 (0.013) | 0.64 (0.002) | 0.40 (0.009) | ||||
0.8 | 0.91 (0.013) | 0.97 (0.010) | 0.51 (0.004) | 0.47 (0.003) | ||||
A | 1.05 | 0.5 | 0.68 (0.012) | 0.84 (0.010) | 0.47 | 0.58 (0.004) | 0.49 (0.003) | |
0.2 | 0.40 (0.011) | 0.59 (0.013) | 0.65 (0.003) | 0.53 (0.005) | ||||
0.7 | ||||||||
0.8 | 0.21 (0.003) | 0.23 (0.002) | 0.51 (0.004) | 0.47 (0.003) | ||||
B | 0.25 | 0.5 | 0.16 (0.003) | 0.20 (0.002) | 0.47 | 0.58 (0.004) | 0.49 (0.003) | |
0.2 | 0.09 (0.002) | 0.14 (0.003) | 0.65 (0.003) | 0.53 (0.004) | ||||
0.8 | 0.33 (0.004) | 0.40 (0.004) | 0.51 (0.004) | 0.47 (0.003) | ||||
C | 0.41 | 0.5 | 0.21 (0.003) | 0.37 (0.004) | 0.47 | 0.58 (0.004) | 0.49 (0.003) | |
0.2 | 0.09 (0.002) | 0.30 (0.007) | 0.65 (0.003) | 0.53 (0.004) | ||||
0.8 | 0.09 (0.004) | 0.12 (0.005) | 0.13 (0.004) | 0.11 (0.004) | ||||
cont | 0.12 | 0.5 | 0.06 (0.003) | 0.11 (0.006) | 0.12 | 0.16 (0.004) | 0.12 (0.005) | |
0.2 | 0.02 (0.002) | 0.11 (0.010) | 0.19 (0.003) | 0.13 (0.008) | ||||
0.8 | 0.26 (0.012) | 0.27 (0.012) | 0.14 (0.004) | 0.13 (0.004) | ||||
A | 0.30 | 0.5 | 0.20 (0.012) | 0.23 (0.014) | 0.13 | 0.17 (0.004) | 0.14 (0.005) | |
0.2 | 0.12 (0.011) | 0.16 (0.019) | 0.19 (0.003) | 0.16 (0.006) | ||||
0.2 | ||||||||
0.8 | 0.06 (0.003) | 0.06 (0.003) | 0.14 (0.004) | 0.13 (0.004) | ||||
B | 0.07 | 0.5 | 0.05 (0.003) | 0.05 (0.003) | 0.13 | 0.17 (0.004) | 0.15 (0.005) | |
0.2 | 0.03 (0.002) | 0.04 (0.004) | 0.19 (0.003) | 0.16 (0.006) | ||||
0.8 | 0.09 (0.004) | 0.11 (0.005) | 0.14 (0.004) | 0.13 (0.004) | ||||
C | 0.11 | 0.5 | 0.06 (0.004) | 0.10 (0.006) | 0.13 | 0.17 (0.004) | 0.14 (0.005) | |
0.2 | 0.02 (0.002) | 0.08 (0.009) | 0.19 (0.003) | 0.16 (0.006) |
Furthermore, while for the continuous case the regression calibration approach still manages to produce unbiased estimates, we see that for model C there are some deviations for large measurement errors. We also see that the tendency of the naive approach to overestimate the effects of Z, as observed in Table 3, is continued here, and now the RC estimates are also affected.
Example
To illustrate our results, we use data on non supplemental folate intake, total energy intake and self-reported depression from the Norwegian Women and Cancer (NOWAC) cohort study started in 1991 [22]. The data were collected by food frequency questionnaires (FFQs), and we analyze a sub replication study in which a sample of the cohort were measured a second time. The replicated subsample consists of 898 individuals with no missing data. Hence, we have W_{ ij }= estimated folate intake through food (in μ g/MJ) for individual i in FFQ j, and Y_{ i }= self-reported depression (yes/no) for individual i, where i = 1, ..., 898, j =1, 2. The prevalence of depression in the sample was 19.7%.
The folate intake, adjusted for total energy intake, was related to self-reported depression using logistic regression modelling. Using the continuous exposure, the naive odds ratio (OR) was estimated as 0.70 (SE = 0.13) for each 10 μ g/MJ increase in folate intake, while the regression calibration approach gave = 0.62 (bootstrapped SE = 0.16). Looking at the effect of going from the first to the last quintile (model A), we found = 0.57, with standard errors 0.15, for both approaches. The simple trend (model B) was estimated to 0.87 (SEs 0.05) for both approaches. Applying the median values in model C, the naive effect estimate was = 0.61 (SE = 0.13) for each 10 μ g/MJ increase in folate intake, while the corrected estimate was 0.52 (SE = 0.15).
Clearly, all of these results are quite unstable. However, we notice that in situations where the original scale is incorporated, the regression calibration approach gives stronger effect estimates than the naive approach. In contrast, when the analysis is performed on the quintile scale, the two approaches give similar results.
