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1 Supplementary Materials for Altered Placebo and Drug Labeling Changes the Outcome of Episodic Migraine Attacks Slavenka Kam-Hansen, Moshe Jakubowski, John M. Kelley, Irving Kirsch, David C. Hoaglin, Ted J. Kaptchuk, Rami Burstein* The PDF file includes: *Corresponding author. Published 8 January 2014, Sci. Transl. Med. 6, 218ra5 (2014) DOI: /scitranslmed Methods Scripted Information Read to Participants Randomization of Treatment and Treatment Labeling Missing Data Analyses of Primary Endpoint Fitting the Main Model Analyses of Secondary Endpoint Table S1. Reasons for excluding 19 of the prescreened subjects. Table S2. Background characteristics of participants and dropouts. Table S3. Selected quantiles of nondichotomous background characteristics of the 66 participants. Table S4. Incidence of missing pain scores and data imputation. Table S5. Structure of the eight treatment sequences and assignment of subjects to treatment sequences. Table S6. Distribution of attacks with imputed pain scores and pain freedom at 2.5 hours. Table S7. Estimates of parameters in the main model for the pain scores including the imputed pain scores at 2.5 hours. Table S8. Percentage decrease in the estimated average pain score from 30 min to 2.5 hours under the seven experimental conditions (from an analysis that included imputed pain scores at 2.5 hours). Table S9. Estimates of parameters in the covariate model for the pain scores including the imputed pain scores at 2.5 hours. Table S10. Estimates of difference (2.5 hours minus 30 min) on the primary endpoint for key contrasts involving treatment and labeling. Table S11. Sensitivity analysis of the main model for the pain scores. Table S12. Estimates of parameters in the main model for pain freedom at 2.5 hours.

2 Table S13. Estimated probability of being pain-free 2 hours after treatment under the seven experimental conditions (from an analysis that included imputed pain scores at 2.5 hours). Table S14. Estimates of difference (2.5 hours minus 30 min) on the secondary endpoint for key contrasts involving treatment and labeling.

3 Supplementary Materials for Altered Placebo and Drug Labeling Changes the Outcome of Episodic Migraine Attacks Slavenka Kam-Hansen, Moshe Jakubowski, John M. Kelley, Irving Kirsch, David C. Hoaglin, Ted J. Kaptchuk, Rami Burstein *Corresponding author. METHODS Scripted Information Read to Participants. You are invited to take part in a research study for the purpose of understanding the effects of repeated administration of Maxalt for the treatment of acute migraine attacks, and why placebo rates are so high in migraine therapy. Our first goal is to understand why Maxalt makes you pain-free in one attack but not in another. Our second goal is to understand why placebo pills can also make you pain-free. Our third goal is to understand why Maxalt works differently when given in double-blind study vs. real-life experience when you take it at home. These goals are scientifically important for developing new therapies for migraine. To reach these goals, we would like to follow 7 of your migraine attacks. Two of these attacks will be treated in the traditional double-blind placebo-controlled way. You will not know whether the pill you take is a Maxalt or a placebo. Four of the attacks will be treated in an open way. We will give you Maxalt in 2 attacks and you will know you take Maxalt and we will give you placebo pills in 2 attacks and you will know you take a placebo pill. At the end of the study we will compare the results of the double-blind placebo treatment to the results of the open treatment attacks. As a control, we will ask you to report your pain in an additional attack (#7) in which you withheld treatment for at least 2 hours. 1

4 Randomization of Treatment and Treatment Labeling. The study-drug envelopes for the 6 treatment conditions combined one of the 3 labels and one of the 2 pills (Fig. 1). Of 720 (= 6!) possible permutations, we chose 8 sequences for delivering the 6 treatment conditions (table S5). The key for the abbreviation system for the 6 treatment conditions is described in the footnote of table S5. In each sequence, maxalt and placebo treatments alternated from one migraine attack to the next, and each labeling was repeated for two consecutive attacks. Within each type of labeling, the treatment pills were given in one order in half of the sequences (maxalt placebo) and in the reverse order in the other half (placebo maxalt). The labeling would have been balanced on all 6 permutations of P ( Placebo ), U (unspecified Maxalt or Placebo ), and M ( Maxalt ) labeling if we had included 4 additional sequences, based on the permutations P U M and M U P (each combined with the two permutations of placebo pill and maxalt pill). We chose to limit the number of sequences to 8 to ensure that a reasonable number of participants would be randomized to each sequence. With our final sample size of 66 analyzable participants, the 8 sequences had an average of 8.25 (range 7 to 10) participants. Had we used 12 sequences, the average would have been 5.5 participants per sequence, with some sequences perhaps having as few as 3 or 4 participants. We chose to omit those 4 sequences because the resulting lack of balance occurs only for the neutral condition ( Maxalt or Placebo ), and the remaining neutral conditions are equally divided between the initial two attacks and the final two attacks, thus maintaining partial balance. Choosing to omit some other subset of sequences would have led to an imbalance for either the Maxalt or Placebo labels, and we assumed that any imbalance for these non-neutral labels would be more likely to affect outcomes. 2

