Meta-analysis applications Assignment

STATA Exercise Homework

Reminder: you can find helpful information regarding each STATA meta-analysis command by typing “help command” (where command is metan, metareg, metacum, metabias, etc.) or consulting the handout on STATA notes.

Questions 1 to 3 of this STATA exercise homework are based on 21 randomized controlled trials conducted from 1959 to 1986 investigating the effect of streptokinase on lowering mortality from myocardial infarction. Use risk ratios to quantify the association between streptokinase and myocardial infarction. RR = (number of cases among exposed (a)/ total exposed (N₁))/(number of cases among non-exposed (b)/total non-exposed (N₂)). Var(RR) = [(N₁ – a)/aN₁] + [(N₂ – b)/bN₂]

Below is a summary of the variables in the accompanying homework dataset. The dataset, strepto_21trials_v4.dta, can be access on the course website. 

{`+---------------------------------------------------------+
| trialnam   year   pop1   deaths1   pop0   deaths0 |
|---------------------------------------------------------| 
1.	| 1st Australian   1973    264        26    253        32 | 
2.	|   1st European   1969     83        20     84        15 | 
3.	| 2nd Australian   1977    112        25    118        31 | 
4.	|   2nd European   1971    373        69    357        94 | 
5.	|  2nd Frankfurt   1973    102        13    104        29 |      
|---------------------------------------------------------| 
6.	|   3rd European   1977    156        25    159        50 | 
7.	|       Austrian   1977    352        37    376        65 | 
8.	|          Dewar   1963     21         4     21         7 | 
9.	|       Fletcher   1959     12         1     11         4 | 
10.	|          Frank   1975     55         6     53         6 |      
|---------------------------------------------------------| 
11.	|        GISSI-1   1986   5860       628   5852       758 | 
12.	|    Heikinheimo   1971    219        22    207        17 | 
13.	|           ISAM   1986    859        54    882        63 | 
14.	|        Italian   1971    164        19    157        18 | 
15.	|          Klein   1976     14         4      9         1 |      
|---------------------------------------------------------| 
16.	|       Lasierra   1977     13         1     11         3 | 
17.	|       N German   1977    249        63    234        51 | 
18.	|     NHLBI SMIT   1974     53         7     54         3 | 
19.	|      UK Collab   1976    302        48    293        52 | 
20.	|         Valere   1975     49        11     42         9 | 
21.	|       Witchitz   1977     32         5     26         5 | 
|---------------------------------------------------------| 
`}

Question 1:

1. Using the “metan” command with direct input of the 2x2 table cell counts, conduct a fixed-effect inverse variance pooling of the study specific RRs and copy the resulting forest plot. What is the pooled RR and 95% confidence interval? What does this say about the effect of streptokinase on myocardial infarction?

2. Is there evidence of significant heterogeneity? Report the p value for heterogeneity, as well as the I-squared statistic and 95% CI of the I².

1. Conduct a random-effect meta-analysis. Report the new pooled RR and 95% confidence interval and copy the resulting forest plot. Comparing the results to the fixed-effect analysis, which confidence interval is larger? Why? Are the % weights the same as in the fixed-effects analysis? Why or why not?

1. Using the study-specific logRR and SE(logRR), compute the fixed and random effects summary estimates. How do they compare to the analysis when the numbers of cases and noncases were used?

Question 2

1. Use “metacum” to examine the pooled effect estimate by adding one study at a time over time. Is there evidence of changing magnitude in pooled effect estimate over time?

1. Did any particular study or studies have a strong influence on the pooled estimate?

1. Apply meta-regression analysis “metareg” to examine if there was evidence of effect modification by baseline mortality rate (estimated from the controls) in a trial.

Question 3:

1. Use the “metabias” command to examine whether publication bias existed using Egger’s test. Paste your output as well as both the funnel plot and Egger’s regression plot.

1. Do the results statistically indicate possible publication bias? If so, which tests?

1. Aside from p-values, describe what one visually looks for in a funnel plot and in Egger’s plot to determine whether there is possible publication bias.

Question 4:

The following data are from five fabricated studies of the effect of soy-protein intake on LDL-C reduction. You will find the data file in the “soy_5b.dta” on the website.

+-----------------------------------------------+
| author dose duration soychg soysd soyn conchg consd conn |
|--------------------------------------------|
1. | Aggregat 30g/day 2w -75 35 24 -4 5 24 |
2. | Bundled 20g/day 3w -42 35 12 -29 22 12 |
3. | Combini 25g/day 4w -45 23 43 -4 5 44 |
4. | Deposita 15g/day 1y -34 64 22 4 8 21 |
5. | Embodyl 50g/day 6w -24 23 53 -3 6 44 |
+--------------------------------------+

Below is a summary of the variables in this homework dataset.

1. Calculate the weighted mean difference between the "soy" treatment arms and "control", using the “metan” command. Summarize the effect size in one sentence, and paste you forest plot.
2. Calculate the standardized mean difference between the "soy" treatment arms and "control", using the “metan” command. Summarize the effect size in one sentence, and paste you forest plot.
3. You have written your manuscript, and are passing it around to your co-authors. One author would like to report the WMD, and another would like to report the SMD. However, they have not taken EPI 233, and defer to your judgment. Which would you prefer to include? Please justify your choice in one or two sentences.
4. How would you deal with the heterogeneity in your meta-analysis? Please briefly motivate your choices.