The 898 individuals included in the replication study were sampled from a larger group (n = 19740 with no missing data) with single measurements of folate intake. Including the total group in the analysis, we got the following results: Using the continuous exposure, the naive odds ratio was 0.84 (SE = 0.03) for each 10 μ g/MJ increase in folate intake, while the regression calibration approach gave = 0.75 (SE = 0.05). Under model A, we found = 0.71 (SE = 0.04) for both approaches, and the simple trend (model B) was estimated to 0.92 with standard error 0.01, again for both approaches. Applying the median values in model C, the naive effect estimate was = 0.78 (SE = 0.03) for each 10 μ g/MJ increase in folate intake, while the corrected estimate was 0.67 (SE = 0.05).
Although we now have varying numbers of replications, the two approaches still give the same results for models A and B, probably because a total of two measurements on just 4.5% of the individuals is not enough to introduce the confusion effect mentioned previously. In total, 98.7% of the individuals were classified equally with the two approaches, and none differed by more than 1 category. The overall findings regarding the comparison naive vs. RC approach are unchanged.
Discussion
We find in this paper that the excellent performance of the regression calibration method for dealing with measurement error on continuous exposures in regression analysis, is diminished when the exposure is categorized before effect estimates are obtained. As shown, one needs to relate back to the original scale for the approach to be valuable.
In particular, we find that the effect estimates using RC are comparative to those obtained by a naive approach of not correcting for measurement error, when the exposure is analysed on a categorical scale. In some cases they are analytically equal. The main reason for the poor results is that categorizing using the corrected exposure still retains misclassification, which is similar to the misclassification obtained with the naive approach, and this misclassification induces bias in the effect estimates. When using the median measured value of each exposure group as explanatory variable, regression calibration works by decreasing the spread in the exposure distribution, thus resulting in larger effect estimates.
For regression analysis including a covariate measured without error, we find some differences between the naive and the RC approach, especially when the correlation between the exposure and the covariate is strong. However, none of the approaches are particularly good.
Since the reason for the poor results is to be found in the treatment of the explanatory variable, our general findings are most certainly not exclusive to any regression model, but can be extended to concern other regression models.
In diagnostic tests, for example, it is quite common to categorize according to a fixed cut-off level, where an extreme value is diagnosed as a case. Furthermore, in epidemiologic studies, one can also relate to fixed exposure groups/exposure groups that are defined independently from the observed data, classifying for example smoking into {0}, {1 – 10}, {11 – 20} and {> 20} cigarettes per day, or body mass index (BMI) into underweight (< 18.5), normal weight (18.5 to 24.9), overweight (25 to 29.9), and obese (≥ 30). A small simulation study was conducted to explore whether the current results sustain when such fixed cut-points are applied, and it seems RC now gains a small advantage compared to the naive approach. Also, the more extreme the cut-point, the larger the difference between the two approaches. This situation corresponds to the one where the true percentiles are known, though the interpretation of the results is somewhat different.
We have focused on a situation with replicates. However, as outlined in the Introduction, other sources of information regarding the measurement error could be either internal or external validation studies or instrumental variables. The approach studied in this paper would still amount to fitting a regression model for the true given the measured exposure, and including the predicted exposure from this model in the main analysis. Furthermore, the percentiles would be predicted by the same model, so naive and corrected categorized exposure are the same in these situations as well.
In some cases it might not be appropriate to use the original scale in the analysis, the researcher might specifically wish to relate to the categorical variables. In our view, there are two possible approaches to obtain efficient effect estimates in these cases. Either a) some information is needed about misclassification probabilities or b) a better way is needed to categorize from the original continuous measurements.
We cannot achieve a) using just replicate measures (without further assumptions on the distribution of X) but could if we had validation data. For example, Rosner [16] suggested to simply treat these situations as misclassification problems, using ordinal regression procedures with validation data. A similar approach involving latent class modeling of replicated data has been proposed [23]. Recently, Küchenhoff et al. [24] developed the MC-SIMEX methodology, to deal with situations with misclassification in categorical exposure and/or response, however the procedure requires either knowledge or an estimate of the misclassification matrix. A Bayesian approach to misclassification problems has been suggested [25], which might be taken a step further in our setting.
To achieve b) one can try to estimate the underlying distribution of X, and its percentiles in a nonparametric way using the replicate measures. There has been extensive work on estimating the distribution of X (see [26] and references therein, and a new idea recently proposed by Freedman et al. [27]) but the ability of these techniques to accurately estimate percentiles has not been fully explored. Work is underway to explore the use of these techniques in the current problem.
Instead of going via the expected values of the continuous exposure, we could find directly the expected categorical exposure. We expect that analysis with expected conditional probabilities (given the observed exposure) of the categories will give better results than the analysis with dummy variables. The latter amounts to adjusting the probability of the most probable category to 1 and all the other probabilities to 0, thereby disregarding the information that lies in the uncertainty of the categorization.
Future work should aim to develop suitable and functional correction procedures in analyses where the exposure variable is categorized according to percentiles, and investigations should be carried out in order to decide which method is the best or most suitable for recommendations to include in routine analysis.
Declarations
Acknowledgements
This work was supported by a grant from the Norwegian Research Counsil, which also supplied a visitor's grant for Buonaccorsi.
Authors’ Affiliations
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