5 The treatment sequences were listed in a spreadsheet ahead of the study. Blocks of the 8 sequences were listed repeatedly, and the order of the sequences was randomized within each block. Each row in the spreadsheet contained one treatment sequence, and the rows were numbered consecutively. Each subject received an identification number according to the order of her/his recruitment and was assigned the corresponding treatment sequence in the spreadsheet. Table S5 shows the distribution of the recruited subjects among the 8 sequences. Missing Data. Sixty-six participants reporting 2 pain scores (30 min and 2.5 h) for each of the 7 attacks would have yielded a total of 924 observations. However, the available data comprised 894 observations because 15 subjects had a total of 30 unreported or unusable pain scores. The analysis took into account the reasons for missingness. The reasons and the numbers of missing and imputed values of pain score and pain freedom outcomes at 2.5 h are listed in tables S4 and S6. In 18 of the attacks with missing data, subjects took rescue medications before the designated time, implying that they did not attain pain freedom at 2.5 h. In those attacks we imputed the missing 2.5-h pain score, according to the following rule. If the subject did not report a 2.5-h pain score or reported a score that was lower than or equal to the 30-min score, we carried over the 30-min pain score. If the subject reported a 2.5-h pain score that was greater than the 30-min score, we used that higher score. Although no single approach is likely to be ideal, the reason for the missingness makes this procedure sensible. Our approach has the limitations that the imputed value may entail some bias and is less variable than the 2.5-h pain score would have been had the subject not resorted to rescue medication prematurely. As a form of sensitivity analysis, we analyzed the available data. Analyses of Primary Endpoint Fitting the Main Model. To describe the analysis of the pain scores, we denote the subject by i (i = 1,, 66), experimental condition by j (j = 1,, 7; table 3

6 S6), and time by t (t = 1, 2; t = 1 corresponds to 30 min, and t = 2 corresponds to 2.5 h). The pain score for subject i at time t under experimental condition j is denoted by y ijt. The analyses used generalized linear mixed models (GLMM). The linear component describes the structure of g(µ ijt ), where g is a link function and µ ijt denotes the expected value of y ijt, and one also specifies a family of distributions for the random variation in y ijt. Our analyses used the logarithm as the link function and the normal distributions as the family for the random component. For the main analysis, the linear component (or linear predictor) had the following form: g(µ ijt ) = β 1 + β 2 Cond2 ijt + β 3 Cond3 ijt + β 4 Cond4 ijt + β 5 Cond5 ijt + β 6 Cond6 ijt + β 7 Cond7 ijt + β 8 Time2 ijt + β 9 Cond2 ijt Time2 ijt + β 10 Cond3 ijt Time2 ijt + β 11 Cond4 ijt Time2 ijt + β 12 Cond5 ijt Time2 ijt + β 13 Cond6 ijt Time2 ijt + β 14 Cond7 ijt Time2 ijt + b i The variables Cond2, Cond3, Cond4, Cond5, Cond6, Cond7 (experimental conditions), and Time2 (2.5 h) are indicator variables; they have the value 1 for certain values of their ijt subscripts and 0 otherwise. NT (no treatment) serves as the reference category for the experimental conditions, and t = 1 (30 min) serves as the reference category for time. A difference between t = 2 and t = 1 on the log scale corresponds to a ratio of pain scores at 2.5 h to pain scores at 30 min. β 1 represents the logarithm of the average of the subjects pain scores at 30 min under the NT condition; it is part of the linear predictor for each experimental condition. Thus, the coefficients for the experimental Cond indicators express the logarithm of the corresponding average 30-min pain score as a difference from the logarithm of the average 30- min pain score under NT. For example, the logarithm of the average 30-min score under the M M condition (j = 4) is β 1 + β 4. Similarly, the coefficient of Time2 expresses the logarithm of the average 2.5-h pain score under the NT condition as a difference from the logarithm of the 4

7 average 30-min pain score under NT; that is, the logarithm of the average pain score at 2.5 h under NT is β 1 + β 8. The coefficients β 9 through β 14 come into play only at 2.5 h under the corresponding condition, because the variables to which they are attached have the value 1 only when both Condj and Time2 are 1. Each expresses the logarithm of its corresponding average pain score as a difference from the sum of the parameter for NT at 30 min ( β 1 ), the parameter for that condition at 30 min ( β j ), and the parameter for NT at 2.5 h ( β 8 ). For example, the logarithm of the average pain score at 2.5 h under the P P condition (j = 7) is β 1 + β 7 + β 8 + β 14. That is, β 9 through β 14 reflect the interaction between experimental condition and time. In the equation displayed above, each of the indicator variables has all three subscripts, i, j, and t, because those variables are part of the model for each combination of subject, experimental condition, and time, even when they have the value 0. We could simplify the notation, however, because the indicator variables have the same value for all subjects, the Cond variables have the same value at 30 min and 2.5 h, and Time2 has the same value for all experimental conditions. That is, we could omit the subscript i from all the indicator variables, we could omit the subscript t from each of the Cond variables, and we could omit the subscript j from Time2. The last term in the displayed equation, b i, is a random effect for subject i. In a mixed model, such random effects account for variation among subjects, which is the main source of correlation among the repeated measures on individual subjects. The parameter to be estimated is the variance of the (normal) distribution of the random effects. We considered including a second random effect, for attack j within subject i, but decided not to do so, as discussed further below. 5

8 To fit the various models, we used Release 12.1 of Stata (Stata Corp LP, College Station, TX, The analyses of the pain scores by generalized linear mixed models used gllamm, a user-written Stata command ( The calculations in gllamm and similar programs involve numerical integration (quadrature), and gllamm has options that allow users to make that process more efficient and more accurate. By default the program uses nonadaptive quadrature with 8 integration points. To assess accuracy, we used the main model and increased the number of integration points to 12, 16, 20, 24, 30, 40, and 50 (and experienced corresponding increases in execution time). The estimates of the coefficients with 50 integration points differed from those with 40 integration points by at most two digits in the fourth decimal place (most of the estimates agreed to four decimal places). We got the same estimates (and smaller execution time) with 20 integration points and adaptive quadrature, so we settled on adaptive quadrature with 20 integration points. Table S7 shows parameter estimates, standard errors, and 95% CIs for the parameters in the main model. The estimates reported in the article are based on this model. In particular, table S8 shows the numerical values of the estimates and 95% CIs plotted in Fig. 3. Sequence of experimental conditions. To investigate the possibility of effects associated with the sequence in which the subjects encountered the experimental conditions (table S5), we expanded the linear model shown above by including effects for the sequence (a main effect), the combination of sequence and experimental condition (a 2-factor interaction), the combination of sequence and time (a 2-factor interaction), and the combination of sequence and experimental condition and time (a 3-factor interaction). A likelihood-ratio test indicated that the expanded model was not a significant improvement (chi-squared = , df = 98, P = 0.116). Thus, the models for the primary endpoint did not include effects for the sequences. 6

9 Covariates. We investigated the influence of 7 background characteristics: age, years since first attack, number of attacks per month, migraine days per month, sex, triptan history, and family history. Adding these 7 covariates to the main model produced a non-significant likelihood-ratio statistic (chi-squared = 9.19, df = 7, P = 0.239). Also, the P value for each covariate was > 0.05 (table S9). Thus, the main model did not include any covariates. Random effect for attack within subject. Adding to the main model a random effect for attack within subject yielded a log-likelihood that was virtually identical to the one obtained without this random effect. The estimates of the parameters were essentially the same as those in table S7, and the estimate of the variance of the additional random effect was with a standard error of In view of these negligible contributions we did not retain this random effect in the main model. Gamma distributions for the random component. We also considered using the gamma family instead of the normal family for the random component in the main model. gllamm gave the error message: initial values not feasible. The initial values for the parameters in the linear model were appropriate, so the difficulty apparently lay with the initial values of the parameters for the gamma distribution, which gllamm did not reveal. It did not seem worthwhile to pursue this problem further, since the direction of the skewness in the gamma distributions is opposite to that in the pain scores. Interaction of labeling and treatment. The main model is suitable for testing the main effect for treatment (the difference between M and P treatments, df = 1), but not for assessing, in the aggregate, the main effects for labeling (df = 2) and the interactions between labeling and treatment. Likelihood-ratio tests of the main effects and interactions are straightforward when one has a sequence of nested models that allows for removal of variables associated only with 7

10 interactions between labeling and treatment (leaving only main effects for labeling and main effects for treatment), followed by removal of variables associated only with main effects of labeling, or variables associated only with main effects of treatment, or removal of both sets of main effects. The main model does not have such a nested structure, because it takes into account the data from the untreated attack (NT). Thus, we modified the main model by replacing the 6 product variables with indicator variables (all as differences from the change associated with NT, as in the main model). The indicator variables were the overall effect of the six treatment conditions and the incremental effects of M labeling, P labeling, P treatment, the M P combination, and the P P combination. Omission of the M P and P P indicator variables yielded a model with no interaction between labeling and treatment (likelihood-ratio test; chisquared = 1.96, df = 2, P = 0.37). To produce a model that contained main effects for treatment and no main effects for labeling, we removed the indicator variables for M labeling and P labeling, and replaced the overall indicator with an indicator for M treatment. The resulting likelihood-ratio test (against the model that contained main effects for both labeling and treatment) had a chi-squared statistic of 7.38, on 2 df (P = 0.025). In the absence of statistically significant interaction between labeling and treatment, it was appropriate to assess, separately, the effect of treatment and the effects of labeling. Table S10 shows the estimate, 95% CI, and P-value for each of these four contrasts. The difference between the two treatments was statistically significant (P <.001). Similarly, the difference between maxalt labeling and placebo labeling was significant (P =.013), and so was the difference between uncertain labeling and placebo labeling (P =.010). The analysis did not find the difference between uncertain and maxalt labeling significant (P =.923). 8

11 Analysis of available data. The available data set (i.e., no imputed values) comprised 894 observations on 66 subjects. The available-data estimates for the Constant and Cond2 through Cond7 are fairly close to the imputed-data estimates (table S11), as one would expect because the imputed values were all at 2.5 h. The slightly smaller imputed-data estimate for Time2 may reflect the lack of increase in 2 of the 3 imputed pain scores for NT. Conversely, the imputeddata estimates for the 6 product variables are higher (i.e., closer to 0) than the available-data estimates, most likely because 12 of the 15 imputed pain scores for the treated attacks were equal to the pain score at 30 min. (Part of the difference between these imputed-data and availabledata estimates offsets the difference in the estimates for Time2.) Biases may not be absent, but the small number of missing observations (a total of 30 out of 924, 3.2%) and the informative nature of most of the missingness suggest that they are not an important concern. Analyses of Secondary Endpoint. In analyzing pain-freedom status at 2.5 h, the outcome variable takes the values 1 (pain-free) and 0 (not pain-free). The analyses used mixed-effects logit models (the xtmelogit command in Stata). For this type of GLMM, the analysis is on the log-odds scale, the link function is the logit, g(µ) = log e [µ / (1 µ)], and the family of distributions for the random component is the binomial distributions. For the main analysis the linear model had the following form: g(µ ij ) = β 1 + β 2 Cond2 ij + β 3 Cond3 ij + β 4 Cond4 ij + β 5 Cond5 ij + β 6 Cond6 ij + β 7 Cond7 ij + b i The definitions of the subscripts and the indicator variables are the same as in the model for the pain scores. The coefficients, β 1 through β 7, and the random effect, b i, have no direct connection with their counterparts in the main model for the pain scores. We have simply followed the common practice of using the same family of symbols for the coefficients in all 9

12 models. It is helpful to keep in mind, however, that in any regression-like model the definition of each coefficient includes the list of all other explanatory variables in the model. Using the same procedure as in the analysis of the primary endpoint, we found no need to include any background characteristics as covariates in the model. Table S12 shows parameter estimates, standard errors, and 95% confidence intervals for the parameters in the main model. The estimates reported in the article are based on this model. In particular, table S13 shows the numerical values of the estimates and 95% CIs plotted in Fig. 4. As in the analysis of the primary endpoint, the aggregate effects of the interactions between labeling and treatment and the aggregate main effects of labeling were tested using a modified model. The likelihood-ratio test for the labeling/treatment interaction was not significant (chisquared = 1.51, df = 2, P = 0.47), and the likelihood-ratio test for the main effects of labeling (against the model that contained main effects for both labeling and treatment) was almost significant (chi-squared = 5.84, df = 2, P = 0.054). In the absence of statistically significant interaction between labeling and treatment, it was appropriate to assess, separately, the effect of treatment and the effects of labeling. Table S14 shows the estimate, 95% CI, and P-value for each of these four contrasts. As with the primary endpoint, the difference between the two treatments was statistically significant (P <.001). However, none of the pairwise differences between the types of labeling were significant. 10

13 Table S1. Reasons for excluding 19 of the prescreened subjects Subject A B C D E F G H I J K L M N O P Q R S Comment CM CM CDH, CM, self-administered demerol CM, had a pacemaker due to PFO (counter indication for triptans) CM, TTH CM, TTH CDH, fibromyalgia CDH, TTH, medication-overuse headache, chronic back pain, epilepsy CDH, fibromyalgia CDH CDH, was not interested after learning more details about the study CDH CDH, daily opioids CDH, daily opioids CDH, medication-overuse headache Non-migraine headache CM, counter indication to triptans EM plus fibromyalgia EM plus chronic back and knee pain, hypertension, impaired memory/comprehension CDH = chronic daily headache, CM = chronic migraine, EM = episodic migraine TTH = tension-type headache. 11

14 Table S2. Background characteristics of participants and dropouts Variable Participants (n = 66) Dropouts (n = 10) P value Age (years) 40.6 (12.7) 40.5 (11.5) 0.98 (1.00) a Age at first attack 18.2 (10.4) 15.7 (8.3) 0.40 (0.45) a Years since first attack 22.2 (12.9) 24.8 (10.0) 0.47 (0.50) a Attacks per month 5.4 (4.0) 4.8 (2.5) 0.56 (0.93) a Days per attack 1.2 (0.7) 1.9 (1.7) 0.22 (0.22) a Migraine days per month 5.6 (3.9) 7.9 (7.1) 0.33 (0.47) a Female/male (percentage ratio) 85/15 90/ b Visual aura (%) b Other pains (%) b Family history (%) b Triptan history (%) b Continuous variables are Mean (SD). parentheses are from rank-sum test. b Fisher s exact test. a Two-sample t-test, not assuming equal variances; P values in 12

15 Table S3. Selected quantiles of nondichotomous background characteristics of the 66 participants Variable Minimum 25th Percentile Median 75th Percentile Maximum Age (years) Age at first attack Years since first attack Attacks per month Days per attack Days per month

16 Table S4. Incidence of missing pain scores and data imputation Reason for missingness Number of subjects Missing 30 min Missing 2.5 h Total missing values Incomplete attacks Imputed pain scores/pain freedom Reported neither the 30-min nor the 2.5-h pain score Reported the 30-min pain score but not the 2.5-h pain score Fell asleep before and beyond the 2.5 h time Took rescue medications before the 2.5 h time a / 18 Totals 15* / 18 a The missing 2.5-h pain score was imputed as described in Materials and Methods. *Two of these subjects had missing data for two reasons. 14

17 Table S5. Structure of the eight treatment sequences and assignment of subjects to treatment sequences Treatment sequence a Treatment conditions Number of subjects Attack 1 Attack 2 Attack 3 Attack 4 Attack 5 Attack 6 Recruited Dropped out Analyzed 5 M M M P P M P P U M U P P M P P M M M P U M U P U M U P M M M P P M P P U M U P P M P P M M M P U P U M M P M M P P P M U P U M P P P M M P M M M P M M P P P M U P U M P P P M M P M M U P U M Totals The 6 pill/label combinations are abbreviated as follows: the first letter (in italic) denotes the label (M for Maxalt, P for Placebo, U for the unspecified Maxalt or Placebo ); the second letter (in color) denotes the actual pill (M for maxalt, P for placebo). a Sequence numbers correspond to the order they were entered in the GLMM analyses (cf. table S6). 15

18 Table S6. Distribution of attacks with imputed pain scores and pain freedom at 2.5 hours Treatment condition* (number of attacks with imputed pain scores / pain freedom) NT U M U P M M M P P M P P Imputed pain scores/pain freedom 3 / 3 1 / 1 3 / / 3 2 / 2 6 / 6 Experimental condition (j) j = 1 j = 2 j = 3 j = 4 j = 5 j = 6 j = 7 *NT, No treatment. For abbreviation of the 6 treatment conditions see footnote of table S5. 16

19 Table S7. Estimates of parameters in the main model for the pain scores including the imputed pain scores at 2.5 hours Variable Estimate Standard error 95% Confidence Interval Constant (NT) Cond2 (U M) Cond3 (U P) Cond4 (M M) Cond5 (M P) Cond6 (P M) Cond7 (P P) Time2 (2.5 h) Cond2 Time Cond3 Time Cond4 Time Cond5 Time Cond6 Time Cond7 Time Var(b i ) Residual variance NT, No treatment. For abbreviation of the 6 treatment conditions see footnote of table S5. 17

20 Table S8. Percentage decrease in the estimated average pain score from 30 min to 2.5 hours under the seven experimental conditions (from an analysis that included imputed pain scores at 2.5 hours) Attack Estimate 95% Confidence Interval Untreated P P U P M P P M U M M M For abbreviation of the 6 treatment conditions see footnote of table S5. 18

21 Table S9. Estimates of parameters in the covariate model for the pain scores including the imputed pain scores at 2.5 hours Variable Estimate Standard error 95% Confidence interval Constant (NT) Cond2 (U M) Cond3 (U P) Cond4 (M M) Cond5 (M P) Cond6 (P M) Cond7 (P P) Time2 (2.5 h) Cond2 Time Cond3 Time Cond4 Time Cond5 Time Cond6 Time Cond7 Time Age (years) Sex Migraine years Attacks/month Migraine days/mo Triptan history Family history Var(b i ) Residual variance NT, No treatment. For abbreviation of the 6 treatment conditions see footnote of table S5. 19

22 Table S10. Estimates of difference (2.5 hours minus 30 min) on the primary endpoint for key contrasts involving treatment and labeling Contrast Estimate 95% Confidence Interval P value Pill: Maxalt vs. placebo < Label: M vs. P Label: U vs. M Label: U vs. P Estimates and 95% CIs are in logarithmic units (base e). Labels: M, Maxalt; P, placebo; U, unspecified. 20

23 Table S11. Sensitivity analysis of the main model for the pain scores* Variable Estimate Standard error 95% Confidence interval Constant (NT) / / / / Cond2 (U M) / / / / Cond3 (U P) / / / / Cond4 (M M) / / / / Cond5 (M P) / / / / Cond6 (P M) / / / / Cond7 (P P) / / / / Time2 (2.5 h) / / / / Cond2 Time / / / / Cond3 Time / / / / Cond4 Time / / / / Cond5 Time / / / / Cond6 Time / / / / Cond7 Time / / / / Var(b i ) / / Residual variance / / *Values are based on the data without/with the imputed pain scores at 2.5 h. NT, No treatment. For abbreviation of the 6 treatment conditions see footnote of table S5. 21

24 Table S12. Estimates of parameters in the main model for pain freedom at 2.5 hours Variable Estimate Standard error 95% Confidence interval Constant (NT) Cond2 (U M) Cond3 (U P) Cond4 (M M) Cond5 (M P) Cond6 (P M) Cond7 (P P) Var(b i ) NT, No treatment. For abbreviation of the 6 treatment conditions see footnote of table S5. 22

25 Table S13. Estimated probability of being pain-free 2 hours after treatment under the seven experimental conditions (from an analysis that included imputed pain scores at 2.5 hours) Attack Estimate (%) 95% Confidence Interval Untreated P P U P M P P M U M M M For abbreviation of the 6 treatment conditions see footnote of table S5. 23

26 Table S14. Estimates of difference (2.5 hours minus 30 min) on the secondary endpoint for key contrasts involving treatment and labeling Contrast Estimate 95% Confidence Interval P value Pill: Maxalt vs. placebo < Label: M vs. P Label: U vs. M Label: U vs. P Estimates and 95% CIs are in units of log-odds (base e). Labels: M, Maxalt; P, placebo; U, unspecified. 24